Personal Wireless Communications: Pwc 05 - Proceedings of the 10th Ifip Conference

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Personal Wireless Communications PWC'OS

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Colmar, France

25-27 Augusat 2005

Personal Wireless Communications PWC'OS editor

Pascal Lorenz University of Haute Alsace, France

Imperial College Press

Published by

Imperial College Press 57 Shelton Street Covent Garden London WC2H 9HE Distributed by

World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 U K office: 57 Shelton Street, Covent Garden, London WC2H 9HE

British Library Cataloguing-in-PublicationData A catalogue record for this book is available from the British Library.

PERSONAL WIRELESS COMMUNICATIONS Proceedings of the 10th IFIP International Conference Copyright 0 2005 by Imperial College Press All rights reserved. This book, or parts thereoj may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher.

ISBN 1-86094-582-1

Printed in Singapore by B & JO Enterprise

Preface Welcome to theloth IFIP International Conference on Personal Wireless Communications hosted in Colmar, France. PWC provides an international forum for discussions between researchers, practitioners and students interested in new developments in mobile computing and wireless networks. PWC’2005 is the tenth conference of this series and is sponsored by IFIP WG 6.8. As other PCW events in the past, this professional meeting continues to be highly competitive and very well perceived by the international networking community, attracting excellent contributions and active participation. This year, a total of 192 papers from 31 countries were submitted, from which 57 have been accepted. Each paper has been reviewed by several members of the PWC’2005 Technical Program Committee. We were very pleased to receive a large percentage of top quality contributions. The topics of the accepted papers cover a wide spectrum: wireless sensors, signalization, traffic and QoS in wireless networks, Ad-Hoc, IEEE 802.11, cellular and mobiles networks. We believe the PWC’2005 papers offer a large range of solutions to key problems in wireless networking, and set challenging avenues for industrial research and development. We would like to thank the PWC’2005 Technical Program Committee members and the referees. Without their support, the creation of a very broad conference program would not have been possible. We also thank all the authors that dedicated a particular effort to contribute to the PWC’2005. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. We are also indebted to many individuals and organizations that made this conference possible, specifically, IFIP, IEEE, ARP, Conseil Gtnkral, France Telecom and University of Haute Alsace. In particular, we would like to thank the members of the PWC’2005 Organizing Committee for their help with all logistic aspects of organizing this professional meeting. We expect the 10th International Conference on Personal Wireless Communications to be an outstanding international forum for the exchange of ideas and results between academia and industry, and provide a baseline of further progress in networking area. We hope you will enjoy your stay in Colmar and be able to spend some time to visit various points of interest on this lovely city. Pascal LORENZ Conference Chair V


International Scientific Committee Arup ACHARYA, IBM TJ Watson Research Center, USA Villy Baek IVERSEN, Technical University of Denmark, Denmark Sathish CHANDRAN, RF Consultant, Malaysia Prosper CHEMOUIL, France Telecom R&D, France Pedro CUENCA, Universidad de Castilla La Mancha, Spain Franc0 DAVOLI, DIST - University of Genoa, Italy Silvia GIORDANO, ICA-DSC-EPFL, Switzerland Cambyse Guy OMIDYAR, Oman Veikko HAKA, Telecom Finland, Finland Takeshi HAlTORI, Sophia University, Japan Sonia HEEMSTRA de GROOT, Ericsson EuroLab Netherlands, The Netherlands Ousmane KO&, UniversitC Paul Sabatier - IRIT, France Sanjeev KUMAR, University of Texas, USA Pascal LORENZ, University of Haute Alsace, France Damien MAGONI, University of Strasbourg, France Gerald MAGUIRE, KTWInst. for Teleinformatik,Sweden Zoubir MAMMERI, University of Toulouse, France Olli MARTIKAINEN, Helsinki Univ. of Tech., Finland Ignacious NEMEGEERS, Delft University of Technology, The Netherlands Algirdas PAKSTAS, London Metropolitan University, UK Guy PUJOLLE, University of Paris 6, France Pierre R. CHEVILLAT, IBM Zurich Research Laboratory, Switzerland Pierre ROLLN, France Telecom R&D, France Nikola ROZIC, University of Split, Croatia Debashis SAHA, Indian Institute of Management (IIM) Calcutta, India Tadao SAITO, Toyota Infotechnology Center, Japan Dilip SARKAR, University of Miami, USA Jan SLAVIK, TESTCOM, Czech Republic Otto SPANIOL, Aachen University of Technology, Germany Samir TOHME, ENST, France Adam WOLISZ, Technical University Berlin, Germany Jozef WOZNIAK, Technical University of Gdansk, Poland Jun ZHENG, University of Ottawa, Canada vii


Contents

Wireless Sensors Energy-Efficient Application-Aware Communication for Wireless Sensor Networks R.M. Passos , C.J.N. Coelho, A.A.E Loureiro, R.A.E Mini (Federal University of Minas Gerais, Brazil)

3

SDMA in Connections between Wireless Sensors and Wired Network K Hasu, H. Koivo (Helsinki University of Technology, Finland)

11

A Reliable and Energy-Efficient Routing Protocol for Wireless Sensor Networks K.K. Loh, S.H. Long (Nanyang Technological University, Singapore); Z Pan (Georgia State University, USA)

19

Analysis of Coverage and Connectivity in Wireless Ad Hoc Sensor Networks J. Wang, L. Wang (Central South University, China); R. Xiao (National Nature Science Foundation of China, China)

27

MANET Cross-layer’s Paradigm Features in MANET Benefits and Challenges L. Romdhani, C. Bonnet (EURECOM Institute, France)

37

Two Bandwidth-Violation Problems and Bandwidth-Satisfied Multicast Trees in MANETs C.C. Hu, G.H. Chen (National Taiwan University, Taiwan); E.H. K. Wu (National Central University, Taiwan)

51

LAP-RRP: A Reliable Routing Protocol with Link Availability Prediction in MANET J. Wang, J. He (Central South University, China); X. Lu, (National University of Defense Technology, China)

59

ix

X

An Efficient Load-Balancing Algorithm for Supporting QoS in MANET M. Brahma, K.W Kim, A. Abouaissa, f? Lorenz, (University of Haute Alsace, France); M.M.O. Lee (University of Dongshin, Korea)

67

A Bandwidth-Efficient Cross Layer Probability Routing €or MANEiTs X. Wang, H. Yu, C. Ran, X. Zhang, W Qi, (Information Science and Engineering Institute, China)

77

Ad Hoc (I) Efficient Bandwidth Allocation for Basic Broadcast and Point-to-Point Services in the ADHOC MAC Protocol J.R. Ga'llego,A. Herna'ndez-Solana,M. Canales, A. Valdovinos, (University of Zaragoza, Spain); L. Campelli, M. Cesana, A. Capone, E Borgonovo (Politecnico di Milano, Italy)

87

Connectivity Aware Routing in Ad-hoc Networks J. Leguuy, 7: Friedman, S. Fdida, (University Pierre et Marie Curie, France); K Conan, A. Cotton, (Thales Communications, France)

99

A New Approach for TDMA Scheduling in Ad-hoc Networks D.D. Vergados,D.J. Vergados, (University of the Aegean, Greece): C.Douligeris, (Univeristy of Piraeus, Greece)

107

A Self Organizing Algorithm for Ad-Hoc Networks N. Kettaj A. Abouaissa, F! Lorenz, (University of Haute Alsace, France); H. Guyennet, (University of Franche-Comti, France)

115

125 HDSR, Hierarchiacal Dynamic Source Routing for Heterogeneous Wireless Mobile Ad Hoc Networks M. Naserian, K.E. Tepe, M. Tarique, (University of Windsol; Canada)

Ad Hoc (11) Analyzing the Effect of Cooperation Approaches M.Frank, M. Holschbach, P: Martini, M. Plaggemeiel; (University of Bonn, Germany)

135

xi

Mobility Management in Multihops Wireless Access Networks E Theoleyre, E Valois (Inria Ares, France)

146

Location Update Protection for Geographic Ad Hoc Routing Z. Zhou, K.C. Yow (Nanyang Technological University, Singapore)

154

PEDCF: Predictive Enhanced Service Differentiation for IEEE 802.11 Wireless Ad-Hoc Networks based on AutoRegressive-MovingAverage Processes N. Tabbane, S. Tabbane (SUP’COM, Tunisia); A. Mehaoua, (University of Versailles, France)

162

IEEE 802.11 A Carrier-Sense based Transmission Power Control Protocol for 802.11 Networks J. Rao, S. Biswas (Michigan State University, USA)

173

Chaos Shift Keying and IEEE 802.11a G. Plitsis (Aachen University of Technology, Germany)

181

Effect of Time-Correlated Errors on Power-Saving Mechanisms for IEEE 802.11 Infrastructure Networks G.A. Safdar, WG. Scanlon (The Queens University of Bevast, U K )

189

197 COMPASS: Decentralized Management and Access Control for WLANs A. Hecker (Wavestorm, France); E.O. Blass (University of Karlsruhe, Germany); H. Labiod (ENST, France)

QoS Provisioning Mechanisms for IEEE 802.11 W A N : A Performance 205 Evaluation J. Villaldn, R Cuenca, L. Orozco-Barbosa (University De Castilla La Mancha, Spain)

QOS Impact of Varying the Minimum Value of Contention Window (CWmin) of the IEEE 802.11 MAC Protocol on the QoS Parameters M. Saraireh, R. Saatchi, R. Strachan, S. Al-Khayatt (Shefleld Hallam University, UK)

219

xii

Statistical QoS Guarantees in Bluetooth under Co-channel Interference J.L. Sevillano, D. Cascado, E Diaz del Rio, S. Vicente, G. Jimknez, A. Civit-Balcells (University de Sevilla, Spain)

227

Performances Evaluation of the Asynchronous Bluetooth Links in a Real Time Environment 7: Khoutaij E Peyrard (Research Icare Team, France)

235

System Simulations of DS-TRD and TH-PPM for Ultra-Wide Band (UWB) Wireless Communications S. Vasana, K. Phillips (University of North Florida, USA)

244

Global Solution for the Support of QoS by IEEE 802.11 Wireless Local Area Networks A. Bedoui (ENII: Tunisia); K. Barkaoui (CNAM, France); K. Djouani (University of Paris 12, France)

252

Traffic Cross-Layer Design for Dynamic Resource Allocation in Wireless Networks J.Y Kim, A. Saidi, R.J. h n d r y (The MITRE Corporation, USA)

263

Coverage Area Analysis of Soft Handoff on Cellular CDMA Systems T.L. Sheu, J.H. Hou (National Sun Yat-Sen University, Taiwan)

279

LMS vs. RLS for Adaptive MMSE Multiuser Detection over Time Varying Communications Channels Z.B. Krusevac, PB. Rapajic, R.A. Kennedy, (National ICT Australia and University of New South Wales, Australia)

287

Performance Analysis of a Preemptive Handoff Scheme for Multi Traffic Wireless Mobile Networks D.D. Vergados,A. Sgora (University of the Aegean, Greece)

295

303 Performance Analysis of DS-CDMA Systems in Multiple-Cell with Correlated Fading Channels J.I.-Z. Chen, N. Chi-Kuang, EC. Chung (Da Yeh University, Taiwan)

xiii

Cellular Networks Multimedia Transmission over Third Generation Cellular Networks A. Alexiou, C. Bouras, K Igglesis (Research Academic Computer Technology Institute, Greece)

317

Distributed Content Sharing in Cellular Networks B. Bakos, L. Farkas, J.K. Nurminen (Nokia Research Centel; Hungary); K. Marossy (Nokia Technology Pla~orms,Hungary)

325

On UMTS HSDPA Performance I! Matusz, J. Wozniak (Gdansk University of Technology, Poland)

338

Supporting Flexible Network Operator Policies in EGPRS Trough Admission Control D.Todinca, I. Sora (University Politehnica Timisoara, Romania); f? Perry, J. Murphy (University College Dublin, Ireland)

346

Performance and Quality of Service Management in GPRS Network 0. El Ghandoul; M.Fikry (Helwan University, Egypt); S. El-Ramly (Ain Shams University, Egypt)

354

Mobile Networks (I) Enabling Mobile IPv6 in Operational Environments X. Fu (University of Gottingen, Germany); H. Tschofenig, S. Thiruvengadam (Siemens AG, Germany); W Yao, (Brunel University Wenbing, UK)

365

Performance Evaluation of Tunnel-based Fast Handovers for Mobile IPv6 in Wireless LANs H. Lu, J. Li, I? Hong (University of Science and Technology of China, China)

373

Mobile IPv6-type Route Optimization Scheme for Network Mobility (NEMO) Support B. f? Kafle (The Graduate Universityfor Advanced Studies, Japan); E. Kamioka, S. Yamada (National Institute of Informatics, Japan)

38 1

xiv

Comparative Analysis of Handoff Delay of MIFA and MIP A. Diab, A. Mitschele-Thiel, R. Boeringel; (Ilmenau University of Technology, Germany)

389

Integration the Protocols HMIPv6 and Diffserv over M-MPLS in Order to Provide QoS in IP Network Mobility J. H. Ortiz (University Polytechnic of Madrid, Spain)

397

Mobile Networks (11) An Agent-Based Framework for Mobile Multimedia Service Quality Monitoring and Diagnosis M.Li (Nokia Research Centel; USA)

405

Neural Network and Self-Learning Based Autonomic Radio Resource Management in Hybrid Wireless Networks C. Shen, D. Pesch, J. Irvine (Cork Institute of Technology, Ireland)

413

421 WebBee: An Architecture for Web Accessibility for Mobile Devices K. Upatkoon, W Wang, S. Jamin (The University of Michigan, USA) Empowering Wireless UPnP Devices with Webprofiles J.I. Vazquez, D.L. De Ipina (Deusto University, Spain)

429

UICC Communication in Mobile Devices Using Internet Protocols B.H. Nguyen, H.K. Lu (Axalto, Smart Cards Research, USA)

438

Mobile Networks (111) Modular Proxies for Service Adaptation and Session Continuation over Heterogeneous Networks I: Seipold, I: Tantidham (RWTH Aachen University of Technology, Germany)

449

A Channel Preemption Model for Multimedia Traffic in Mobile Wireless Networks TL. Sheu, YJ. Wu (National Sun Yat-Sen University, Taiwan)

457

xv Nonuniform-Detection-BasedFast Mobile IP Handoff for Wireless LANs B. Shen, H.Zhang, K Liu, E Zhao (Beijing Jiaotong University, China)

465

Analysis of ACD: Autonomous Collaborative Discovery of User and Network Information T Zhang, S. Madhani (Telcordia Technologies, USA); S. Mohanty (Georgia Institute of Technology, USA)

473

EasyMN: An Effective IP Mobility Solution for High-Mobility Network L. Wang, M. u! Xu, K. X u (Tsinghua University, China)

479

Signalization Proposal of PAPR Reduction Method for OFDM Signal by Using Dummy Sub-can-iers l? Boonsrimuang, K. Mori, H. Kobayashi (Mie University, Japan)

489

Adaptive Scheduling for Heterogeneous Traffic Flows in Cellular Wireless 497 OFDM-FDMA Systems S. Valentin, H. Karl (University of Paderborn, Germany); J. Gross, A. Wolisz (TU Berlin, Germany) The Power Spectral Density of the H-Ternary Line Code: A Simulation Model and Comparison A. Glass (Technical Studies Institute, UAE); N. Abdulaziz (University of Wollongong, UAE); E. Bastaki (Dubai Silicon Oasis, UAE)

507

An Optimized CPFSK-Receiver based on Pattern Classification in Combination with the Viterbi Algorithm D. Briickmann (University of Wuppertal, Germany)

518


Wireless Sensors


ENERGY-EFFICIENT APPLICATION-AWARE COMMUNICATION FOR WIRELESS SENSOR NETWORKS

RODRIGO M. PASSOS , CLAUDIONOR J. N. COELHO JR , ANTONIO A. F. LOUREIRO AND RAQUEL A. F. MINI Department of Computer Science Federal University of Minas Gerais, Brazil { passos, coelho,loureiro,raquel} Qdcc.ufmg. br In this work, we propose a new dynamic power management (DPM) technique for wireless sensor networks (WSNs) that selectively shutdowns the sensor node radio and other hardware components based on the application-level information. The DPM technique is modeled using the hybrid automata framework, which is leveraged t o represent different duty cycles, according to the application requirements. Both the application-level information and the application requirements determine the sensor node duty cycle and the tradeoff between energy conservation and data delivery rate. We provide a comparison of our DPM technique using the main types of routing protocols to show the performance of our DPM technique in a sensor network modeled for a fire detection application. Simulation results reveal that our DPM technique can change the duty cycle of a node according t o the data delivery quality required by the application and the need for communication, saving energy and, thus, extending the WSN lifetime.

1. Introduction

Sensor networks are commonly designed to be used in hostile environments and the re-charge process of typically battery operated sensors can be almost impossible, which makes the energy consumption in sensor nodes crucial for the network lifetime. The sensor nodes energy limitation requires energy-efficiency in all aspects. At design time, much work has been performed to make energy efficient circuits, architecture, communication protocols, routing protocols, algorithms and sensing . At run time, dynamic power management (DPM) techniques have been leveraged to selectively shutdown hardware components to avoid the waste of energy". In multihop WSNs, communication is the major consumer of energy5 and must be carefully designed and performed to reduce the energy consumption. According to7, the main factors of energy ineffiency can be due to useful energy consumption (transmitting, receiving and routing data) 3

4

and wasteful energy consumption (idle listening, collisions, overhearing, and control packets). The transmission process and the idle listening problem represent the main factors of energy consumption and they must be carefully conducted by the power management (PM) policy, achieving the application requirements in a satisfactory way. We believe that the best trade-off among energy conservation, packet latency and data delivery rate can be obtained when application-level information is used in the communication process. In4, we have shown the benefits of a system-level DPM technique for single-hop communication. In this work (Section 2), we extend the DPM technique t o multi-hop communication, by leveraging the application-level information to selectively turn off the sensor radio, avoiding unnecessary transmissions due to neighbor data similarity, avoiding the idle listening problem and reducing the energy consumption, without modifying the routing and MAC protocols. As previously introduced in4, the hybrid automata framework is used to model the DPM and the application behavior in a formal way. In order to show the performance of our DPM technique (Section 3), we use the basic routing algorithms for WSNs to show the benefits and impacts of a system-level DPM technique. We also use a fire detection application to show the performance influence of the application-level information.

2. A New Application-Aware DPM Approach

In4, we have previously defined the DPM hybrid automata model in formal way. In this work, we improve the application-driven DPM technique by leveraging the application-level information t o analyze the needs for communication and t o avoid wasteful energy sources (idle listening). We extend the power state machine, through the hybrid automata framework, representing in a single model control data, neighborhood and environment information, and the sensor node duty cycle. The DPM hybrid automata represents different control modes. Each control mode represents a duty cycle that performs different rates of sampling, transmitting and receiving operations, according to the required QoS delivery. According to the required quality of service, each location defines the necessary hardware configuration, selectively turning off unnecessary components. The main goal of the DPM hybrid automata is to change duty cycles (locations) according to the QoS requirements of the application. The expected behavior of the variables are used to keep the control mode in a lower power consumption location. Unexpected behaviors may

5

require a higher data delivery rate, forcing the DPM hybrid automata to go to a location that corresponds to the required QoS. The application-level requirements trade off energy conservation and data delivery rate, performing the transitions to the most appropriated location (duty cycle), according to the environment and the neighborhood behavior. Figure 1 represents the sensor node duty cycle leveraged by the DPM technique. The duty cycles represent the sensor node operation behavior for each location of the DPM hybrid automata. They define the operation rates of sampling (I&), transmitting (Rt) and receiving (I?,.), and the sleeping time (&). The operation rates are related to the maximum execution time of the duty cycle and they axe defined according to the required QoS.

Figure 1. Graphical representation of the DPM duty cycle.

According to each operation rate and to the duty cycle execution time, the main sensor node tasks are performed to guarantee the sensor node basic operation. After a sleeping timeout, if the duty cycle execution time is not over, the sensor node may decide to listen for packets by turning on the radio in receive mode, or it may perform a sensing operation. Otherwise, if the duty cycle execution time is over, the PM policy makes a decision of changing the duty cycle, according to the flow and invariant conditions of the DPM hybrid automata. After a sensing operation, the sensed data may be transmitted, according to the transmission rate. If the sensed data is relevant (it reaches an invariant condition), it may be necessary to change to a new duty cycle that better represents the data delivery quality required for the sensed information. Otherwise, not relevant sensed data may contribute

6

to conserve energy by the leverage of a sleep mode, according to the sleeping rate. In the same duty cycle a change decision is evaluated for the routing process. According to the data received from neighbors of a node, the P M policy verifies if the current sensor node duty cycle is able to attend the routing process in a satisfactory way. Otherwise, a more appropriated duty cycle may be required. The energy-efficiency is obtained by verifying at each sensor node whether it needs to communicate. If a sensor node is transmitting relevant data, it may need their neighbors (or at least the routing path) to route its data to the sink. This situation may trigger a transition to a location (control mode) that maps a receive-dominant duty cycle with a higher receiving rate and a higher execution time. On the other hand, a transmit-dominant location is useful to report unexpected data behavior detected by the sensing operation of the sensor node. The hybrid automata can also map sleep-dominant and sensing-dominant locations, or any kind of required QoS, in terms of data delivery rate. In fact, the total execution time and operation rates performed in each duty cycle model the power consumption of the hybrid automata locations. Our DPM technique tries to exploit the sleep-dominant locations, according to the application control variables behavior. In the DPM technique context, the need for communication decision and the communication rate required decision are performed by the leverage of three types of application-level information: (i) environment sensed data, (ii) neighborhood received data and (iii) neighbors current duty cycle. The environment sensed data is useful to monitor the environment behavior. The environment variables are represented as differential equations (flow conditions) in the hybrid automata. The flow conditions are used to determine the expected behavior of the environment. The sensed data is used to determine if the environment is behaving as expected. An expected behavior may lead a transition to a sleep-dominant location or a transition to a location that implements a low transmitting rate. Otherwise, an unexpected behavior may lead a transition to a transmit-dominant location, according to the application requirements of data delivery quality. The neighborhood received data is useful to monitor the neighbors environment behavior. An unexpected environment behavior may lead a transition to a receive-dominant location, improving the data delivery rate to the sink. Otherwise, expected data received from the neighbors can be helpful to conserve energy, since there is no need to route a similar data. Data similarity may lead a transition to a sleep-dominant location, specially in high density sensor networks, since there is no need to report the same data

7

to the sink from sensor nodes with similar data. The neighbors current duty cycle is important t o adjust the sensor node duty cycle. Every time a transmission occurs or the node duty cycle is changed, a control packet is broadcasted to all neighbors, informing the current node duty cycle and the duty cycle start-up time. Although the transmissions of control packets are increased, the neighbors duty cycle knowledge may be useful to avoid wasteful communication sources, since the probability of a successful communication operation is also increased. Transmissions should be performed when there is a higher probability of a receiver in listening mode. A transmit-dominant duty cycle may need the receiver in a receive-dominant duty cycle. However, there is no need for a receive-dominant duty cycle if the neighbors remain in a low transmitting rate. A node aware of its neighbors duty cycles is able to exploit sleep modes, when the traffic is low, and wake up in a more appropriated time.

3. Performance Evaluation

In order to evaluate the energy savings and the communication impacts of the new DPM technique , we simulate it and compare it to the ideal DPM model (represents the best and not realistic DPM model) and a communication model without a DPM scheme. Since the application-driven DPM works on a higher layer than the communication process, we evaluate the communication impacts of the DPM using three kinds of routing approaches3:(i) the classical flooding algorithm used by a sensor node to broadcast data to all its neighbors, (ii) the EF-Dee (Earliest-First Tree) algorithm that builds and keeps a data dissemination tree for the entire network and (iii) the SID (Source-Initiated Dissemination) algorithm which the reactive data dissemination process starts from the data source. The data delivery quality is determined by the environment fire probability, to simulate the behavior of a fire detection application modeled in4. In terms of energy consumption (Figure 2(a)), the DPM technique indicates a better performance over the EF-Tree (no DPM), independently of the routing approach. The expected environment behaviors, represented by the lower fire probabilities, require lower data delivery rates. In this situation, the DPM hybrid automata is able to take a transition to a sleepdominant location (duty cycle), conserving energy. Otherwise, unexpected environment behaviors, represented by higher fire probabilities, force the leverage of transmit-dominant duty cycles by the PM policy, increasing the data delivery rate and spending more energy. The need for communication

8

decision is performed by the analysis of the environment sensed information, resulting on the use of the most appropriated duty cycle for each fire probability. Since the EF-Tree (no DPM) is not aware of the application-level information, the energy consumption is not related to the fire probability.

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In terms of data delivery quality (QoS), Figure 2(b) shows the expected behavior of the DPM technique that selectively changes the data delivery rate, according to the fire probability and to the required application data delivery quality. The EF-Tree (no DPM) model represents the highest data delivery rate and the ideal model represents the necessary data delivery rate, according to the fire probability. It is clear to see that much energy is wasted when no necessary data delivery rates are performed. Although the DPM technique results on a lower data delivery rate, even for the maximum fire probability, much energy can be saved by trading off data delivery quality and power conservation, according to the environment behavior. Since the fire probability may remain low for hours or days, low data delivery rates are required, saving energy and improving the sensor lifetime. The analysis of the sensed neighbors information is important to identify data similarity among sensor nodes, avoiding redundant data delivery to the sink and conserving energy. Figure 2(a) shows that the DPM technique conserves much energy due to data similarity, specially for lower fire probabilities, when the environment temperature tends to be more similar. The leverage of the current neighbors duty cycle is also important to avoid wasteful communication sources. A sensor node that receives its neighbors current duty cycle, is able to evaluate the most appropriated moment to perform transmissions, when there is a higher probability of

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listening neighbors, and it is able to evaluate the most appropriated moment to search for packets, avoiding long periods of idle listening. We also evaluate the impact of turning off the radio over the data dissemination process, since the DPM technique works independent of the communication protocols. Figure 2 shows the DPM performance when environment and neighborhood data are used on the duty cycle change decision. The SID (DPM) seems to be more adaptive to the network topology changes resulted by the turning off the radio process, although the EF-Tree (DPM) is worse, since the DPM technique may cause network disconnections by turning off an important node in the routing path. The SID (DPM) and the Flooding (DPM) save more energy, since more information about the network is known, due to the broadcast nature of these approaches. A better knowledge about the neighborhood can be used to keep the sensor node in sleep-dominant duty cycles for longer periods . A not pre-determined routing structure is useful to save energy, without losing much data delivery quality. For the EF-Tree (DPM) approach, although the energy consumption is reduced (see Figure 2(a)), the idle listening time remains high, due to disconnections from turning off the radio process. In fact, although much energy can be saved by a DPM technique, the data delivery quality can be influenced negatively, according to the data dissemination approach. It seems to be a very difficult task to model a DPM technique unaware of the communication routing process, although on-demand routing approaches, like SID, seems to be an alternative. Figure 3 shows a comparison in terms of idle listening time, for different levels of application-level information leverage by the DPM technique. Figure 3(a) shows that, although the DPM technique conserves energy by

10

choosing duty cycles according to fire probability, it results in the increase of the idle listening time, due to the respective increase of the average sleeping time that requires more idle listening nodes searching for packets to guarantee the communication process. On the other hand, Figure 3(b) shows that the neighborhood current duty cycle information is crucial to avoid wasteful communication sources, like idle listening, although it does not make much difference for the DPM technique using the EF-Tree routing algorithm, due to the fixed tree routing structure, which requires the parent nodes to listen for packets frequently. 4. Conclusion and Future Work

In this work, we propose a new DPM technique that selectively turns off the sensor node radio to avoid useful and wasteful communication factors. We show that the leverage of application-level information in the power management policy seems to be a good way to trade off data delivery quality and energy conservation. Although the performance evaluation shows that much energy can be saved exploiting sleep-dominant duty cycles, our DPM technique does not perform well for all kinds of data dissemination algorithms, since the process of turning off radio does not consider the routing process. In fact, we show that on-demand routing approaches for DPM techniques work better than other routing approaches, due to their adaptability to network changes. Therefore, as future work, we plan to extend the model to incorporate important parts of the routing process to improve the efficiency of the DPM.

References 1. E . Y . CHUNG, L. BENINI, AND G. D . MICHELI, Dynamic power management using adaptive learning tree, ICCAD, 1999. 2. A. Z. ET AL, Eficient power management in real-time embedded systems, ETFA'03, (2003). 3. C. F. ET AL, ,Protocolo adaptatiuo hz3rido para disseminaGCo de dados e m redes de sensores s e m fio auto-organita'veis, SBRC, May 2004. 4. R. M. P . ET AL, Dynamic power management in wireless sensor networks: An application-driven approach, WONS, January 2005. 5. S. C. ET AL, Lifetime analysis of a sensor network with hybrid automata modelling, WSNA, 2002. 6. A. SINHAAND A. CHANDRAKASAN, Dynamic power management in wireless sensor networks, IEEE Design & Test of Computers, 18 (2001), pp. 62-74. 7. W. YE, J. HEIDEMANN, AND G. D . ESTRIN, An energy-efficient m a c protocol for wireless sensor networks, New York, NY, June 2002, IEEE INFOCOM.

SDMA IN CONNECTIONS BETWEEN WIRELESS SENSORS AND WIRED NETWORK VESA HASU, HEIKKI KOIVO Control Engineering Laboratory, Helsinki University of Technology P.O.Box 5500, 02015 HUT, Finland Wireless sensor networks are not just stand-alone applications. Wireless sensors need to communicate to wired networks, where monitoring applications are usually connected to. This paper examines performance of SDMA in connections between wireless sensors and a wired network. SDMA is considered in the form of adaptive beamforming in the wired network base station. Interest is laid especially on connections with and without feedback. Performance is examined through simulations in a line-of-sight and a more realistic multi-path environments.

1. Introduction

The purpose of this study is to examine to the usefulness of the space division multiple access (SDMA) in the connection between wireless sensors and a fixed network. Saving the transmission power and reducing the complexity of wireless sensors is a necessity. Lighter soft- and hardware in sensor nodes leads to easier multiplication of sensor network size and thus opens new application fields. The communication in wireless sensor networks does not always need to be ad-hoc. For example in monitoring applications, wireless sensor nodes need to transmit to the wired network, in where monitoring takes place. Additionally in very large ad-hoc networks, it might be more useful to utilize a wired network connection than a multi-hop route between two distant nodes. Hence examination of efficient strategies between wireless and wired worlds is needed. SDMA is considered in the form of the receiving beamforming (BF) ([3], [4]) in the wired end of the connection, Compared to the other multiple access (MA) techniques, SDMA does not require accurate scheduling as time division multiple access (TDMA) or as wide frequency band as frequency division multiple access (FDMA). In addition, it is applicable simultaneously with the other MA techniques for better communicational capacity.

2. Link Between Wireless Sensors And Wired Network What additional value SDMA and BF antenna arrays can bring to the 11

12

communication between wireless sensors and wired network? In addition to the larger number of supported connections, they give a prospect to use connections without feedback. SDMA is considered for the cellular systems in [5]. Wireless sensor networks, which communicate ad-hoc, are obviously able to communicate with feedback to wired stations. In those cases, communication capacity between nodes and bases can be restricted, and hence the use of SDMA is worthwhile. If the wireless nodes are distant to the base or SINR is small, it is advisable to collect the information to a single node from a larger cluster of nodes and then send to the wired base station [6]. The upsides of connections without feedback are lighter hardware and software, leading to smaller battery usage. The lack of feedback enables wireless sensor node constructions without receivers. The advantage of the receiverless node is the smaller requirement for hardware and complex signal processing, and the smaller power consumption. After all, the power consumption of the analoddigital-conversion of receivers is high (for examples, see [l] and [2]). The downside of the feedbackless connections is inability to use several multiple access and radio resource management techniques, e.g. TDMA, FDMA, power control (PC)and collision avoidance algorithms, and therefore the communication capacity is reduced. Also, SDMA with BF requires a control signal channel for efficient interference rejection in bursty traffic. 3. SDMA - Beamforming

The receiving BF is an antenna array technique, in which the incoming radio waves are spatially filtered based on channel measurements. Antenna array is a set of co-operating antennas. The spatial filtering corresponds to amplifying or attenuating waves based on their directions of arrival to the antenna array. In practice, BF is done by weighting signals of antennas by a suitable combination of complex-valued weights. An example of a BF gain lobe is drawn in Fig. 1. One characteristic of BF systems are so called null gain hections, in which directions the incoming radio waves are faded out completely. If number of antenna elements in the array is nu, each antenna array weighting has nu - 1 null gain directions [3]. This means that nu simultaneous connections can be supported through BF, if transmitters are in different directions in respect to the receiver and BF is done appropriately. The null gain steering is the essence in SDMA. A successful steering cancels interference and MA is provided for. Several BF algorithms are presented in the literature for antenna weights determination [3], [4]. Some of them rely on channel measurements, e.g. signal covariance matrices or steering vectors, and some of them are based on training sequences, i.e. known bits attached to transmissions to study channel conditions.

13

The main algorithm used in this work is maximum signal-to-inte$erenceund-noise (SINR) beamforming algorithm [4], which relies on the channel measurements by signal covariance matrices. The key advantage of maximumSINR beamforming (MSBF) is, by definition, ability to maximize SINR-levels. While the interference reduction is vital in wireless communications, it is especially important in feedbackless systems. For SDMA systems, the reduction correspond to an accurate steering of null gains towards interferers. In practice, BF techniques relying on the training sequences are more practical to implement than the SINR-beamformer [3], especially in the bursty traffic. In addition, a control signal is also needed for perfect null gain orientation. null gain, directed towards interferer I

beamforming lobe, Le. the gain level in dfiertnt spatial directions

0

= transmitter

0

Fig 1: An example of beamforming gain.

4. Simulation Study Of Line-Of-Sight Connections

While connections without feedback and using SDMA are considered, one essential question is: how often unacceptable signal conditions are expected to occur? The following simulation results examined the effect of transmission power level and adaptive antenna specifications in a BF system.

4.1 Simulation Specifications Simulations included transmitting wireless sensors and a BF base station. The sensor nodes were considered to be in randomly spread fixed locations in a space with radius r. The base station antenna array had two configurations: four elements in tetrahedron or six elements in triangle with half-wavelength spacing. The base station was set outside the node cluster to distance Y . Geometrical specifications simulate, for example, industrial processes, in which system measurements are sent to a monitoring system. It would be more advantageous for SDMA system )o have more spatial distribution, i.e. the base would be in the middle of the sensor nodes. On the hand, the near/far effect could become significant in those cases. The system used MSBF. This meant that there must be a control channel fiom wireless nodes to the base station in addition to the communication channel. Signal path losses were modeled with the exponent two. The relative noise

14

power in the system was -80 dB throughout simulations. The SINR target level for connections was 7 dB,which corresponds to the bit-error-rate (BER) of 10" using coherent binary phase-shift-keying-modulation [9]. Since the usefulness of SDMA in feedbackless communication with random transmission times and combined to TDMA was examined, SINR levels of all combinations with 1-8 simultaneously transmitting nodes were simulated, and probabilities of supported connections were determined. As a definition, a connection was supported, if SINR was larger than the SINR target. These results are good for comparing support probabilities in feedbackless connections and in SDMA/TDMA with multiple simultaneous transmitters. 4.2 Results

Fig. 2a shows support probabilities in systems with 15 randomly spread nodes and triangular antenna array with six elements. Fig. 2b presents support probabilities of a system with a tetrahedrical four element antenna array. In order to guarantee spatial diversity, results were averaged over 100 simulations. Figs. 2 show that the BF theory holds quite well in cases of high transmitting power: n, simultaneous connections are supported, where n, is the number of antenna array elements. On the other hand, n, connections are not always supported. If transmitting nodes are in the same direction, the spatial resolution of the antenna array is not accurate enough and signal can not be separated from the interfering signal. In Figs. 2, the near/far effect of wireless communications can be seen as lower support percentages in cases with lower transmission powers. If Fig. 2a and Fig 2b are compared, the transmission power level is much more significant factor in the case with fewer antenna elements. LOO 90

80 70 60

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Number of simuhanwus transmitters

a)

b)

8

Fig. 2: Support probabilities with different transmission power levels. System includes 15 randomly spread nodes and triangular antenna m a y with a) six elements, b) four elements.

15

Support probabilities varied a lot over 100 simulations. As an example, confidence limits for probabilities of the four antenna element array case are given in Table 1. It shows that in addition to average performance reduction, low transmission power adds dependency on the spatial distribution. These results inhcate that SDMA enables the feedbackless communication between wireless sensor nodes and wired base stations, if the control channel can be utilized well enough, e.g. BF can direct nulls. Performance limitations are related to the number of elements in the array nu,as the BF theory suggests. If nodes transmit with random intervals and the average number of simultaneous connections is lower than nu,SDMA approach guarantees a good throughput, Table 1: 95% confidence intervals of the percentages of the supported connections in cases with different transmission powers and simultaneous transmitters. -20 dB -30 dB -40dB -50 dB [99.8%, lOO?’”,][99.7%, loo%] [98.9%, loo%] [94.3%, loo%] [77.1%, 92.0%] [99.6%, lOO?’o] [99.1%, 100%] [95.8%, lOO%] [81.9%, 99.9%] [44.8%, 70.1%] [98.9%, lOO%] [95.90/,, 100%] [83.4%, 99.5%] [55.8%, 83.3%] [18.0%, 35.1%] [51.0%, 56.0%] [50.8%, 55.6%] [47.4%, 54.0%] [29.5%,46.3%] [4.0%, 16.4%] [18.9%,31.2%] [18.8%,31.0%] [17.9%,30.0%] [10.6%,24.8%] [0%,8.0%]

4.3 Extensions To SDMA/TDMA Systems With Power Control SDMA can be applied in wireless sensor networks with and without feedback. In the system point of view, the basic difference between the two versions is that feedback enables TDMA and PC. If there are several transmitters and BF is used with PC and some other MA technique, BF is more like a radio resource control technique. This is since in those cases BF is more for the interference rejection than MA. The closed-loop PC performance in S D W T D M A systems is examined in [7]. Even though results in [7] are on cellular systems, results on the blocking rate are applicable in the node-base station case: the blocking rate of the calls is reduced by a decade, while using the closed-loop PC. Simulations with varying distance between base and nodes with PC were done. The base station included the tetrahedrical antenna array, and the distance from the nodes to the base station was varied. Decentralized PC was used with five iterations. Admission control was not applied. While more transmitters are on, PC problem becomes easily unfeasible without the admission control, and none of the connections can be supported in the same timeslot. Successful admission control would guarantee at least nu supported connections all the time. Simulations showed that almost all connections could be supported in all cases with at most nu,i.e. four, nodes were transmitting. This was expected for S D W T D M A : a successful combination of PC enables supporting nu transmissions in each TDMA time slot. In addition, transmission power levels

16

were reduced radically, see Table 2. Further, PC adds robustness against the radio channel variations, which was not examined here. Table 2: Average transmission powers in dBs of different distances between the base and nodes, and different numbers of simultaneous transmitters. Transmitters 2 3 4

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2.1

5. A Case Simulation Study Of Wireless Nodes In An Industrial Hall Additional simulations in an industrial hall environment were made. These give insight about the efficiency of SDMA, while lines-of-sight (LOS) are not guaranteed, and reflected non-line-of-sight (NLOS) signals are present.

5.1 Simulation Specifications The simulations were similar to Section 4 with the following exceptions. The communication is talung place in a hall with four reflected components as shown in Fig. 3. The rest of reflections were assumed to be negligible due to shadowing by machinery etc. Reflections were set to cause -7 dB attenuation and a random shadowing caused -10 dI3 attenuation. The shadowing was added to LOS and NLOS paths with probability of 0.1 and 0.4, respectively. Simulations included two base station configurations (four or six element arrays) and three different PC-BF-versions: MSBF with constant relative transmission power of -10 dB,MSBF with PC, and maximum gain beamforming (MGBF) with PC. MGBF weight vector is the principal eigenvector of the signal covariance matrix as.MGBF was applied to obtain information about the SDMA efficiency, if gain nulls are not directed towards interferers. In order to guarantee as large support rate as possible in cases with PC, a simple admission control was also implemented. The admission control denied transmissions from nodes experiencing too poor channel and interference conditions. If the admission control was not implemented, transmission powers were high and support percentages were still small.

Fig. 3: A sketch of the multipath reflections in the industrial hall environment.

17

5.2 Results

Support percentages of different four and six element antenna arrays and PC-BF -configurations are shown in Fig. 4. Average transmission powers of these cases are collected in Table 3. Figure 5 shows that MGBF is not clearly good enough for MA technique. MGBF does not direct its nulls as does MSBF. Additionally, as expected, PC and more antenna elements clearly increase the percentages of the supported connections. If support probabilities are compared to the ones of LOS connections in Fig. 2, the decreased performance due to reflected signals and shadowing is evident. Multi-path components and shadowing lead to more scattered radio environment, and BF becomes much harder task. The cure for severe multipath conditions is to increase the number of antenna elements n,. Average transmission powers in Table 3 indicate that PC combined with admission control can save transmission power. Although, Table 3 reveals that occasionally the situation is vice versa. Increases in transmission powers of some cases in Table 3 are consequence of the admission control, and they should be interpreted in respect to support percentages in Fig. 4. Even though the system with MGBF and PC uses more power than the system without the PC in some cases, it has clear benefits in percentages of supported connections. 100

-

90

80

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60

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50 40 30 20 10

1

2

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1

Fig. 4: Support probabilities over 100 cases with base station configurations and power control. System included 15 randomly spread nodes. Table 3: Transmission powers in dBs of configurations, and different numbers of simultaneous transmitters.

I

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4 elem.,

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18

6. Conclusions

All in all, SDMA is a promising technique for node access to the wired network. The greatest advantage can be received by a S D W T D M A combination. The possibility of supporting more connections to the wired network with feedback channel is obvious based on the literature [7], and it is also confirmed by the simulations in both line-of-sight and multipath environments. The feedbackless communication between wireless and wired networks seems to be possible using SDMA. This would make possible to make smaller, cheaper and simpler hardware for wireless sensor nodes. However, even though SDMA systems have a lot of promise in lightening the configuration of sensor nodes, it must be taken into account that the effective use of BF and null steering is necessary. Moreover, the effect of many multipath signals to the performance must be considered properly in the system design. A well-planned application of control channel, non-moving nodes and fairly stable radio channel are required for transmissions without feedback. References 1. J. Sevenhans, Z.-Y. Chang, “ A D and DIA conversion for telecommunication”, IEEE Circuits Devic., 14(1): 32 - 42, 1998. 2. A. Anttonen, T. Rautio, “Performance and complexity analysis for adaptive sample rate converten in GSM/UMTS/HIF’ERLAN2 mobile transceiver”,IEEE Int. Symp. on Circuits and Systems, ISCAS, vo1.4, pp. IV-489 - IV-492,2002. 3. J.C. Liberti, T.S. Rappaport, “Smart Antennas for Wireless Communication”, Prentice Hall NJ, 1995. 4. R.A. Monzingo, T.W. Miller, Introduction to Adaptive Arrays, Wiley, 1980. 5. W.J. Huang, J.F. Doherty, “An Evaluation of Blocking Probability for Threefold SDMA”, Proc. Military Comm. ConJ, vol. 2, pp. 1248-1252,2001. 6. D. Nicholson, C.M. Lloyd, S.J. Julier, J.K. Uhlmann, “Scalable Distributed Data Fusion”, Proc. F$h Int. Con$ Inform. Fusion, ISIF, vol. 1, pp. 630-635,2002. 7. J. Yunjian, S . Hara, Y. Hara, ”Impact of Closed-loop Power Control on SDMNTDMA System Performance”, Proc. 56th Vehicular Tech. Con$ Fall-2002, vol. 3, pp. 1825-1829,2002. 8. J. Yunjian, S. Hara, Y. Hara, ”Performance of T D D - S D W M A System with Multi-Slot Assignment m Asymmetric Tmffic Wireless Network”, Proc. 13th IEEE Symp. Personal, Indoor, andMobile Radio Comm.,vol. 5 , pp. 2317-2321,2002. 9. S. Haykin, Digital Communications,Wiley, 200 1. 10. F. Rashid-Farrokhi, L. Tassiulas, K.J.R. Liu, “Joint Optimal Power Control and Beamforming in Wireless Networks Using Antenna Arrays,” IEEE T. Comm., 46(10): 1313-1324,1998.

A RELIABLE AND ENERGY-EFFICIENT ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS PETER, KOK KEONG, LOH School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798 SAY W A N , LONG School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798 YI, PAN Department of Computer Science, Georgia State University, 34 Peachtree Street, Suite I450 Atlanta, GA 30302-4110, USA In wireless sensor networks, nodes generate important data that are of interest to the network users. These data have to be delivered reliably to the hub in the network which will then be disseminated to the network users. Routing protocols for such networks have to provide reliable delivery of data using minimal energy as nodes are energyconstrained. We propose EAR; a novel routing protocol that achieves reliability and energy efficiency in data delivery.

1. Introduction

With the introduction of wireless sensor networks (WSNs) [l-21, many applications have been conceptualized to leverage on this emerging technology. Some examples include battlefield surveillance, large-scale industrial and environmental monitoring. All these applications require data collected by the nodes to be delivered to some sink nodes also known as hubs which will then disseminate the data to the network users. A routing protocol that can provide reliable delivery of data is essential. Achieving reliability is not an easy task in WSNs because of high node failure rate and high packet loss. The nodes are battery-operated and therefore have limited energy resource. Routing protocols designed for WSNs have to achieve reliability while expending minimum amount of energy to maximize the network lifetime. 19

20

With this in mind, we propose a novel routing protocol called EAR that provides robust data delivery from the nodes to the hubs and also achieve energy-efficiency in WSNs. The remainder of this paper is organized as follows. Section 2 describes our proposed routing protocol in details. Simulation results are presented and discussed in Section 3. Finally, Section 4 concludes this paper. 2. Protocol Design Details 2.1. Overview In EAR, nodes generate Report (RPT) packet that contains information of interest to the network users. The RPT packet is then routed to the hub in the network. EAR requires a Medium Access Control (MAC) protocol that provides reliable link-to-link transmission. One example is IEEE 802.11[3] MAC protocol that provides reliable link-to-link transmission by using three-way handshaking mechanism. Using such MAC protocol is necessary in a wireless environment because RF links are unreliable and loss of messages is frequent due to message collision. Explicit control messages are therefore needed to detect lost messages. 2.2. Setup Phase The hub will broadcast an Advertisement (ADV) packet indicating that it is ready to receive RPT packet generated by nodes. When neighboring nodes around the hub receive this ADV packet, it will store the route contained in the ADV packet in their routing table. Nodes will not propagate the ADV packet received. Every node will back off for a random amount of time before beginning an initialization process. A node begins the initialization process by broadcasting a Route Request (RREQ) packet asking for a route to the hub. When a hub receives a RREQ packet, it will broadcast a Route Reply (RREP) packet containing the route information. Similarly, when a node receives a RREQ packet, it will broadcast a RREP packet containing the route information if it has a route to the hub. Otherwise, it will ignore the RREQ packet. Nodes do not propagate RREQ packet. When a node receives a RREP packet, it will store the route in its routing table. When it has at least a route to the hub, it skips the initialization process. Therefore, by introducing random delay for each node to begin initialization

21

process, a portion of nodes will have received a RREP packet before they have begun their initialization process. This enables fast propagation of routes and also saves on the amount of control packets generated in the setup phase. A node will store more than one route to the hub. A route in the routing table is indexed using the next hop node’s ID that is a neighbor of this node. We define Node 1 to be a neighbor of Node 2 and vice versa when they are one hop away from each other. In other words, they are within communication range of each other. A node will only keep one route entry for a neighbor that has a route to the hub. That neighbor could have multiple routes to the hub but it is of no concern to the node because all it needs to know is that this neighbor has a route to the hub so it can forward RPT packet to this neighbor. In the routing table, every entry is uniquely identified by the neighbor’s ID.

2.3. Route Management In WSNs, nodes have limited memory and therefore the size of the routing table has to be restricted. This leads to the question of how to select the best routes and to only keep the best routes in the routing table at all times. In EAR, two metrics are used to decide the admittance of a route into the routing table. 2.3.1. RouteLength Metric The primary metric is the number of hops a route needs to reach the hub, which is called RouteLength. The reason for choosing this metric is because the best route is always the shortest route (route with least RouteLength value). It will have the lowest packet latency and expends the least energy. However, the RF link between a node and each of its neighbors will not be the same because of the difference in physical distance and the type of terrain between them (e.g. two nodes might be obstructed by a tree that attenuates RF signals). In this situation, the best route is not the shortest route because trying to forward a packet to a neighbor with a shorter path but bad RF link quality will expend more energy in retransmission and also increases packet latency than forwarding to a neighbor with a longer path but with good RF link. In EAR, the above problem is eliminated by using a concept known as “Route Blacklisting”. Initially, routes are admitted into the routing table using RouteLength as admission criteria to ensure that only the shortest routes are chosen. As RPT packets start to flow through these routes, less desirable ones will start to exhibit high packet error rate and will eventually be blacklisted and omitted from the routing table. Routes that are omitted from the routing table

22

will not be admitted again until after a period of time. Some routes are only affected by temporary external disturbances and so should be given the chance to be re-admitted into the routing table after a period of time. 2.3.2. Routescore Metric

The second metric uses Routescore that is calculated using the formula as shown, Routescore = (PE x WE + PL X Wd PE- energy level of the next hop node (0.0 to 100.0) WE - assigned weight for PE (0.0 to 1.0) PL - link quality to next hop node (0.0 to 100.0). Uses a sliding window of size N gets the average ratio of successes on using this route. WL - assigned weight for PL (0.0 to 1.0) RouteScore takes on a value from 0 to 100 and a higher value indicates a better route. The sum of the weights (WEand WL)must be equal to 1. 2.3.3. Route Replacement

When a RREP packet is received, a check is done on the route carried on the RREP packet. If the route is a blacklisted route, it is ignored. If it is an existing route, then the route information is updated. If it is a new route and the routing table is not fill, then it is stored in the routing table. If it is a new route and the routing table is fill, a route replacement strategy is carried out. In the replacement algorithm, the first step is to search for the worst route with the largest RouteLength in the routing table. In the event of a tie, the route with the lowest Routescore is chosen. In the second step, the worst route is then compared against the incoming route and the path with lower RouteLength is admitted into the routing table. If there is a tie, then the route with the higher Routescore is admitted. To calculate the Routescore for the incoming route, an arbitrary value has to be assigned to PL as the link quality is unknown. Assuming the PE of the worst route and the incoming route are the same. The factor in deciding if the incoming route is to be admitted will depend on the value assigned to PL for the incoming route. 2.4. Data Dissemination

After the setup phase, every node in the network will have at least one route to the hub. Depending on the application, nodes will either start generating RF'T

23

packets at periodic intervals or go into idle mode waiting for some event to happen before generating RF'T packets. When a RPT packet is generated at a source node, it cames two fields that are used by EAR in its header; ExpPathLen and NumHopTraversed. The first field defines the expected number of hops this packet will have to traverse before it reaches the hub. It is initialized according to the formula, ExpPathLen = NH x a NH is the number of hops from this node to the hub for the route selected to forward this packet. a is some weight assigned from 0.0 to 1.0. NumHopTraversed records the number of hops a packet has traversed so far and is initialized to 0. The packet is then queued in the output buffer before being forwarded to the next hop in the route. When the next node receives the packet, it will increment NumHopTraversed by one and then compare it with ExpPathLen that is never altered after initialization. If ExpPathLen is larger than NumHopTraversed, the routing mechanism will choose a route with the highest RouteScore. Should there be a tie in the RouteScore; the route with the lowest RouteLength is chosen. By assigning a > 0, a packet can take a longer route with better link quality, assuming RouteScore is determined by link quality alone. If ExpPathLen is smaller than or equal to NumHopTraversed, a simple route selection mechanism requiring only two comparisons is used. Firstly, select route with the lowest RouteLength. If there is a tie, select the route with the highest RouteScore. The logic is that if the number of hops a packet has traversed exceeds the expected number of hops, there must be some changes in the network topology due to node failure or environmental noise affecting the RF communication. During this transient period of instability where routing table undergo changes, the packet will take the shortest route to the hub. The same routing mechanism is used at each intermediate node until the packet reaches the hub. 2.5. Route Update

Nodes in WSNs may be prone to failure and the unpredictable RF link quality between neighboring nodes changes frequently causing the network topology to change with time. Also, node energy levels will decrease according to the amount of data packets they receive for routing. Hence, nodes need to maintain updated and fresh routes in the routing table at all times. EAR uses a novel solution that provides rapid updating of route at negligible cost.

24

The solution uses the handshaking messages used by IEEE 802.1 1 MAC protocol. When node I wants to send a data packet to node 2, node I sends an RTS packet and when node 2 receives the RTS packet, it will send a CTS packet to node 1. The idea is to piggyback route infonnation on both RTS and CTS packet. This enables the neighbors of both node I and node 2 to obtain the latest route information of the two nodes.

3. Simulation We implemented EAR on GloMoSim [4] and compared it against GRAB [5]. We used GloMoSim to simulate a WSN where the nodes are referenced after crossbow MICA2 mote [6] which is a popular hardware platform for WSNs. The specification together with other settings for the simulator is shown in Table I .

Table 1: Simulator

We use the following metrics to measure the performance of the routing protocols. Packet delivery ratio (PDR): The percentage of data packets successfully routed to the hub. Packet latency: The average time it takes for a packet to be routed to the hub. Total Energy Consumption:The total energy expended by all nodes. We simulated a scenario where a noisy wireless medium is compounded with node failure. A noise model where every node in the network except the hub takes on a random noise factor between 10% and 50% was used. The noise factor of a node indicates the probability that packets received by the node are assumed to be corrupted or lost in transmission. A fault model where 50% of the nodes in the network fail at a random time within the simulation duration was used. We placed a source node generating packet at a rate of 1 packet every 10 seconds at one corner of the terrain area and a hub at the opposite comer. To study the scalability of the routing protocols, we conducted the test from 50 nodes to 500 nodes at 50-node interval. As the number of node increased, we increased the terrain size proportionately so to keep the node density constant.

25

The nodes are uniformly distributed in the terrain. We used a different seed for each run of the test and the results are averaged over 30 runs. 3.1. Results

The results are shown in Figure 1. EAR has higher PDR and lower packet latency than GRAB while expending lesser energy. GRAB uses broadcast to forward packets, the number of redundant packets generated are high. This results in high bandwidth utilization which increases the rate of packet collision and also incurs higher packet latency. The redundant packets also consumed additional energy. Although GRAB attempts to achieve reliability through forwarding redundant packets on a mesh but using broadcast does not allow a node to know if a packet has been successfully delivered to the next hop. If a packet is lost due to corruption after being broadcasted by the originating node, then that packet is lost for good as the originating node doesn't know of the failure and none of the neighbors will receive the packet.

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t L. 4--

3

3 m -

1

t

lo00 -

0

/

100

200

300

400

Numbr of nodas

Figure 1: Simulation results

4. Conclusions

We have proposed EAR; a reliable routing protocol for WSNs that uses novel techniques to achieve high reliability, low packet latency and low energy consumption compared to GRAB which is another routing protocol designed for reliability and robustness. Encouraged by current results, we will continue to improve the performance of EAR. References

1. Culler D., Estrin D., Srivastava M., “Guest Editors’ Introduction: Overview of Sensor Networks” IEEE Computer Magazine, vol. 37, pp. 41-49, August 2004 2. Akyildiz I. F., Cayirci E., Sankarasubramaniam Y., Su W., “A Survey on Sensor Networks” IEEE Communications Magazine, vol. 40, pp. 102-114, August 2002. 3. LAN MAN Standards Committee of the IEEE Computer Society, “Wireless LANmedium access control (MAC) and physical layer (PHY) specification”, IEEE, New York, NY, USA, IEEE Std 802.1 1-1997 edition, 1997. 4. Ahuja R., Bagrodia R., Bajaj L., Gerla M., Takai M., “GloMoSim: A Scalable Network Simulation Environment”, Technical report 990027, UCLA, Computer Science Department, 5. Lu S., Ye F., Zhang L., Zhong G., “GRAdient Broadcast: A Robust Data Delivery Protocol for Large Scale Sensor Networks” ACM Wireless Networks (WINET), Vol. 11, No.2, March 2005. 6 . CrossBow MICA 2 motes specification http://www.xbow.com

ANALYSIS OF COVERAGE AND CONNECTIVITY IN WIRELESS AD HOC SENSOR NETWORKS* JIANXIN WANG, LUPENG WANG School of Information Science and Engineering, Central South University Changsha, 410083, China

RENYI XIAO National Nature Science Foundation of China, Beijing, 100083, China

Coverage and connectivity are two fundamental problems in sensor networks and answer the questions about the quality of service that can be provided by a particular sensor network. We consider a new wireless sensor networks model that assumes each node can just connect with the other nodes that locate at the neighbor cells. And then we provide a mathematic analysis of the fundamental relationship among cells number, nodes number and the probability of coverage and connectivity. The analysis shows that the coverage is related to the number of sensor nodes and the size of the sensing area, the connectivity is also decided by the network topology besides the above two factors. The analysis results give underlying insights for treating coverage and connectivity in a unified framework.

1.

Introduction

Recently, the concept of wireless sensor networks has attracted a great deal of research attention due to its wide-range of potential applications [ 11. A wireless sensor network consists of tiny sensing devices, deployed in a region of interest. Each device has processing and communication capabilities and the base station aggregates and analyzes the messages received [2]. Since sensors may be spread in an arbitrary manner, two issues are quite important in the deployment of wireless sensor networks. They are [3]: Coverage: One goal of a sensor network is that each location in the physical space of interest should be within the sensing range of at least one of the sensors.

*

The work is supported by the National Natural Science Foundation of China under Grant No. 90304010

27

28

Connectivity: To aggregate all the sensor data, the sensors need to organize themselves into a connected ad-hoc network. The location and placement of the sensors determine the connectivity of the sensor network. The problem of sensing coverage has been investigated extensively. Some papers analyzed the coverage problem and developed corresponding algorithms, but they didn’t consider or simplified the connectivity problem [4-81. Several other protocols aim to maintain network connectivity, but do not guarantee sensing coverage, such as ASCENT [9], and GAF [lo]. There are also many works that consider both coverage and connectivity problem. Ref. [ 113 presents the design and analysis of protocols to achieve guaranteed degrees of coverage and connectivity. In [12], the authors at first proved a fhdamental result and based on this, they introduced a density control algorithm named OGDC. In [ 131, the authors consider different sensing ranges and give an extended result of [ 121. In this paper, we at first define a new model for coverage and connectivity. Then we analyze coverage and connectivity problem and investigate the relationship between coverage and connectivity. The analysis results give underlying insights for treating coverage and connectivity in a unified framework and can be used in the design of corresponding protocol for coverage and connectivity in wireless sensor networks. 2. Network Model and Problem Statement

In this paper, we use the following cell partitioned network model. As shown in Fig.1 (a), the network area A is partitioned into C non-overlapping identical hexagonal cells. There are many sensor nodes in area A that are placed randomly and independently. Each node can just communicate directly with the nodes that locate at the same cell and neighbor cells.

(a) Cell partitioned network (b) the corresponding graph (a) Fig.] Cell partitioned nc rork. and its corresponding graph

We provide a mathematic analysis of the relationship among cells number, nodes number and the probability of coverage and connectivity. The analysis is based on the basic characters of sensor networks and can be extended to analyze

29

other practical sensor networks. We mainly discuss the following problems that are important in the applications of such network model. (1) How much is the probability PoccupyaN(h? that all the cells are occupied by sensor nodes when there are N nodes in all? (2) How many sensor nodes Noccupya@th) are required to get the probability P o c c U p y a / ~ N o c c u p y a / ~ p t h lager )) than the threshold pth? (3) Suppose the probability of an independent cell being occupied is P, that is to say, for any one arbitrary given cell in wireless sensor network, the probability that there is at least one sensor node is P, what is the probability Pconnlp(P) that all the sensor nodes in area A are connected? (4) What is the probability Pconn(N)that N nodes that are distributed in C cells randomly can be connected? 3.

The Proposed Solutions

3.1. Coverage: Poccupy4~dN) and Noccupy4~~(Pt~

Lemma 1. Suppose to put n sensor nodes into k different cells ( n 2 k ), the number of the different ways to ensure that each cell has at least one node is given by

Proof: Let S be a set consist of all k" different ways of n sensor nodes placing in k cells ( n 2 k ). Supposing s E S , s has the property ai if the ith ( 1I

i I k ) cell

does not have a node. Let K ( ~ ... ~ a,4%. )be ~ , the ~ number of the elements in S, which have all the properties

,...,a i m , then K ( ~ , , ~ , ~ equals . . . ~ ~to~ the )

number of different ways of n nodes placing in the k-m cells, that is@-m)" . In terms of the inclusion-exclusion principle, the number of the elements in S that has none of the properties ail,ai2,...,aimis k

k

x K ( a i , a i ,. . . a i m )k" = +x(-l)"

Fk(n)=IS I + z ( - l ) " m=l

m=l

ISi,C) Theorem 4 can be proved directly by using Lemma 2, so that the proof is omitted for brevity. 1.0-

0.8-

0.6-

Z

'

*! 0.4 0.2-

0.0,

1 0

50

1W

N(lk tmdm of gnsm mds)

Fig3 Connectivitywith different number of sensor nodes

Fig.5 shows the relationship between the probability of connectivity and the number of sensor nodes. Basically, the probability Pc,,,,,,(N) of connectivity is increasing with the increase of the number of sensor nodes. But there is an exception when the number of nodes is very small. When the number of sensor nodes is 60 and C equals to 30, the probability of connectivity is 0.95327. 4. Conclusion

In this paper, we defined a new network model and analyzed the fundamental relationship among cells number, nodes number and the probability of coverage and connectivity and exact expressions were derived. The analysis shows that the coverage is related to the number of sensor nodes and the size of the sensing area, the connectivity is also decided by the network topology besides the above two factors. This analysis results gives underlying insights for treating coverage and connectivity in a unified framework. References 1. I. F. Akyildiz, W. Su, Y.Sankarasubramaniam, E.cayirci. Wireless Sensor

Networks: A Survey. Computer Networks, March 2002; 393-422

34

2. D.Tian, N.D.Georganas. Connectivity Maintenance on Coverage Preservation in Wireless Sensor Networks. In proceedings of CCECE 2004, May 2-5,2004, Niagara Falls, Canada. 3. K. Kar, S . Banerjee, Node Placement for Connected Coverage in Sensor Networks. Proceedings of WiOpt 2003, Sophia-Antipolis, France, March 2003. 4. K. Chakrabarty, S . S . Iyengar, H. Qi, E. Cho. Grid coverage for surveillance and target location in distributed sensor networks, IEEE Transactions on Computers, 51(12):1448-1453, December 2002. 5.

S. Meguerdichian and M. Potkonjak. Low Power 011 Coverage and

Scheduling Techniques in Sensor Networks. UCLA Technical Reports 030001. January 2003. 6. S . Meguerdichian, F. Koushanfar, M. Potkonjak, and M. Srivastava, Coverage Problems in Wireless Ad-Hoc Sensor Networks. INFOCOMOl, Vol3, pp. 1380-1387, April 2001. 7. D. Tian and N. D. Georganas. A coverage-preserving node scheduling scheme for large wireless sensor networks. In First ACM International Workshop on Wireless Sensor Networks and Applications, Georgia, GA, 2002. 8. C. Huang and Y. Tseng. The coverage Problem in a Wireless Sensor Network. WSNAO3,September 19,2003, San Diego, CA. 2003. 9. A. Cerpa and D. Estrin, ASCENT: Adaptive Self-Configuring Sensor Networks Topologies, INFOCOM, June 2002. 10. Y. Xu, J. Heidemann, and D. Estrin, Geography-informed Energy Conservation for Ad Hoc Routing, MobiCom 2001, Rome, Italy, July 16-21, 2001. 11. X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, and C. Gill, Integrated Coverage and Connectivity Configuration in Wireless Sensor networks, SenSys'03, Los Angeles, CA, November 2003. 12. H. Zhang and J. C. Hou. Maintaining scheme coverage and connectivity in large sensor networks. Technical report, UIUC, 2003. 13. J. Wu and S . Yang, Coverage and Connectivity in Sensor Networks with Adjustable Ranges, accepted to appear in 2004 International Workshop on Mobile and Wireless Networking (MWN), Aug. 2004.

mANET


CROSS-LAYER’S PARADIGM FEATURES IN MANET: BENEFITS AND CHALLENGES

LAMIA ROMDHANI AND CHRISTIAN BONNET Institut Eurecom 2229 route des CrCtes - BP 193, 06904 Sophia Antipolis, fiance E-maihomdhani,bonnet0eurecom.fr Nowadays, the cross-layer design approach, is the most relevant concept in mobile ad-hoc networks which is adopted t o solve several open issues. It aims t o overcome MANET performance problems by allowing protocols belonging to different layers to cooperate and share network status informations while still maintaining separated layers. The central key of related research studies is what information can be shared and how it used in cross-layer architecture to provide QoS enhancement and enable an efficient resource utilization. In this work, we detail the most coupling features of introducing cross-layer models in mobile ad hoe networks. Then, we discuss the risks and the challenges facing this new architecture.

1. Introduction

Ad hoc networks have many characteristics that meet a lot of node heterogeneity. A fundamental issue in such multihop wireless environments is that network performance can degrade rapidly as the number of hops increases. Major problems to transmit data over available radio channels exist in every layer of the protocol stuck. In one hand, adaptive rate selection, adaptive antenna pattern, adjust power control are issues of the physical layer. In the other hand, the link reliability, the admission control, and the access control to the shared channel are some issues of both routing and MAC layers. Moreover, there are several real-time application requirements that have to be respected in order to provide QoS support and achieve service differentiation. In the past, a lot of research have been conducted to address these issues separately. One new research direction to optimize data transfer in ad hoc networks is the cross-layer design without respecting the original layered design approach in which each layer operates independently. The layered approach is simple, flexible, and scalable as the case in the Internet, but 37

38

it led to poor performance in ad hoc network even with the optimization applied to the evolved protocols because they are no taking into account network and application constraints. For example, each layer have to react in route failures and collisions in its own way and there are no coupling of different layer informations to meet some parameters in order to address a good coordination of the efforts satisfying as well as possible the application requirements. As conclusion, the co-operation between layers to enable performance enhancement is very important and useful in wireless ad hoc networks. The global objective of such co-operation is to achieve a reliable communicationon-the-move in highly dynamic environments as well as QoS provisioning. In this paper, we review the parameters that should be provided by each layer to other layers in order to improve the global performance. In some cases, specific processing should be done by intermediate layers to present the parameters to other layers in a comprehensive and understandable way. Lot of works have been presented in the open literature that introduce several coupling ways and solutions between different communication layers 1,2,4

The remainder of this paper is organized as follow: In Section 2, we discuss the problems of accommodating a good service for each layer in the layered approach going from the physical layer to the application layer. We also identify the most important parameters in each layer to be managed in a cross-layer architecture. Then, we review the most works that have been conducted to study the cross-layer design in mobile ad hoc networks in Section 3. In Section 4,we outline our observations that lead to fix the potential risk of cross-layer design in MANET. Then, we give our recommendations on how a cross-layer architecture should be designed in an efficient and scalable manner in Section 5. We conclude the paper in Section 6.

2. Limitations of Layered Approach in MANET

As it is well known, networks are organized as a series of layers, each one built upon the one below it. The goal of this architecture is to split the network into smaller modules with different functionalities and deal with more manageable design and implementation. The purpose of each layer is to offer certain services to the higher layers, shielding these layers from the details of how the services are implemented. So the advantage behind the layered protocol architecture is to reduce complexity by dividing and

39

conquering approach. This simplicity ensure an easy way to standardize, and to deploy new flexible protocols (easy upgradeable). However, wireless networks don’t come with links. The channel quality changes dynamically. The applications require a minimum of QoS that could not be achieved in such very dynamic capacity networks. Hereafter, we analyze the problems related to each layer and we give an overview of the characteristics and QoS metrics of each layer.

2.1. Limitations Related t o Physical Layer7$ Characteristics The wireless channel varies over time and space and has short-term (or small-scale) memory due to multipath. The channel variation meets the amount of contention, time-varying fading, multi-path, variation of the SNR. Indeed, these variations are caused either due to motion of the wireless device, or due to changes in the surrounding physical environment, and lead to detector errors. This causes bursts of errors to occur during which packets cannot be successfully transmitted on the link. Fast channel variations due to fading are such that states of different channels can asynchronously switch from good to bad within a few milliseconds and vice-versa. Furthermore, very strong forward error correction codes (i.e. very low rates) cannot be used to eliminate errors because this technique leads to reduced spectral efficiency. The techniques that may be used to adapt to rapid SNR changes in wireless links and mobility include: power control, multiuser detection, directional antenna, adaptive modulation and software radio. However, sharing these informations with high layers, has a big benefits on performance as shown in For example, characterizing the application requirements help to use the adaptive modulation, the knowledge of channel quality help to avoid useless MAC retransmission..etc. 2y7.

2.2. Limitations Related t o MAC layer’s Characteristics

The main issue at MAC layer is to adress an efficient medium access mechanism to resolve contentions. CSMA-based MAC protocols can yield an efficient operation (under proper loading levels) when the carrier sensing operation is spatially effective. Unfortunately, stations may be geographically located in a manner that induced blocking, leading to masked terminal scenarios. In this case, two major problems have been identified: hidden terminal and exposed terminal conditions. Despite of introducing RTS/CTS handshaking scheme, leading to the Multiple Access Collision Avoidance (MACA) protocol, the MAC layer still suffers from the problems of inter-

40

ference resolution, exposed terminal, efficient medium utilization. Indeed, the optimal strategies of resource sharing issue among different classes of users, still the main challenge also for the FDMA, TDMA techniques. 2.3. Limitations Related to Routing Layer’s Characteristics

Routing protocols for ad hoc networks require to consider the reasons for link failure to improve its performance. It sould be adaptive to cope with the time-varying topology and time-varying network resources. For instance, it is possible that a route that was earlier found to meet certain QoS requirements no longer does so due to the dynamic nature of the topology. In such a case, it is important that the network intelligently adapts the session to its new and changed conditions. So, the goal of QoS routing is to optimize the network resource utilization while satisfying application requirements. Indeed, it is not enough to find a shortest path but also with available resources as battery, bandwidth, and buffer. Note that the factors that can change the topology of an ad-hoc network are: the mobility of nodes, change of power, the MAC layer mechanism because different schedule for the contending nodes, results in different topology, the flow dynamics that flows come and go; if a node has nothing to transmit, its links are gone from the topology, and finally the mode of nodes: sleeping or active mode. If a node goes to a sleeping mode, its links are gone from the topology and hence it can’t participate to route establishment and communication.

2.4. Limitations Related to h n s p o r t Layer’s

Characteristics TCP combines error control (ARQ), flow control that are not over-running the receiver buffer, and congestion control that is not clogging the network, and not overloading the capacity in the routers. Moreover, TCP enjoys simplicity of control and gains widest acceptance. However, this simplicity of control is at the cost of efficiency loss. TCP is not able to distinguish the presence of congestion in wired networks, mobility, collision in wireless links, and bit errors due to poor quality of wireless links. Single bit error could trigger congestion control mode (TCP getting into slow start phase); even fast retransmit/fast recovery is not effective in coping with packet/bit errors. So, TCP needs to handle delay (RTT) and packet loss statistics that are very different from those in wired networks.

41

2.5. Energy conservation Some scenarios where an ad hoc network could be used are business associates sharing information during a meeting, military personnel relaying tactical and other types of information in a battlefield, and emergency disaster relief personnel coordinating efforts after a natural disaster such as a hurricane, earthquake or flooding. In fact, in such scenarios, maximizing the network lifetime is a very important deft since recharging battery is very difficult (hard) to do in such conditions. Indeed, the network connectivity is strictly related to the possibility of routing between each node in the network. The energy exhaustion problem leads to network disconnection and resource unavailability problems. 2.6. Limitations Related to Application Layer’s

Characteristics There are some application’s requirements that should be considered in order to maintain as good as possible the performance and offer a minimum service delivery according to their constraints. Indeed, there are time-bounded applications that are sensitive to delay and others require high throughput and/or less packet loss rate. For example audio traffics should reach destinations at most up to 400 sec. The corresponding packets are almost short. They could have the highest priority: minimum waiting time in the queue, and so short medium access time ( e g short contention window size). Moreover, they require short and less congested routes to reach destinations within a short delay. Throughput-constrained applications require less congested routes and available queue to enqueue packets. Hence, successful transmission should be assured and they are less sensitive to delay comparing to above described class. TCP traffcs are very sensitive to both packet loss and delay. Background traffic should not be starved and so a minimum service has to be guaranteed. The key question is how to adapt physical layer parameters, distribute fairly the access to the medium and achieve an efficient bandwidth sharing while providing service differentiation and application requirements with the less possible complexity? In the next Section, we describe how these problems and information, related to each layer, are exchanged over the different protocols in the layered stack in order to address cross optimization and QoS provisioning. Hence, we discuss the most various cross-layer approaches that have been proposed in the literature.

42

3. Review and Discussion of Cross-Layer Proposals

Each layer of a stacked set of modules maintains an independent set of statistics for error conditions and performance metrics. When a problem occurs, it may manifest itself as aberrant statistic values in multiple layers in the system. In classical systems, there is no logic that correlates these aberrant statistic values across different system layers. This lets thinking about alternative solution as cross-layer design. The main feature of the proposed studies in the literature is the determination of what information could be shared and how is it used in a cross-layer architecture to provide QoS enhancement and enable an efficient resource utilization? Hereafter, we describe some examples of cross-layer integration for ad-hoc networks. 3.1. Physical layer 0

+ MAC

Adaptive power control and MAC

The proposed solution is based on MAC and physical layer cooperation. It estimates the channel using RTS packet and transmit the information using the CTS packet. Then, an adaptive power control mechanism is described according to the obtained information from MAC layer. Furthermore, a receiving node should be spatially separated from any other transmitter by at least a distance D that leads to spatial separation. So, the D parameter greatly influences the amount of interference suppression: If we have low D, more users are selected in the valid set, but a lot of interference must be managed by the power control. The drawback is that power control may not be feasible. If we have high D, there are less users scheduled and so easier job for the power control. However, the scheme may be too conservative higher delays resulted from scheduling because of only these users can run the power control algorithm. In the second step, the power control mechanism optimizes the power allocation among different users. If we have few scheduled users, the MAC layer does not re-optimize its selection based on information from the physical layer ,the loop is not closing. 3.2. Physical layer 0

+ M A C + routing

Adaptive modulation

+ MAC + routing

A cross layer networking system is described in '. The paper proposes a coordination between routing, MAC, and physical layers. When a node

43

receives the RTS packet, it estimates the SNR. Then, the transmission rate is mapped from the estimated SNR, and appended to the CTS packet. So, the sender transmits data at the adapted rate. An M-QAM scheme is used in which the constellation size changed with SNR. The routing decision is made based on three metrics. The first one is the bandwidth that represents the rate of link between node i and j. The second one is the interference duration that is the interval from the when the RTS packet is sent to when the data packet is received. The third one, is the congestion that is the queuing delay in the buffer of transmit node. Adaptive beamforming

+ MAC + routing

In 7, the proposed mechanism describes a cooperation between adaptive beamforming, MAC, and routing. The scheme uses the same MAC for directional antennas, but transmits RTS over multiple hops (MMAC protocol). The presented network performance results depend on the simulated network topology. There are several cases that were studied: Manhattan networks with aligned routes, Manhattan networks with random routes, and Random configuration. For all three cases, the numerical parameters chosen are: Antenna beamwidth equal to 45 Omnidirectional, transmission range equal to 250 m, Directional transmission range (DD link) equal to 900 m. The performance measure only average throughput. It shows that in general MMAC, better than DMAC, better than 802.11. However, when the routes are aligned, using MAC and directional antennas degrades the performance, compared to the case with omnidirectional antennas (802.11). For Manhattan networks, more directional interference occurs, due to the aligned paths. The gain is more if we can actually exploit the spatial reuse property of the directional antennas. If not, the performance will be worse because of the increased directional interference (higher gain for the directional antennas). 0

Power control

+ scheduling + routing

The cross-layer approach introduced in 5, is presented at four levels: First, the proposed adaptive MAC protocol is sensitive to contention. Second, influence of network layer FIFO queuing on better bandwidth utilization. Third, importance of transmission scheduling. Fourth, routing and power control interactions. In the proposed Progressive Back Algorithm (PBOA) Protocol, nodes contend during every contention period. Unsuccessful nodes progressively backoff during progression of contention period. Successful nodes use remaining contention interval to discover minimum power

44

needed to transmit their data. There are two benefits of this approach: the first that energy conservation is enhanced because of tuning transmission range as possible. The second benefit is that both interference and collisions are reduced thanks to the proposed backoff procedure. The performance of these interactions depends on several constraints. Indeed, The cost of packet control overhead and packet lost could be more significant than the performance improvement in an arbitrary mobile node in a particular scenario. The key observation is that the protocol performance looks worse than some optimal choices because these two protocols are distributed and hence require global knowledge to schedule their transmissions which is hard to achieve in a very high mobile and distributed networks. 3.3. MAC 0

+ Routing + network layer

MAC utilization+Interface queue+reactive routing protocol

In 8, the authors propose a mechanism for detecting network congestion, in order to improve the performance of all types of traffic. Indeed, there are two metrics which are used to measure the congestion level. The first one, is the average MAC layer utilization around each node. Instantaneously, this metric can be equal to 1 or 0. It is equal to 1 if the MAC layer is utilized (there is at least one packet in the transmission queue, during backoff decrease period, inter-frame space, detection of physical carrier). The second metric, is the instantaneous interface queue length. This metric is used to avoid nodes that are congested even there is no contention. The proposed mechanisms aim to influence routing decisions that will follow other route discovery scheme either than the short hops count used traditionally. Indeed, it is unsuitable to establish routes over nodes that are already busy. However, if we avoid busy nodes in route establishment, there are some routes that cannot be established even they exist. The congestion information is also used when the medium utilization is high, to influence the setting of the Explicit Congestion Notification (ECN) bits in the IP header of packets at each node. ECN is used to prevent the loss of packets along that flow. At transport layer, the MAC layer utilization metrics measured around the node allow TCP sender to tune its parameters according to these metrics since they represent a recent level to the wireless medium utilization. At higher layer, these metrics can be used to decide or not data compression. Indeed, when the medium is busy the sender can decide to compress the data. However, the compression should represent a trade off between bandwidth consumption and the CPU time used for compression

45

and decompression. 0

topology information+Enhanced back-off +Proactive routing

In 9 , a cross-layering design has been presented in the context of research project called MobileMAN. This project investigates a local interaction among protocols in a manet node. For example, MAC layer exploits the topology information collected by network layer to achieve fair channel scheduling and fix the problem related to hidden and exposed terminals. An enhanced backoff scheme is introduced. At transport layer, the different events occurring at lower layers such as route failure, route changes and congestion, are analyzed in order to minimize the useless data retransmissions. Moreover, MobileMan considers routing according to the crosslayering principal. Indeed, a path per-formability index is computed using congestion, link quality, and other parameters that can influence system performance. Furthermore, the MobileMan transport protocol exploits information reported by the routing and Wi-Fi layers in the Network Status component to avoid useless data retransmission.The authors suppose that a node has a knowledge of the hole network topology and so a proactive routing protocol should be used. Hence, it seems that for some scenarios, it is very hard, costly, and not efficient to address this cross layer architecture regarding the dynamic traffic nature and the high mobile node speed. Any information has been provided to how compute the path per-formability index or other cross layer parameters considered in this project. Probability of successful transmission+route selection+energy conservation In 3 , tow cross-layer designs based on energy consumption are presented.The proposed schemes, namely, Energy-Constrained Path selection (ECPS) and Energy-Efficient Load Assignment (E2LA), employ probabilistic dynamic programming (PDP) techniques and utilize cross-layer interactions between the network and MAC layers. They aim to enhance the operation of existing power-based multi-path routing protocols via crosslayer designs and optimal load assignments. The Energy-Constrained Path Selection (ECPS) consists of maximizing the probability of successful transmission in at most n retry. That is mean that the total n transmissions don’t exceed a total amount of energy equal to y. Furthermore, the authors developed four distinct reward schemes for which E2LA assigns routing loads accordingly. In ECPS mechanism, the MAC sublayer provides the network layer with information pertaining to successfully receiving a CTS

46

or an ACK frame, or failure to receive one. ECPS, in turn, chooses the route that will minimize the probability of error or, equivalently, maximize the expected reward. The proposed medium access control (MAC)-based performance studies, revealed that battery capacity may not be efficient for achieving energy-based fairness and system longevity for wireless mobile multi-hop ad hoc and sensor networks. However, energy conservation may be attained only if valuable MAC (and PHY) input is passed to the network layer. In addition, illustrative examples of E2LA were presented, and its diverse properties were introduced and validated. 0

power control

+ topology information

In a study of cross-layer design based on power conservation, and congestion informations in ad hoc network have been presented. The authors describe a power control based cross layer architecture. Indeed, they detail the significant impact of power control on all protocol stack above the physical layer. Furthermore, they summarize several works that have been done to address power saving in the protocol stack and show how the power information could be considered at each layer. Moreover, the work claims that, exchanging the topology information between different layers through their interfaces, is very important to support QoS such as geometric location, channel, link conditions. A proposed mechanism, that uses the number of neighbors around the node to adjust transmission power, has been presented. 3.4. Physical layer 0

+ MAC + Application

SNR information +MAC retransmission+ adaptive FEC

Real-time applications, such as audio and video streaming over wireless links, suffer from bandwidth variation, packet losses, and heterogeneity of the receivers. In 6 , the authors propose to exploit the mechanisms available at the lower layers of the protocol stack in order to address an adaptive cross-layer protection strategies for robust scalable video transmission. This mechanism uses a multipath channel model to simulate the wireless indoor channel. This channel model provides the bit error rate (BER) of the link for the eight different PHY modes of 802.11a under different channel signalto-noise ratio (SNR) conditions. Then, the authors analytically derive the packet loss ratios and throughput efficiency at various channel conditions, considering a given packet size, a given number of retransmissions at MAC layer, and an application layer FEC. These parameters are dynamically

47

adapted according to an end-to-end distortion model in order to achieve an efficient transmission of video streams. The presented algorithm presents a good performance for video streaming. However, it is only centralized. In the next section, we discuss the constraints of introducing cross layer architecture and the recommendations to achieve a good and optimized solution. 4. The Implementation Cost of a Cross-Layer Architecture

The advantages of cross layer design is to exploit the interactions between layers in order to improve QoS support and optimize resource utilization. Moreover, this new architecture promotes adaptability at all layers based on the exchanged information and tight their interdependence. However, understanding and exploiting the interactions between different layers is the core of the cross-layer design concept. For example, the cross layer models introduced in and in 9 , require respectively the congestion information and hole topology information to build routes using layer cooperation mechanisms. Hence, if we consider high variable scenario in term of mobility and traffic load, the collected metrics will be inaccurate and so become inefficient and very costly. Indeed, it is hard to characterize the best and efficient interactions between protocols at different layers. Moreover, joint optimization across layers may lead to complex algorithms. Note that complexity consumes more resources for computing and introduce a new problem of scalability. So, we have to answer the following question: is cross-layer design suitable for all types of wireless networks and all types of applications? If yes, that means that we have to throw away the OSI reference model and we don’t need to consider a network architecture anymore? This is clearly impractical and disaster in terms of implementation, debugging, upgrading and standardization. The solution is to maintain the layered approach, while accounting for interactions between various protocols at different layers. 5. Achieving a good trade-off between complexity and

enhancement in cross-layer architectures While cross-layer model could enhance the performance of the applications and achieve better QoS support, there is a lot of proposed models that have to be compared and optimized. In the most cases, we have to take into account the benefits of each model that provides layer cooperation comparing to its complexity. Indeed, there are some proposals that compute global

48

or local metrics which are used to make decisions for route establishment, scheduling, tuning transmission rate, etc. However, using these metrics in a cross-layer model could be not efficient because they are have sometimes inaccurate values which do not reflect the real situation around a given node. Moreover, since a node moves with an arbitrary speed and toward an arbitrary destination, the computed metrics (according to the participation of the node in communication and the traffic load level around it) could change during the time. So, other nodes that consider the metrics of that node to build routes for example, could have an inaccurate information since this later change according to mobility, traffic, and capacity. We believe that cross-layer a QoS model is a somewhat “danger”. In one hand, the modification that we have to add in the protocol stack and the complexity in introducing a new parameters and new algorithms to provide a ”good” layer cooperation are usually introduce a high complexity risk. In the other hand, this could be very interesting given that it captures the characteristics of the capacity, the expected behavior of node load to choose the ”best route’’ between sources and destinations in a way to achieve a global load balancing, and in other cases have knowledge about neighbor density and ”quality” to adapt transmission rate and to use scheduling strategies in an efficient manner. So, if we recapitulate, cross-layer is a promised solution to address QoS support and service differentiation in mobile ad hoc networks, but it is affected by mobility and so the “lifetime“ of the availability of the accurate available informations. We recommend the following requirements to efficiently design a QoS cross-layer model which leads to the architecture shown in Figure : (1) Choosing the metrics: choosing of a very useful and efficient metrics such as battery level, available bandwidth, and mobility rate. (2) Computing the metrics: the way of computing these metrics regarding one path (energy, lifetime of nodes, throughput, delay, etc.) have to be decided. The well-known approach is to minimize a cost function for a given link in the path between a source and a destination then consider the different costs computed for all links in the path. Depending on the nature of the metric, the cumulative value could be additive, concave and multiplicative. Other techniques could be also used such are variance and max-min. Computation and complexity costs should always be taken into account.

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(3) Adapting metrics’ values: an adaptive method should be used to update the measured metrics: They could be updated even more when mobility increases and less in a stable network while taking into account traffic load variation and application requirements. (4) Deciding to use or not the metrics: As shown in Figure 1,considering the information useful for model selection, the more efficient model has to be chosen according to the two following parameters: (a) Regarding to the network behavior: in some cases, when the traffic load and its characteristics change rapidly (high mobility), it is very difficult to compute accurate values of the metrics that can be used to address QoS. Hence, the complexity of the cross-layer model becomes too high comparing to the expected performance enhancement and it is recommended in this case to use the legacy layered approach. (b) Regarding to the user application: each layer of the protocol stack responding to local variations and informations from other layers. We have to evaluate the benefits and the disadvantages of the cross layer model for each specific user application.

Inlormations

Transpon layer Cross-Layer

uselul for architecture

Architecture

selection

Physical layer Layered Archltecture

Figure 1. New architecture design

As a conclusion, the decision to use a cross-layer model is very coupled with the nature of the user application and the evolution of the network behavior. The very promising cross-layer design model consists in maintaining the layer isolation in the protocol stack while enabling a cross-layer interaction according to network and traffic characteristics.

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6. Conclusion

Many subsystems of appliance operating systems are implemented as stacked modules. For example, the TCP/IP subsystem consists of the link layer, the network layer (IP), the transport layer (TCP and UDP) and the application layer organized as a protocol stack. In this paper, we discussed the most important features based on crosslayer exchanged information, introduced for mobile ad hoc networks. Despite of the performance improvement that this new design can achieve, there are some risks into changing the legacy layered architecture. Indeed, several issues need to be talcked before these interactions can be successfully exploited such as implementation, debugging, upgrading and standardization. We have to specify and explain whether cross-layer paradigm is suitable for all types of wireless networks and applications or not. Even if the answer is yes, it is necessary to maintain the layered approach, while enabling interactions between various protocols at different layers. References 1. X.Li and Z.Bao-yu, Study on cross-layer design and power conservation in ad hoc network, PDCAT, Pages324 - 328(2003). 2. ElBatt, T.; Ephremides, A.; Joint scheduling and power control for wireless ad hoc networks, Wireless Communications, IEEE Transactions on , Volume: 3 , Issue: 1 , Jan. 2004 Pages:74 - 85 3. A.Safwati,and al. Optimal cross-layer designs for energy-eficient wireless ad hoc and sensor networks, Performance, IEEE Computing, and Communic& tions Conference, 2003, 9-11 April 2003 Pages:123 - 128 4. Wing Ho Yuen; Heung-no Lee; Andersen, T.D.; A simple and eflective cross layer networking system for mobile ad hoc networks, PIMRC 2002. 5. Toumpis, S.; Goldsmith, A.J.Performance, optimization, and cross-layer design of media access protocols for wireless ad hoc networks, ;Communications, 2003. ICC '03. IEEE International Conference on , Volume: 3 , 11-15 May 2003 Pages:2234 - 2240 vo1.3 6. V.Schaax, and al. Adaptive cross-layer protection strategies for robust scalable video transmission over 802.11 WLANs, Selected Areas in Communications, IEEE Journal on , Volume: 2 1 , Issue: 10 , Dec. 2003 Pages:1752 - 1763 7. R. R. Choudhury, X. Yang, R. Ramanatham, N. Vaidya, Using directional antennas for medium access control in ad hoc networks, ACM, Mobicom atlanta, September 2002. 8. Y . Hu and D. B. Johnson, Exploiting Congestion Information in Network and Higher Layer Protocols in Multihop Wireless Ad HOGNetworks, ICDCS March 24-26 2004 Hachioji, Tokyo, Japan. 9. M.Conti, and alcross-Layering in Mobile Ad Hoc Network Design, Published by the IEEE Computer Society, February 2004.

TWO BANDWIDTH-VIOLATION PROBLEMS AND BANDWIDTH-SATISFIED MULTICAST TREES IN MANETS CHIA-CHENG 'Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan ERIC HSIAO-KUANG WU Department of Computer Science and Information Engineering National Central Universiiy, Chung-Li, Taiwan GEN-HUEY CHEI? In the existing mobile ad hoc network (MANET) Quality-of-Service (QoS) routing/multicasting protocols, the methods of bandwidth calculation and allocation were proposed to determine routes with bandwidth guaranteed for QoS applications. As our observations, two bandwidth-violation problems will be incurred in the above protocols. In this paper, a heuristic algorithm is proposed to determine a feasible bandwidth-satisfied multicast tree without the two bandwidthviolation problems.

1. Introduction A multicast group contains a special host (server) that is responsible for transmitting data packets to the other hosts (clients) in the same group. A mobile ad hoc network (MANET) is formed by a group of mobile hosts that can communicate with one another without the aid of any centralized point or existing infrastructure. Due to the recent provision of high-speed wireless Internet services, the QoS requirement applications will be crucial for the integrated new-generation wireless multimedia communication Systems. In the existing QoS routing/multicasting protocols [ 1-51, some admission methods were proposed to determine bandwidth-satisfied routes for bandwidthrequirement requests. However as our observations, two bandwidth-violation problems in the above existing MANET QoS routing/multicasting protocols would be incurred. First, this problem arises because a host only considers its local bandwidth (one-hop) transmission information while determining whether a new route can 51

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pass it or not. This problem is called as Hidden Route Problem (HRP) in this paper. Second, another bandwidth-violation problem, called as Hidden Multicast Route Problem (HMRP), would mislead the bandwidth reservation for QoS multicast applications in the existing QoS multicasting protocols. In Section 2, the bandwidth-reservation methods in the existing QoS routing/ multicasting protocols are overviewed and the more details for HRP and HRMP will be illustrated. In this paper, we aim to determine a feasible bandwidth-satisfied multicast tree without HRP and HRMP in MANETs. We minimize the number of forwarders for reducing the number of hosts participating in packet forwarding so as to lower bandwidth and power consumption. To attain the purpose, a heuristic algorithm to obtain a feasible bandwidth-satisfied multicast tree, denoted as FBST, is proposed in Section 3. In Section 4, studies are carried out to evaluate the performance of the proposed algorithm. In Section 5 , this paper concludes with some remarks and future works.

2. Bandwidth-reservation and related problems In wired networks, since a dedicated point-to-point link liJ between host vi and vj is used, the maximum bandwidth b-maxjj of 1, is only consumed while vi or vj transmits packets to each other. The neighbors of vj and 5 will not consume b-max, when vks transmit packets to vi or vj so that the remaining availability bandwidth b r i j for l j j can be computed by v; and vj. Based on the above, the judgment is achieved easily for the requested flow with bandwidth requirement b-req. If brjJ2b-req, vi has enough available bandwidth to forward the flow to vj. A bandwidth-satisfied route Ra,fv,+vb+vc+vs,. ..+ve+vf is determined as the equation: min{b-r,b, b-rb,,, b-r,d ,..., b-re,f} 2b-req. The routing protocols [l, 21 and the multicasting protocols (MCEDAR [4] and M-CAMP [ 5 ] ) utilize the concepts by using point-to-point link from the wired networks for MANETs. Since a host shares the radio channel with all its neighbors in MANETs, the bandwidth will be consumed not only by itself but also by its neighbors. Refer to R,)h the remaining bandwidth in three hosts (va,V b and v,), in which v, and v, are located at the transmission range of vb, will be consumed if vb transmits the packets to v,. That is the consumption will occur at the links l,s, in which V ; E {v,, vb, v,} and vjs are the neighbors of v,, vb and v,. Further, other forwarders along Raf make the similar bandwidth consumption to their neighbors. AQOR [3] proposed a different scheme for computing the bandwidth. A host is modeled as a resource unit in contrast to a link. Suppose the maximal

53

bandwidth and the remaining availability bandwidth of host vi are denoted as b-maxi and b-ri, respectively, to replace b-maxij and b-rij. Referring to the bandwidth-satisfied route R, the total consumed bandwidth b c i to the host vi by the requested flow with bandwidth requirement b-req will be computed as following equations: b-c,2b-req, b_cb=3b_req, hcC=3b-req, b-cp3bpreq,. .., b-ce=3 b-req and b-crb-req. However, no matter link resource oriented scheme or host resource oriented scheme in the above QoS routing/multicasting protocols, two bandwidthviolation problems HRP and HMRP would likely occur. HRP An illustrative example is shown in Figure l(a), where two routes v p v g and v,,, are established and one route v a j - + v eis being constructed. For --v,, convenience, we use to denote a route of one hop and to denote a route of one or more hops.

--

+

(a)

@)

Figure 1. The examples of HRP and HMRP

Suppose that the capacity of each host is constant, say 11 units, and the bandwidth requirements of va--ve, v p v g and v,,--v, are 3 , 2 and 7 units, respectively. When the route determination proceeds to host v,, the available capacity of v, has to be computed in order to determine if v, can be one forwarder in v,--ve. Now that the bandwidth requirement of va--ve is 3 units. If v, can be constructed as one forwarder of v,--v, , the bandwidth requirement for each of the three hops (Vb-v,, V,-Vd and v p v , ) is 3 units, i.e., the total necessary remaining capacity for v, is 9 units. There is an ongoing transmission from vf to vg in the radio coverage of v, whose bandwidth requirement is 2 units. Hence the capacity remaining for v, is 11-2=9 units and v, can be constructed as one forwarder of va--ve. However, the establishment

54

of forwarder v, increases the bandwidth consumption in the radio coverage of v~ to 12 units, which violates the capacity of vy. 0 HMRP Another bandwidth-constraint violation problem, HMRP, will likely happen when the multiple routes from a server to all clients are determined concurrently. The illustrative example for HMRP is shown in Figure l(b), where one multicast route from server v, to clients v, and vi (including va--v, and va++ v;) is being constructed. The problem is encountered when a multicast route is to be constructed from the server by broadcasting the routing request to the multiple clients. Suppose that the bandwidth requirement for each of v,++ve and va++vi is 3 units. When the route determination proceeds to host v,, the available capacity of v, has to be computed in order to determine if v, can be one forwarder in va+v,. Similar to the above, the total necessary remaining capacity for v, is 9 units and v, can be constructed as one forwarder of v,+-ve. Because the bandwidth reservation will be activated for the flow only when the real data flows at the host, the remaining capacity for vg is still 11 units such that vg can be at the same time. However, the constructed as one forwarder of va-+vi establishments of v,+-ve and v,++v; cause the bandwidth consumption in the radio coverage of v, to 12 units, in which there are four forwarders (vb,v,, vd and vg) and each forwarder consumes 3 units of bandwidth. That violates the capacity of v,. The same violation also happens in the radio coverage of vg. 3. Heuristic algorithm

In FBST, two procedures are designed. The first one denoted as SBSP is proposed to determine the shortest bandwidth-satisfied path without HRP for a bandwidth-requirement source-destination pair. The second denoted as SBST can determine a feasible bandwidth-satisfied multicast tree without HRP and HMRP for a multicast group. By SBSP and SBST, FBST determines a feasible tree selecting with minimum number of forwarders from a set of feasible trees which are derived from an iterative operation. The information to take as the input parameters of FBST is listed as follows: V: a set with n hosts, denoted by vl, v2,..., v,. djj: a binary integer djJ=l(did=0) to denote that v; is (is not) a neighbor of vi. b-mmi: maximum transmission bandwidth for vi. bongoing;:the sum of bandwidth transmitted from host vi for the ongoing flows. Dongoing: the destination set of the ongoing flows. v,: the sever of the request multicast group. D-reg: the client set of the request multicast group. b-req: the bandwidth requirement for the request multicast group.

55 PROCEDURE DIAGNOSE (F,D , 6 - req) { for each vi E F do ( b-aJieri =b-ongoingi + b - r e q ; if b - aJieri > b - mar j return false; I * vi without enough remaining bandwidth } I * to diagnose the neighbors of vi for avoiding violation * I for each vi E F do { for each neighbors v of vi , i s . , di,, =1 and i # j

PROCEDURE SBSP ( v , ,vd ,b - req, D , T ) ( x = Y (vd};

-

foreachv,EY a n d i # d ( if DIAGNOSE( ( vi } u T , D ,6 - req ) == tiue and d,,d == 1 the n hi = 1 and Pi = ( v i } ; else hi = m and P, = (}; while X is not empty do ( determine v x E X so that h, = min( hi I vi delete vx from X ; if h, f m ( for each v i E X and di,x = 1 do ( if DIAGNOSE(( v i } u P, u T , D , b - r e q ) = = true then hi = h, + 1 and Pi = ( v i } + P,;

{ if b - ongoing > 0 or v j E (Du D - ongoing) if ( C b - a J i e r , + ) > b - m a r , return false; d,.d

I

X};

}

} return true; I * vi can be a candidate forwarder * I

} return H and P ; I * H and P are the sets containing hi and Pi

}

Figure 2. High-level description of DIAGNOSE

E

*I

1 Figure 3. High-level description of SBSP

In SBSP, a sub-procedure DIAGNOSE in Figure 2 is proposed to judge whether HRP will happen for a request, in which the set of candidate forwarders, the set of destinations and bandwidth requirement are defined as F, D and b-reg, respectively. SBSP as shown in Figure 3 is proposed to determine the shortest bandwidth-satisfied path without HRP from the destination v d to all the other hosts, in which the path from v d to the sender v, is also included. Since SBSP will be utilized iteratively by SBST to determine a feasible tree, the input parameters D and T are used to record the relative information of the previous iterations. When SBST executes SBSP in the first iteration, the first client is contained in D and T is empty. In the second iteration, the first two clients and the forwarders determined in the first iteration are contained in T and D, respectively. The rest iterations are similar to the first two iterations. The second algorithm SBST as shown in Figure 4 is proposed to determine a feasible bandwidth-satisfied tree T.SBST executes ID-seqI times of SBSP for determining T. A template set D that contains some clients is used to make sure that each client in D should have enough bandwidth to receive the flow. Initially, T and D are set to empty. In the first iteration, the first client of D-seq is added to D and the shortest feasible path P, from v, to the first client is obtained from SBSP. Obviously there is no feasible tree for the group if P or P, is empty. If not, we set T=P,. In the second iteration, the second client of D-seq is also added to

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D such that D contains the first two clients of D-seq. In order to avoid HMRP, the first client and the forwarders belonged to T should be taken into consideration while the feasible path for connecting the second client is being determined. In the way, the feasible path to connect the second client and obtained from SBSP will not incur HRF' to the existing flows and HMRP to the first path derived in the first iteration. To connect the second client and T, the forwarders belonged to Tare the candidate points in SBST. We select a feasible path P,with the minimal number of forwarders, where VFT, so as to minimize the number of forwarders in the final feasible solution. Then, a new T is derived from merging T and Pf If P or Ps is empty, SBST stops the execution and returns possible-no-solution. In the rest ID-seql-2 iterations, the executions are the same as the second iteration. PROCEDURE SBST (v, ,D ( let T and D are empty; for i = 1 to I D - seq I do ( let v d be the ith element in D - seq ; add V d to D ; let H and P be the sets obtained from SBSP( v s , v d , b - req , D , T ) ; ifi=1 ( if P == empty or P, = empty return no - solution ; else T = P,;

else if P == empty return possible-n o - solution ; else ( determine PJ E P so that h , = min( h i I v i E T ); if PJ == empty return possible-n o - solulion ; else T = T u P J ;

"I I

0 4

. ,

1

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Figure 5 . Average admission rate

-- '

}

I return T ; }

Figure 4. High-level description of SBST

Nmbarofgmlpl

Figure 6 . Average receiving rate

FBST utilize SBST to determine a bandwidth-satisfied multicast tree without HRP and HMRP. In SBST, a feasible tree is constructed to connect the clients one by one based on the sequence of the clients. However, SBST may fail in finding a feasible tree whenever one exists. To remedy the disadvantage,

57

SBST will be executed several times by an iterative algorithm. A distinct permutation sequence of the clients is given to SBST for computing a feasible solution in the each iteration, and several feasible trees may be derived. In FBST, the final solution tree is selected from these trees. We know that there are ID-reql! permutations, in which D-req is the set of the clients, such that the number of the iterations to execute SBST is ID-reqI!. Suppose that Tiis the feasible tree derived from the ith iteration, where l...no

c o n p a d node broadcast HELP l o one+ep neighbors

8

n

I4 The wnossted node send NOTIFY te node B

-a

(b) some nolghbor node. rend OK to the congesled node &r re0iV.d OK, the Congested nod. ohms. nods D 10 inlermediale nod.

(dl The node B RdlW Path horn the conw1.d node to t k node 0

Figure 1. Load-balancing scenario

3.2. Message format and procedure of load-balancing

For providing load-balancing ability in IEEE 802.1 1 MAC, we have designed new messages as depicted in figure 2: HELP, OK and NOTIFY. Where 'queuestatus' is the available buffer space of the transmitting node. The size of queuestatus field is 2 bit and each value means that; 0 is empty, 1 is low, 2 is medium and 3 is high. IA field is the address of an intermediate node.

Determine queue status In load-balancing algorithm, we determine 'queue-status' with queue occupancy for decision of congestion.

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If queue occupancy < low threshold than queue-status = 0; Else if low threshold < queue occupancy < medium threshold than queue-status = 1; Else if medium threshold < queue occupanq < high threshold than queue-status = 2; Else queue-status = 3 ; Where queue occupancy is the ratio of the number of waiting packets at the queue over total queue space. HELP format Frame Control

oueue~stalus (2bils)

RA

Queue-alatus

Receiving

(2bns)

Address

TA

IA

FCS

OK format Frame Con,rOl

Transmitang Addregs

FCS

NOTIFY format F,Bme cDnlml

Queue-stalua (2bnU)

Recelvlng Address

TmnsmWng Address

Intermediate nDdeAddress

Fcs

RA : Receiving Address TA : TransmittingAddress IA : Intermediate Address

Figure 2. Packets’ format

Decision to send HELP message If ‘queue-status’ is greater than 2 when a node receives DATA packet, a node become congested node and sets IA to transmitting address of DATA packet and then broadcasts a HELP message to ask neighbors for help. Decision to send OK message When a node receives HELP message, if the ‘queue-status’ is less than the ‘queue-status’ of HELP packet and IA of HELP message is belong to my neighbors then a node sends OK packet to the congested node. Send NOTIFY packet to the intermediate node When congested node receives OK message, congested node sets IA of NOTIFY message to transmitting address of OK message and sends NOTIFY message to the intermediate node. Receive NOTIFY and reroute DATA packet When a node receives NOTIFY message, a node records NOTIFY to the database. After node receive new DATA packet, if RA of DATA packet is the same as RA in the database, a node replaces RA of DATA packet to IA that coupled with RA in the database

72

4. Performance evaluation We performed simulation to evaluate the validity of the proposed load-balancing scheme and compared its performances with IEEE 802.1 1 MAC by using NS2 network simulator [8] which is a freely available discrete-event object-oriented network simulator, which provides a framework for building a network model, specifying data input, and analyzing data output and presenting results. 4.1.

Simulation configuration

Two network models were used for simulation. In the first one, 20 mobile nodes were configured to create an ad hoc network as shown in figure 3 . The second network is that consists with 50 mobile nodes and node’s position is randomly generated. In all simulation, we used CBR (Constant bit rate) traffic source, each CBR source generates packet every 0.05 seconds. The packet size is set to 1K bytes. For each simulation, we run simulation 10 times to avoid the bias of random number generation. The simulation time of the network model 1 is set to 70 seconds and that of the network model 2 is 100 seconds. In ad-hoc network model 1, we configured 5 CBR connections as described in figure 3 :

4.2. Numerical results of 20 nodes model To evaluate performance of the load-balancing scheme, we executed simulations while varying queue size from 10 to 100 packets. Figure 4 plots an average number of received packets.

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We observe that our load-balancing scheme increases throughput up to 7 % as depicted in figure 4. Figure 5 and 6 depict average number of lost packets by collision and average number of lost packets by buffer over-flow. Loadbalancing scheme provides better performance in lost packets by collision than IEEE 802.11 MAC protocol. The reason is that load-balancing scheme provides ability to change the path fkom congested node to neighbor node. As shown in figure 5, the number of packets lost by collision is more decreased as much as increase queue size of nodes. It is because that the larger queue could contain more packets. The purpose of the load-balancing scheme is that distribute excessive load to neighbor nodes fkom a congested node. So loadbalance scheme is able to reduce packet loss by collision. Avemsa receIyed packets NYm8Cr of

paclet.

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Figure 4. Average received packet with variable queue size

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74

4.3. Numerical results of a random topology model

For evaluating performance of the load-balancing scheme in ad hoc networks, we use the second network model. This model is a random topology that consists of 50 mobile nodes and node’s position is randomly generated. Each node moves randomly by using the “setdest” command of ns2 simulator. Figure 6 plots average number of received packets with number of flows variation. We observe that overall throughput increase as the number of flows increase. In all cases, IEEE 802.11 MAC with load-balancing protocol provides better throughput than IEEE 802.11 MAC. The load-balancing scheme is able to increase throughput up to 14%. Averact9 -wad

packet.

N u m b s d padres

Figure 6. Average received packets in a random topology Loat by solffiion “umber d p . a . 3

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Figure 7, 8 depict the average number of lost packet by collision and by buffer over-flow. It shows that the load-balancing is effective in ad-hoc network which change topology by arbitrarily movement of mobile nodes. In general the results were quite positive in the sense that IEEE 802.11 MAC protocol with loadbalancing outperformed than IEEE 802.1 1 MAC without load-balancing in most cases. Using our technique, load-balancing scheme may improve performance when the traffic load is heavy and should distribute excessive load of a congested node efficiently.

U

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Figure 8. topology

Average ost packets by buffer overflow in a random

5. Conclusions

In this paper, we have presented an efficient load-balancing algorithm in IEEE 802.1 1 MAC for supporting QoS in mobile ad hoc networks. Load-balancing ability of our proposed algorithm is able to distribute excessive load of a node to its neighbors. Our algorithm takes advantage of distributing and efficiently using network resources (buffer space), reducing network congestion and increasing overall performance (throughput). For evaluate validity of load-balancing algorithm, we compare performance with IEEE 802.1 1 MAC by simulation. Simulation results show that loadbalancing algorithm distributes excessive load efficiently. And also it shows that load-balancing algorithm improves throughput and reduces packet loss. It would also be interesting to relax some of the assumptions in our system model to investigate wider applicability of self-stabilization. The study of QoS provision in MANETs can be an important area of future research. Load balancing algorithm would result in more efficient QoS provision in MANETs by cooperating prioritized queuing disciplines.

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References 1. C. E. Perkins. Ad Hoc Networking. Addison-Wesley, Upper Saddle River, NJ, USA, Jan 200 1. 2. I. Chlamtac, M. Conti, J.J. Liu, “Ad Hoc Networks” vol. 1, no. 1, pp. 13 6 4 , Jan 2003 3. M.W. Subbarao, J. S. Pegon “Simulation Framework for a Mobile Ad-Hoc Network”, Proceedings of OPNETWORK 1999, Washington DC. Sept. 1999. 4. Elizabeth M. Royer, Charles E. Perkins. “An Implementation Study of the AODV Routing Protocol“ vol. 3, pp. 1003-1008, Proceedings of the IEEE Wireless Communications and Networking Conference, Chicago IL, Sept. 2000. 5. David B. Johnson, David A. Maltz, and Josh Broch. “DSR The Dynamic Source Routing Protocol for Multihop Wireless Ad Hoc Networks. In Ad Hoc Networking”, edited by Charles E. Perkins, chapter 5, pp. 139-172. Addison-Wesley, 200 1. 6. Vincent D. Park, M. Scott Corson. “A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks”. http://www.cs.odu.edu/ skovvuri/tora.pdf cited 10.04.2004 7. Charles E. Perkins, Pravin Bhagwat. “Highly Dynamic DestinationSequenced Distance-Vector Routing (DSDV) for Mobile Computers“. In Proceedings of the SIGCOMM ’94, http://people.nokia.net/charliep/ txt/sigcomm94/paper.ps cited 0 1.03.2004 8. NS-2 homepage: http://www.isi.edu/nsnam/ns 9. Lahti Marja-Leena, “IEEE 802.11 Wireless LAN“, http://www.tml.hut.fi/ OpinnotITik- 10.55 1/2000/papers/IEEE~8O2/wlan.html, 2000. 10. I. Aad and C. Castelluccia “Differentiation mechanisms for IEEE 802.1 1” Proc. of IEEE INFOCOM 200 1, pp. 209-2 18. 11. M. Barry, A. T. Campbell, A. Veres, “Distributed Control Algorithms for Service Differentiation in Wireless Packet Networks”, Proc. of IEEE INFOCOM 200 1, pp. 582-590 12. G. Bianchi, I. Tinnirello, “Analysis of Priority Mechanisms based on Differentiated Inter-Frame Spaces in CSMA/CA.”, in Proc. IEEE VTC 2003, vol. 3, pp. 1401 - 1405, Orlando (FL), Oct 2003. 13. IEEE 802.11 WG, IEEE Std 802.11e/D8.0, Draft Supplement to IEEE standard for Telecommunications and Information exchange between systems. Local and metropolitan area networks. Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, Feb 2004.

A BANDWIDTH-EFFICIENTCROSS LAYER PROBABILITY ROUTING FOR MANETS * XIAOMEI WANG HONGYI W CHONGSEN RAN XIA ZHANG WEINING QI Dept. of CommunicationEngineering, Information Science and Engineering Institute Zhengzhou, 450002, P.R.China A cross layer routing protocol is proposed for MANETs in this paper, which is called Bandwidth-Efficient Cross Layer Probability Routing (BECLPR). BECLPR differentiates between the send bandwidth and the receive bandwidth of the node. Based on the available send/receive bandwidth range provided by the MAC layer, a probability model is defined in BECLPR which is called Available-Bandwidth-Aware Probability Model of the Node (ABAPMN). By that each node processes the route request message with a given probability on its ABAPMN in the route discovery procedure, BECLPR probabilistically distributes the loads among the nodes according to the available sendlreceive bandwidth resources in MANETs. Also the output queue length of the node on the path is considered in searching for a path. Simulation results show that BECLPR outperforms the Ad hoc On-demand Distance Vector routing (AODV) in terms of the packet delivery ratio and routing overhead. BECLPR has lower end-to-end delay than AODV when the network load is not too high.

1. Introduction

Mobile Ad hoc Networks (MANETs) [ 11 are multi-hop wireless networks where all mobile nodes cooperatively maintain network connectivity without communication infrastructures for routing. Many different routing protocols have been proposed in the literature [2-51. However these algorithms consider the shortest path with the minimum hop count as the route selection criteria. This may lower the network utilization and increase the end-to-end delay, since some particular heavily loaded mobile nodes have little bandwidth resources to support the packet-relaying function. Several load-aware approaches have been proposed, which consider the effect of the loads of a node on the neighbors [6, 71. But they do not differentiate between the incoming loads and the outgoing loads of the node. In fact, the total amount of a node’s available send (receive) bandwidth is affected only by the incoming (outgoing) loads of neighbors and *

This work is supported by the National Natural Science Foundation of China (NSFC) (Approved No. 60472064) and the National High Technology Research and Development Program of China (863 Program) (Approved No.2003AA123340).

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that of itself. Furthermore, as long as the available send (receive) bandwidth is adequate, a node can send (receive) data successfully even if the available receive (send) bandwidth is little. A Bandwidth-Efficient Cross Layer Probability Routing (BECLPR) is proposed for MANETs in this paper. BECLPR differentiates between the send bandwidth and the receive bandwidth. A probability model is introduced in the route discovery procedure of BECLPR, which is made based on the node’s available sendreceive bandwidth range and is called the Available BandwidthAware Probability Model of the Node (ABAPMN). By that each node processes the route request message (RREQ) with a certain probability based on its ABAF’MN in searching for a path, BECLPR is able to probabilistically distribute the loads among the nodes in the network and get the path with relative adequate available bandwidth resources. The output queue length is also considered in BECLPR. Simulation results show that BECLPR outperforms the Ad hoc On-demand Distance Vector routing (AODV) [4] in terms of the packet delivery ratio and routing overhead. BECLPR also has better end-to-end delay performance than AODV when the network load is not too high. This paper is organized as follows. Section 2 presents BECLPR in detail. Performance evaluations of BECLPR are introduced in Section 3. Finally, concluding remarks are found in Section 4. 2. BECLPR

Due to its efficiency, AODV is adopted as the baseline routing algorithm throughout this paper. BECLPR differentiates between the send bandwidth and the receive bandwidth which are used to send and receive data respectively. When a source node S has a flow for the destination node D, the routing tries to find a path with adequate bandwidth resources. Namely, S should have enough available send bandwidth; D has adequate receive bandwidth; the intermediate node(s) on the path should make both adequate. However it is very difficult to guarantee this due to the shared channel and the dynamics in MANETs. To handle this, three things are done in BECLPR. First, the total amount of the available receivekend bandwidth of the node is computed by the MAC protocol but not by the routing protocol, since the former is more aware of the availability of the bandwidth resources. Second, ABAPMN is adopted in the route discovery procedure to probabilistically achieve a path with relative adequate sendreceive bandwidth resources. Third, the output queue length of the node(s) is considered in searching for a path.

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2.1. Computation of the available senrt/receive bandwidth of the node The node in MANETs shares the wireless bandwidth resources with its neighbors. By analyzing the exposed and hidden terminal problem in detail, we realize that the total amount of a node’s available send (receive) bandwidth is affected only by the incoming (outgoing) loads of the neighbors and that of itself. In Figure 1 (a), the maximum data transmission capacity of the node is C. There are three flows. The corresponding used bandwidth is rl, r2 and r3. Now node X’s current available send bandwidth is C-rl-D, where r l is the used receive bandwidth of Y 1 for receiving the incoming flow and r3 corresponds to the used receive bandwidth of Y3. Similarly, X’s available receive bandwidth is C-r2-r3, where r3 is X’s used send bandwidth and r2 is that of Y2. From this example, we make two conclusions. First, the amount of the available send bandwidth of the node and that of the available receive bandwidth may be different. Evidently, it is good for routing to differentiate them. Second, the amount of the available send (receive) bandwidth of a node equals the value of subtracting the total amount of the used receive (send) bandwidth of all the neighbors and that of itself from the maximum data transmission capacity.

Figure 1. The illustration of node X sharing the wireless bandwidth resources with its neighbors.

Now let’s take a close look at the total amount of the used receive bandwidth of all the neighbors. Shown in Figure 1 (b), Y 1 has a data flow from Z1 and its used receive bandwidth is rl. Y2 is receiving from 22. Its used receive bandwidth is r2. Y1 is out of range of 2 2 and Y2 out of range of Z1. The two transmissions do not interfere with each other and may be transmitted simultaneously. So the total amount of the used receive bandwidth of all the neighbors (Y1 and Y2 in this example) of node X is the bigger one between r l and r2. When the nodes move about, another topology may form as shown in Figure 1 (c), where Y2 is in the range of Z1. The two transmissions interfere with each other and can’t be transmitted simultaneously. So the total amount of

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the used receive bandwidth of all the neighbors of X is the sum of r l and r2. It is similar for computing the total amount of the used send bandwidth of the neighbors. As a result, the available sendreceive bandwidth range of node X is achieved shown in Eqs. (1) and (2).

Bkilable

Where BZilab,eand stand for the available send and receive bandwidth of node X, C is the maximum data transmission capacity, Nb(X) is the neighbor collection of node X, B2edand BU2, are the used receive and send bandwidth of node i. Let M and N be the number of the incoming flows and that of the outgoing flows of node i respectively. We obtain B2ed = and B2ed =

c,=, Bk N

c,=, M

B,

,where Bk is the bandwidth required for transmitting flow

k. It pays to say that a flow forwarded by a node means that this node has an incoming flow and an outgoing flow. Let B2m = C- Bu2d- max Bu2d and

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(x)

~2= C - B ~ X- C B ~(ifBzn Y < 0 , let Bzn= 0). used

used

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2.2. ABAPMN

The ABAF'MN of node X is defined as follows:

1

P', Px = P," .P', P,"

, X is the source node. , X is the intermediate node.

(3)

, X is the destination node.

Where PX is the probability for node X to process the RREQ message (Px~[O,l]),P', andP' are the send probability and the receive probability as

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presented in the following equations, in which B, is bandwidth requirement carried in the RREQ message.

2.3. Modifications made for BECLPR based on AODV In original AODV, nodes don't consider their available bandwidth resources but forward the first received RREQ message definitely, while in BECLPR nodes process the RREQ message probabilistically according to their available receiveisend bandwidth range. Each node uses the probability computed according to Eq. (3) to process the RREQ message in the route discovery procedure based on AODV. Table 1 describes the basic operation behavior. Table 1. The basic operation behavior in the route discovery procedure of BECLPR. Node

1

Steps Compute the probability Ps according to Eq. (3), do the following with the I probability Ps: Source node (node S) I Generate a RREQ message; Piggyback the output queue length of S into the fields: NqI, and Pql,; , Broadcast this message. Compute the probability Px, do the following with the probability Px: Intermediatenode If the output queue length is bigger than the value of the Nqlenfield, (node X) replace the latter with the value of the output queue length of X; Add the value of the output queue length to Pqlenfield; ........... Establish the reverse route and rebroadcast ". this first received RREQ. j If it has not sent any RREP for the request with the same source-destination Destination node pair, do the following with Po: (node D) If NqlenI a*Qlmit(ais a constant coefficient), establish the reverse route and send RREP back to the source node S; j Otherwise, add a record in the route list maintained in node D to record i the correspondingroute information. i After a given interval of time, if no reply has been sent, the record with / least Pqlsnis selected from the route list and used to set up the reverse route I and send RREP to the source node.

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The output queue length of the node(s) on a path is also considered in BECLPR. Two additional fields, called Nqlenand Pqlen,are added into the RREQ message. Nqlenis used to carry the maximum value of output queue length of the nodes on the path. Pqlenis to piggyback the sum of the output queue length of

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each node on the path. Qlimitstands for the limit of the output queue length. The output-queue related operations are shown in Table 1. If the destination node receives a RREQ message with Nqlenno bigger than a*Qamit,it doesn’t need to wait for the arrival of other RREQ messages but replies to the source immediately. Otherwise, the Pqlenfield is used to help BECLPR to get a better path, which usually occurs in the heavy network load condition. Even if the intermediate node has a route to the destination, it doesn’t reply to the source.

3. Performance evaluation We used ns-2 [8] to simulate and evaluate the performance of BECLPR. We are interested in the packet delivery ratio, end-to-end delay and routing overhead. The evaluations are based on the simulation of 40 mobile nodes moving about in an area of 800 X 800 sq. meters for 100 seconds of simulated time. The nodes move randomly according to the “random waypoint” model, in which the maximum moving speed of the node is 1 d s . The average pause time of nodes between two movements is 20s. The channel capacity is 2Mbits/s. Constant bit rate (CBR) is adopted as the data traffic. Packet size is 512 bytes. The sending rate of each CBR source is randomly set between 5pkts/s and 20pkts/s. The coefficient a in Table 1 is 0.8. A slight modification of IEEE 802.1 1 MAC DCF is adopted as the MAC protocol. Due to the MAC packets, routing packets and other overhead, the net channel capacity for transmitting data is far smaller than 2Mbits/s. Through simulations we found the maximum capacity for transmitting data is approximately 550kbitds and used this value for C in Eqs. (1) and (2). Figure 2 compares BECLPR with AODV under different load conditions in terms of the packet delivery ratio. BECLPR has a much higher packet delivery ratio than AODV. As the number of CBR sources increases, the difference between them becomes apparent. Since original AODV does not,consider the available bandwidth of the node, the selected nodes may be congested while other nodes are very light loaded, thus many packets are droRped by the heavily loaded nodes. BECLPR probabilistically distributes the data flows to the nodes according their available sendreceive bandwidth, so more packets are delivered. The considering of the output queue length also avails to improve the performance of packet delivery ratio. Although BECLPR delivers more packets than AODV, it still outperforms AODV in terms of end-to-end delay when the network load is not too high (here the number of sources is smaller than 14.), shown in Figure 3. When the network load is so high that the load on each discovered path is also very heavy, the delay of BECLPR becomes higher than that of AODV.

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a 0.5 0.4

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(a) Normalized routing load

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(b) The comparison between the number of RREQ and RREP messages of BECLPR and that of AODV

Figure 4. The comparison between BECLPR and AODV in terms of the routing overhead.

Figure 4 (a) shows the routing overhead in normalized routing load. Normalized routing load is the ratio of the number of routing messages propagated by every node in the network and the number of data packets received by the destination nodes. As the number of the sources increases, the routing overhead of BECLPR retains low, while that of AODV is increasing.

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This is due to two main causes. First, the number of the RRFiQ and RREP messages propagated in BECLPR is far less that of AODV, shown in Figure 4 (b). As the network load increases, the available sendreceive bandwidth of the node decreases, which causes some RREQ messages dropped by the nodes running BECLPR with a given probability. The number of the RREP messages of BECLPR also decreases, since only the destination responds to the source node. Second, the destination nodes under BECLPR receive more packets than those running AODV, since BECLPR has a higher delivery ratio than AODV. 4. Conclusions

A Bandwidth-Efficient Cross Layer Probability Routing for MANETs (BECLPR) is proposed in this paper, BECLPR differentiates between the send and receive bandwidth, which are computed by the MAC protocol. Based on the proposed probability model ABAPMN, each node processes the RREQ message with a given probability. BECLPR is able to probabilistically distribute the loads among the nodes in the network according to the available sendreceive bandwidth. Also the node’s output queue length is considered in the route discovery process. Simulation results show that BECLPR outperforms AODV in terms of the packet delivery ratio, end-to-end delay and routing overhead. References 1 . http://www,ietf.org/html,charters/manet-charter.html. IETF mobile ad hoc networks (MANET) working group charter. 2. David B. Johnson, David A. Maltz, and Yh-Chun Hu. The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR). JNTERNET-DRAFT , Internet Engineering Task Force, July 2004. 3. C.E. Perkins and P. Bhagwat. Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers. Comp. Comm. Rev, Oct. 1994, pp.234-244. 4. C. Perkins and E. Belding-Royer. Ad hoc On-Demand Distance Vector (AODV) Routing. RFC 3561, Internet Engineering Task Force, July 2003. 5. T. Clausen and P. Jacquet. Optimized Link State Routing Protocol (OLSR). RFC3626, Internet Engineering Task Force, October 2003. 6. H. Hassanein and A. Zhou. Routing with Load Balancing in Wireless Ad Hoc Networks. Proc. ACM MSWIM, Rome, Itary, July 2001. 7. K. Wu and J. Harms. Load-Sensitive Routing for Mobile Ad Hoc Networks. Proc. IEEE ICCCN’Ol, Scottsdale, AZ, Oct. 2001. 8. http://www,isi.edu/nsnam/ns/doc/ns-doc.pdf. December 13,2003.

Ad Hoc(I)


EFFICIENT BANDWIDTH ALLOCATION FOR BASIC BROADCAST AND POINT-TO-POINT SERVICES IN THE ADHOC MAC PROTOCOL " J.R. GALLEGO', L. CAMP ELL^, M. CESANA~,A. CAP ONE^, F. BORGONOVO~, A. HERNhJDELSOLANA', M. CANALES', A. VALDOVINOS 'Departamentode Ingenieria Electrbnica y Comunicaciones,University of Zaragoza, Maria de Luna I , 50018 Zaragoza, Spain,

'Dipartimento Elettronica e Informazione, Politecnico di Milano, Piazza L. Da Vinci 32, 20133 Milano, Italy An effective Medium Access Control for communications in wireless Ad hoc networks should be able to support both broadcast and point-to-point communications paradigms. The ADHOC MAC protocol, recently proposed within the European Commission funded CarTALK2000 project, seems to match these requirements. As a matter of fact, it allows the exchange of connectivity information among wireless terminals which can be usefully exploited to devise both broadcast and point-to-point services. In this paper we evaluate through simulation the efficiency of the protocol in a mixed traffic scenario where broadcast and point-to-point communications coexist. An adaptive bandwidth allocation strategy is proposed to share the resources between both services in a dynamic situation. The capability of the protocol to establish parallel point-to-point data communications and the corresponding improvement in the point-to-point efficiency is also evaluated

1. Introduction

The transmission media in wireless environment has to be shared by definition. Further on, the radio resources are often limited in comparison with the number of users which access them, thus the capacity of any wireless network is highly determined by the capability of the medium access control mechanism to handle the access process and to achieve high resource reuse [ 11. ADHOC MAC [2] is a medium access control protocol recently introduced within the European Commission funded CarTalk2000 project [3] for providing connectivity in ad hoc inter-vehicles networks [4]. ADHOC MAC works on a slot synchronous physical layer and implements a completely distributed access technique capable of dynamically establishing a reliable single-hop Basic This work is supported by projects fiom CICYT and FEDER, TEC2004-04529/rCM.

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broadcast CHannel (BCH) for each active terminal, i.e., each transmission within a BCH is correctly received by all the terminals within the transmission range of the transmitter. Each BCH carries signalling information that provides a prompt and reliable distribution of layer-two connectivity information to all the terminals. This information provides a valuable basis for the efficient implementation of point-to-point data services, exploiting parallel transmissions, and also supplies a prompt means to manage different QoS requirements for these services, through the use of priorities. In [5] and [6] we have studied the performance of ADHOC MAC broadcast services in a static scenario and with users’ mobility respectively. In this paper we evaluate through simulation the efficiency of the protocol in a mixed traffic scenario where broadcast and point-to-point communications coexist. An adaptive bandwidth allocation strategy is proposed to share the resources between both services in a dynamic situation. The goal of the proposal is to guarantee access requirements for BCH whereas capacity for extra data communications is optimized. The capability of the protocol to establish parallel point-to-point data communications and the corresponding improvement in the point-to-point efficiency is also evaluated. The remaining paper is organized as follows. In Section 2 we briefly summarize the basis of the ADHOC MAC protocol and the proposed bandwidth allocation strategies for basic broadcast and point-to-point services. In Section 3, both the resource sharing strategies and the point-to-point service efficiency are evaluated through simulation. Finally, in Section 4 some conclusions are provided. 2. The ADHOC MAC Protocol 2.1. Basic Operation Modefor BCH and point-to-point Communications

ADHOC MAC operates with a time slotted structure, where slots are grouped into virtual frames (VF) of length N, and no frame alignment is needed. In the BCH, each terminal broadcasts information on the status of the channel it perceives. The BCH contains a control field, namely, Frame Information (FI) field, which is an N-elements vector specifying the status of the N slots preceding the transmission of the terminal itself. The slot status can be either BUSY or FREE: it is BUSY if a packet has been correctly received or transmitted by the terminal, otherwise it is FREE. In the case of a BUSY slot the FI also contains the identity of the transmitting terminal.

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Figure 1. Example of the FI information propagated by the terminals 1-7 in the one-hop clusters A, B, and C represented by ellipses.

Based on received FIs, each terminal marks a slot, say slot k, either as RESERVED, if slot k-N is coded as BUSY in one FIs received in the slots from k-N to k-1 at least, or as AVAILABLE, otherwise. If a slot is AVAILABLE, it can be used for new access attempts. Upon accessing an AVAILABLE slot, terminal j will recognize after N slots (a frame) its transmission either successful, if the slot is coded as "BUSY by terminal j" in all the received FIs, or failed, otherwise. In figure 1, an example of FIs transmitted by a set of terminals is given. The union of all one-hop (OH) clusters with a common subset is denoted as two-hop (TH) cluster. The terminals belonging to the same OH-cluster see the same status (AVAILABLE or RESERVED) for all the slots; terminals belonging to different OH-clusters of the same TH-cluster mark as RESERVED all the slots used in the TH-cluster, whereas terminals belonging to disjoint OH-clusters usually see a different channel status. As a result, slots can be reused in disjoint OH-clusters, but can not be reused in the same THcluster and, therefore, the hidden-terminal problem can not occur [4]. The BCH provides a reliable single hop broadcast channel which can be used both for signaling and for data traffic purposes. Upon this basis, point-to-point data communications among terminals can be effectively established by exploiting the distributed signaling provided by the FIs. To this end, each entry of the FI encloses a PointToPoint (PlT) flag, which is handled as follows: A terminal sets the PTP flag of a given slot in the FI, if the packet received in the slot is a broadcast one or if it is destined to the terminal itself.

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Figure 2. Examples of parallel transmissions. Transmission fiom terminal I is established first. Allowed transmissions by terminal 2 are indicated by solid arrows.

In order to set up a point-to-point communication, all the AVAILABLE slots can be used. Further on, even some RESERVED slots can be used according to the following rule: A RESERVED slot can be accessed if: 1.The PTP flag is signaled off in all the received FIs and

2. The FI received from the destination terminal signals the slot as FREE.

The conditions above allow point-to-point transmissions to share the same slot when there is no collision at the receivers. This can be seen referring to the four cases shown in figure 2. The cases a and b in the figure consider two transmitting terminals, say 1 and 2, belonging to different not disjoint clusters. Assuming that terminal 1 has already activated a PTP channel with 3, terminal 2 can transmit using the same slot if these two conditions above are satisfied. In case a, terminal 2 can use the same slot as terminal 1 even if it is signalled as RESERVED. In fact, the only PTP flag ON is that in the FI transmitted by terminal 3 and not received by terminal 2 (satisfylng condition (l)),and the FI generated by terminal 4 marks the slot as FREE (satisfjmg condition (2)). In case b the FI, generated by terminal 3 and received by terminal 2, prevents terminal 2 from transmitting (not satisfylng condition (1)). In this case parallel transmission would, in fact, interfere at terminal 3. In cases c and d the two transmitting terminals belong to the same cluster. In case d terminal 3 can use a RESERVED slot since both conditions (1) and (2) are satisfied (in fact, this is the exposed-terminal case) whereas in case c condition (2) is not satisfied and a collision would occur at terminal 4. If several access attempts occur concurrently, collisions can still occur. Then, the transmitting terminal has to perform a further check according to the following rule:

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The point-to-point transmission is successful if the slot is coded as BUSY in the FI of the destination terminal; otherwise the transmission is failed. 2.2. Bandwidth allocation strafegies

Once a terminal has acquired its BCH channel, it can establish additional broadcast data communications if the data payload in the BCH is not Iong enough. In the same way, different PTP data communications with all its neighbours can be established. In this paper, only extra PTP communications are considered, so in the remaining of the paper these additional communications are referred as PTP. However, the proposed strategy can be generalized since dimensioning is made only according to BCH demands. In its basic operation mode, every slot in the frame can be used for both PTP and BCH transmissions. In this situation, as the number of PTP communications grows, the number of AVAILABLE slots for new terminals accessing the system decreases, leading to a reduction in the number of terminals that can access the system for a given number of slots. As the acquisition of a basic broadcast channel is mandatory to access the system, an appropriate dimensioning of the network must guarantee certain resources for BCH transmissions. As a metric for the BCH performance, we take into account the outage probability. A terminal is declared in outage if it does not acquire a BCH within a period of a given number of frames after birth. According to this situation, it must be guaranteed a trade-off between ensuring an acceptable outage probability for BCH channels while providing the maximum throughput for PTP data communications. In order to guarantee an outage probability for new terminals accessing the system, we propose a frame subdivision into two subframes, where the performance of BCH is not limited by the amount of PTP traffic in the network: A frame with N slots is divided into NBCHand N p r ~slots for BCH and PTP communications. (1) N = NBCH+ NPTP For this assumption, it is required a slot and frame time synchronization of each terminal in the network, that can be obtained with the Global Position System (GPS) or other solutions [7], [S]. With this subdivision, the probability of access the system is higher. When a terminal tries to access the system, it looks for an AVAILABLE slot. The existence of an AVAILABLE slot for a new terminal can only be statistically guaranteed: if the neighbours have enough FREE slots, it is probable that there is a common FREE slot for all of them. The frame subdivision brings together the FREE BCH slots making more probable for a terminal to find an AVAILABLE slot.

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If a static frame subdivision is considered, NBCH limits the maximum density of terminals supported by the system. A lower density of terminals implies that resources are wasted, since extra FTP communications could be allocated in the free BCH slots. On the other hand, when the density grows over expected, terminals declared in outage could access the system using slots of the FTP subframe. To overcome these limitations, an adaptive subdivision strategy that moves the border between the slots dedicated to each type of traffic within the frame according to the channel dynamics is proposed and evaluated: A set of W possible values for NBCH [Ni < N2 10 is satisfied, then all the nodes which satisfy to this condition can form a virtual group. 4.1.2. Mobility: Ad hoc networks are characterized by the mobility of nodes, in which one group can contain several nodes moving with different speeds. Our goal is to form a stable group; therefore we set a given S threshold to differentiate mobile nodes having high speed from others with medium or low speed. We gather nodes moving with high speed in one virtual group, and the same task with other nodes moving with medium or slow speed. In basic ad hoc networks nodes can exchange [RTS, CTS, DATA, and ACK] messages, via a complete virtual graph, in order to guarantee group self-stability realized by the homogeneous mobility of nodes having practically the same power of signal. Furthermore, this method ensures a reliable communication between wireless mobile nodes. 4.2. Phase two: leader election

In this second and last phase, the goal is the election of a node to be the leader of the formed group. Initially, every node possesses appropriate values; these values are in terms of energy, of signal power and mobility; within a defined group, nodes exchange messages to elect a node as the leader. The election algorithm is based on the work presented in [13] for the broadcasting of messages in wireless ad hoc networks, in which nodes do not consume a large quantity of energy during the control messages exchange. Indeed, to introduce

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the election, start nodes broadcast a request message called Req(Prop) to the other nodes of the formed group, where ‘LPr~p77 represents energy, signal power and mobility. When other nodes receive the Req(Prop) message, they compare their own “Prop” with those received from start nodes. The aim of these comparisons is to estimate the most powerful node in order to elect a leader within a defined group. Once the leader is designated, every node sends a confirmation message called Conf() to the leader, and another message to inform the other nodes. G, : is a virtual group formed by nodes having checked the two conditions. G,= {nl,n2, n3... ni. .. nj.. . nk} Algorithm: For any node nk from G , : We consider a group Si :{ in order to establish contacts between nodes in G , } V ni, c G , : ni Sends and Receives (RTS/CTS/DATA/ACK]i to and from Si in order to establish contacts

ni Sends Req (Prop)i To Si when Si nodes receive ni message

Begin

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all nodes send a ConfO message to the leader end End

5. Simulations In this section, we report the results of simulations that we have performed to study the performance of the proposed algorithm, for a better comprehension of how to elaborate the leader election algorithm for ad hoc networks. The performance metrics that we consider in our simulations are bandwidth loss, energy consumption, and the throughput. 5.1. Simulation environment

With ns2 [14], the simulated network consisted of 50 nodes uniformly distributed over a zone size of 1000 square meters. For all network sizes, nodes

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move according to the Random Waypoint mobility model [6]. We set the node speed to 1 lOm/s. Each node was equipped with a radio transceiver, assuming Free-Space Propagation path-loss model. We used the IEEE802.11 like the MAC layer protocol. In our simulations, a leader node transmitted a heartbeat message to all other nodes. We set the value of heartbeat message interval to 30 seconds, and the maximum time of heartbeat message loss to 5 seconds. Nevertheless, a start node initiated a new leader election if it had not received a beacon message from the leader for 90 seconds. It should be noted that the duration of messages were chosen to accommodate the random waypoint mobility model, and can be set according to application requirements.

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6. Results and Discussion 6.1. Bandwidth In wireless ad hoc communications, periodic transmission of control messages consumes a part of the available bandwidth. To estimate bandwidth loss, we set up a simple scenario, in which several nodes of the group send Req(Prop) and Conf() messages with constant rate, in order to saturate the bandwidth.

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The figure 1 represents the loss of bandwidth caused by MAC layer messages added to request and confirm election messages [Req(Prop), Conf()], during communication between a node and group members. Our scenario allows to measure the impact of the size of Req(Prop) and Conf() messages, on the

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bandwidth consumption. In figure 1, we can observe that the bandwidth degradation percnetage is equal to 0.11% for 100 exchanged messages. This fall is due to the medium control messages added to our election messages. Generally, a quantity of bandwidth is saturated by the medium control messages. In our case, the leader election messages exchanges consume a slight part of available bandwidth, which makes our method more advantageous.

6.2. Energy Among the most important problems of mobile ad hoc networks, there is energy consumption, because the life span of mobile nodes depends on it. More exactly, as each node is autonomous and has a limited energy, it is necessary to avoid excessive energy consumption. It is even necessary to use as little energy as possible during the election, so as to leave the biggest part for the applications that will follow. Within this frame, we supposed for our simulations a scenario to calculate energy consumption during four independent stages. 90 80

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Figure 2 depicts four curves, the first and the second curves (from the horizontal axis) represent a broadcast and a multicast of Req(Prop) and Conf() messages, between group members. We notice that energy consumption is important in the first, because message exchanges were done by a Broadcast and the consumed energy was equal to 26% of available energy.

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The second curve represents a multicast of [Req(Prop) and Conf()] messages. We observe that the energy level decreases rapidly which means that the consumption of energy is 39%. The third curve illustrates the consumed energy during the leader election, by exchange of Req(Prop) and Conf() messages using the method presented in [13]. We notice that the consumed energy (20%) is clearly weaker than the energy consumed by broadcast or multicast. We prove by this, that the operation of the election consumes a little quantity of energy. On the last graph (the highest one), we observe that after leader election operation, energy consumption decreases more (9%), because nodes do not exchange control messages, insofar as the leader manages its group. 6.3. Throughput The reason for which ad hoc network nodes cannot have a perfect and durable knowledge of network topology, is that any node in such a network can move, appear, or disappear all the time. This constraint makes the conception of a routing or signalling protocol very complex.

Figure 3. Network capacity

Our aim is to set up a feasible method within a mobile ad hoc group, to provide group stability that would be able to support QoS applications. In this context, we proposed a simulation scenario to calculate the rate of UDP sent packets, in which we performed sending parquets of 1 and 10 Mbps from the leader toward group nodes, and observed the network behaviour during packets transfer. Figure

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3 shows simulation results and that performance degradation in terms of delivered packets is very slight. In fact, we can explain this degradation by the nodes mobility that entails packets loss and IEEE802.11 MAC layer also generates interferences. The obtained results prove that group self-organizing by leader election, is able to insure QoS. 7. Conclusion and perspectives

In this paper, we have presented a new asynchronous, distributed leader election algorithm for mobile ad hoc networks, based on new metrics which represent the node’s properties. To obtain homogeneous groups of nodes, the proposed algorithm is based on two phases. In the first phase, we form a group in which nodes must check two conditions: signal power for communications and mobility for group stability. In the second phase start nodes trigger the election to determine the most valued node within the defined group. Finally, we have simulated the algorithm and have provided useful insight, based on our experiences in designing leader election algorithm. Although in this paper we described our algorithm as an extrema finding one, it can be used in scenarios where a unique or several leaders are desired. Currently, we are investigating on a new routing protocol using leader election metrics. References 1. Sudarshan Vasudevan, Jim Kurose, Don Towsley, “Design and Analysis of a Leader Election Algorithm for Mobile Ad Hoc Networks” ICNP Proceedings of the Network Protocols, 12th IEEE International Conference on (ICNP‘04) - Pages: 350-360.2004. 2. W.B. Dunbar, E. Klavins, and S. Waydo. “Feedback controlled software systems”. CDS technical report 2003- 002, California Instit. of Tech, 2003. 3. T.Arici,B.Gedik,Y.Altunbasak, and L. Liu, “PINCO: a Pipelined InNetwork Compression Scheme for Data Collection in Wireless Sensor Networks,” in Proceedings of IEEE International Conference on Computer Communications and Networks, 2003. 4. B. Wang and S. K. S. Gupta, “On maximizing lifetime of multicast trees in wireless ad hoc networks “in International Conference On Parallel Processing (ICPP-03), Kaohsiung, Taiwan, China, Oct. 2003. 5. J. Yoon, M. Liu and B. Noble. “Random Waypoint” Considered Harmful In Proc. of IEEE INFOCOM, 2003.

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6. Yu Chen, Jennifer L. Welch “Self-stabilizing mutual exclusion using tokens in mobile ad hoc networks”. Workshop on Discrete Algorithms and Methods for MOBILE Computing and Communications 2002. 7. Robert E. Schapire“The boosting approach to machine learning: An overview”.In MSRI Workshop On Nonlinear Estimation & Classification, 2002. 8. N. Malpani, N. Vaidya and J. Welch. “Distributed Token Circulation in Mobile Ad Hoc Networks”. In Proc. 9th International Conference on Network Protocols (ICNP), November 200 1. 9. A. Arora, M. Demirbas, and S. S. Kulkarni. “Graybox stabilization”. Proceedings of the International Conference on Dependable Systems and Networks, pages 389-398, July 2001. 10. R E. Lee and Y. Xiong, “System-level types for component-based design” in Lecture Notes in Computer Science, Embedded Software, vol. 221 1. Heidelberg, Germany, 2001, pp. 237-253. 11. D. Coore, R. Nagpal and R. Weiss. “Paradigms for Structure in an Amorphous Computer. Communications of the ACM archive. 2000. 12. J.E. Wieselthier, G.D. Nguyen, A. Ephremides, “On the Construction of Energy Efficient Broadcast and Multicast Trees in Wireless Networks,” in Proceedings IEEE INFOCOM 2000, pp. 586-594. 13. L. Breslau et.al. “Advances in network simulation” IEEE Computer, 5-2000. 14. A.Arora and M.Gouda.“Distributed Reset”.ln IEEE Transactions on Computers, 1994. 15. Navneet Malpani, Jennifer L. Welch, Nitin Vaidya “ Leader election algorithms for mobile ad hoc networks”. Workshop on Discrete Algorithms and Methods for MOBILE Computing and Communications. Proceedings of the 4th international workshop on Discrete algorithms and methods for mobile computing and communications Boston, Massachusetts, United States, 96 - 103. 2000.

HDSR: HIERARCHICAL DYNAMIC SOURCE ROUTING FOR WIRELESS HETEROGENEOUS MOBILE AD HOC NETWORKS *

MOHAMMAD NASERIAN, KEMAL E. TEPE, AND TARIQUE MOHAMMED University of Windsor, Department of Electrical and Computer Engineering, Windsor, Ontario, Canada E-mail: { naseria, ktepe, tarique} Quwindsor. ca

In this paper, hierarchical dynamic source routing (HDSR) protocol is introduced for heterogeneous mobile ad hoc networks architecture. In this network architecture, there are two tiers: Forwarding nodes (FN) and mobile nodes (MN). FNs route the packets and MNs host the applications. Dynamic source routing (DSR) protocol is modified and optimized for mobile multi-tiered ad hoc network architecture. The new routing protocol, HDSR, separates route discovery (route requests and replies), and packet forwarding functionalities. Those two functionalities are distributed into two node types, where only route requests and replies are functional in MNs when they are source or destination, and both route discovery and packet forwarding are functional in FNs. HDSR combined with hierarchical architecture has lower routing overhead and shorter route discovery delays, because in HDSR there are smaller number of route reply and route request messages than in regular DSR. HDSR is implemented by network simulator (Network Simulator-2 of University of California). It was shown via computer simulations that HDSR improves average end-to-end delay per packet, network throughput and packet delivery ratio in wireless ad hoc networks compare to regular DSR.

1. Introduction Mobile ad hoc networking is becoming increasingly popular as a mean of providing instant networking to groups that may be within the transmission range of one another. These networks are self-initializing, self-configuring and self-maintaining, all of which can be coined with term “self-organizing”. Routing is an essential part of network protocols to provide self-organizing ‘This work is supported by Natural Science and Engineering Research Council of Canada (NSERC).

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capability, and it is the most widely studied element for ad hoc networks. Numerous routing protocols have been developed or adopted from Internet routing protocols for ad hoc networks. Broadly, those protocols can be classified as: (1) Proactive routing protocols, and (2) On demand (Reactive) routing protocols. In proactive routing, routing information is periodically exchanged among network nodes, like Dynamic Sequence Distance Vector (DSDV) Because of those periodic updates and information exchanges, the network consumes large portion of the useful bandwidth for routing control overhead (i.e., route maintenance and update packets). That is why proactive protocols do not scale well. On the other hand, in on demand routing, routes are discovered when they are needed. Such provision eliminates periodic routing updates, hence allows protocols to operate more efficiently (i.e. less routing overhead) than proactive routing protocols. That is why most of the recently proposed routing protocols for ad hoc networks fall under the on demand category, like Ad Hoc On Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) 3 . Although on demand routing protocols offer lower average routing overhead than proactive routing protocols, there are still two problems that need to be solved. The first one is the large routing overhead during route discovery phase, and the second one is the large end to end packet delay. There are proposals to improve routing overhead and scalability. One of those proposals is clustering and cluster based routing Clustering (active or passive) can be described as grouping nodes into clusters. A representative of each group can be named as cluster head and other members are called cluster members. There are proposals to provide efficient formation of clusters, selection of the cluster heads and its member nodes 13. To form clusters and maintain the clusters, network nodes need to cooperate and exchange information with each other, which can increases the control overhead packets. Passive clustering l4 has recently been proposed to exploit ongoing traffic to propagate cluster related information. Although it requires less overhead packets to form and maintain clustering, it still requires some partial information about the neighbors. Other proposals that provide improvement and scalability to routing protocol suggest systematically reducing the number of messages generated and transmitted during the flooding. Those schemes can be loosely classified as probabilistic schemes and location based schemes 'O1l2. The major problems of probabilistic schemes is that the probability at which a node should rebroadcast is not universal, but specific to each topology. There is no analytical formula to obtain that probability. Local topology 5*13114.

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information is used to avoid unnecessary rebroadcasts in location based schemes. In Ref. 11, 12, self-pruning and neighbor-coverage schemes were proposed, in which a node does not rebroadcast if the packet is delivered to all neighbors of this node by a prior broadcast. Our approach to improve the performance of reactive routing, particularly in DSR, is to introduce hierarchy. The new protocol derived from DSR, called Hierarchical Dynamic Source Routing (HDSR), limits the number of nodes that participate in the route discovery of the protocol, which in turn reduces overhead and delay compare to DSR. That architectural change provides HDSR to reduce the routing overhead significantly because number of nodes that involve in the route discovery is smaller and they can find and return routes faster to the source. That can reduce the end to end delay too. In addition to those, MNs do not need to acquire and maintain any statistical information about the neighbors or do not need to send maintenance messages or location information about the neighbors. Such reductions could significantly save bandwidth of the network, hence improve the throughput of the network. Rest of the paper is organized as follows. In Section 2, we will explain HDSR in details, which will provide its differences from DSR. Later in Sections 3 we will provide simulation model and results. Finally Section 4 summarizes our conclusions.

2. Hierarchical Dynamic Source Routing Protocol

In HDSR protocol, we classify the participating nodes of the network as Mobile Node (MN) and Forwarding Node(FN). We assign different functionalities to those nodes depending on what type of node they are. MNs initiate route discovery. FNs help them to find source route to the destination MN. The destination MN replies back through the FNs to source MN. Once source MN discovers the routes, it starts sending packets to the destination. FNs assist the MN to forward packets to destination MN. Route discovery and route maintenance in HDSR are different from those in DSR. When a source MN originates packet to a destination MN. If the source cannot find a source route in its route cache, it initiates a route discovery by transmitting a ‘route request packet’ as a local broadcast packet. Only FNs, which are within the range of the source MN receives the broadcast packet. Other MNs, which are also within the range of source MN and which are not the destination of this packet, discard the broadcast message and do not broadcast further. Only the FNs re-broadcast the request to other FNs unless the destination MN receives this route request packet.

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The destination MN then replies back to the source MN through the FNs. After receiving the route reply, the source MN record the source route in its cache and starts sending packets to the destination MN using the source route it has just discovered. Route maintenance is performed by FNs only. When a F N detects that the next link from itself to the next MN or FN is broken, it updates its own route caches by marking all the paths which use the broken link as invalid and sends route error message to the source MN and all other F N which uses the broken link for packet transmission. We will now explain how HDSR reduces overhead packet during the route discovery processes and prevent route request and route reply flooding. Figure 1shows how a route is discovered in HDSR. In this scenario nodes 1, 2, 3, 5 and 6 are MNs and nodes 4 and 7 are FNs. Route discovery is initiated by MN-1 t o find a source route to destination MN-8. MN-1 transmits the route request packet as a local broadcast message. MN-2, MN-3 and FN-4 are within the range of MN-1. MN-2 and MN-3 are restricted not to re-broadcast the route request further. They are not forwarding node and they are not the destination as well. Only FN-4 will rebroadcast the request packet after adding itself in the request packet. FN-7 will only accept the route request packet only because it is the only FN within the range of FN-4. FN-7 rebroadcasts the request packet and the route request packet finally reaches the destination MN-8. MN-8 replies back to source node. Upon receiving the reply packet, source MN-1 record its route cache and starts sending packet through the source route it has just learned from the reply packet. In this case only three broadcast messages are generated. Redundant route request broadcasting by MNs except the source MN have been eliminated in HDSR which saves bandwidth by reducing packet colli-

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sion. Figure 1 illustrates how route reply flooding is prevented in HDSR. In this case there is only one FN and all other nodes are MNs. Route discovery was initiated by MN-1 to find a source route to the destination MN-7. MNs 2, 4, 5, 6 and FN-3 are within the range of MN-1. Assume each MN and FN has a source route in its cache. In HDSR protocol, only FN-3 will reply back to MN-1 in contrary to replying procedure used in DSR where all the MNs reply back to MN-1. All other MNs which received the route request message discard it. MN-1 starts sending packet to destination MN using the route 1-3-7. Thus route reply flooding is limited in HDSR when each node replies from its route cache.

3. Simulation Model and Results

We used Network Simulator 2 (NS-2) to implement and test the performance of the new proposed protocol. In NS-2, the effective transmission range of wireless radio is 250 meters and the medium access control (MAC) protocol is based on IEEE 802.11 with 2 Megabits per second raw capacity. Traffic sources are Constant Bit Rate (CBR) with 512 bytes per packet. The mobility model uses the random waypoint model in a rectangular field. In this model each node starts its journey from a random location to a random destination with a randomly chosen speed, which is uniformly distributed between 0-20 mls. When the mobile node reaches its destination it stays at that location for the period of a pause time, p seconds, and then it chooses another random destination and moves toward this new destination with a new randomly chosen speed. We vary the pause time p , which affects the mobility scenarios. Each CBR source starts randomly in the first ten seconds of the beginning of the simulation and simulation runs for 600 seconds. In order to increase reliability of the simulations, each connection scenario is simulated 10 different times with new topologies and different mobility scenarios. The reported results are average of these 10 simulations. In order to obtain these new simulation environment, we modified NS-2 source code to suit our needs, and at the same time implemented HDSR protocol and hierarchical architecture. Figures 3, 4,and 5 show performances of simulations of 80 MNs scenarios versus the pause time of mobile nodes. The size of the rectangular area that mobile nodes are located is 1000 x 1000 meters. There are 20 CBR sources with data packet rate of 2 packets per seconds. In HDSR, there are 12 FNs in additions to MNs and locations of the FNs are chosen randomly

130 as well. Figure 3 shows the routing overhead in HDSR and DSR. The over-

head in HDSR is consistently lower than DSR in all scenarios, and for this scenario it is approximately 50 times lower. We repeated these experiments with differing number of nodes, and we found that overhead improvement in HDSR is higher when the number of nodes in the network grows. The difference between HDSR and DSR overhead increases when the mobility is

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higher (i.e., shorter pause times). Due to the higher number of routing overhead packets, the network with DSR routing protocol has lower bandwidth for data packets, which we think adversely affects performance metrics in DSR compared with HDSR. For example, throughput of the network with HDSR is improved 3 times in high mobility and 20-30 percent in low mobility cases compared with that of DSR (Figure 4). In different scenarios, the throughput is always better with HDSR. The average end-to-end delay is also improved with HDSR. Figure 5 shows average end-to-end delay of scenarios with 80 mobile nodes. In that case, the delay in DSR is 3 times higher than that of HDSR for very high mobility (i.e., pause time less than 50 seconds) and few tens of times in low mobility cases. Delivery ratios of HDSR was better than DSR too. We also repeated our simulations choosing scenarios similar to Ref. 15. Figure 6 shows how HDSR saves overhead in a 50 Node scenario which results better throughput (Figure 7). Number of FNs in the network naturally affects the performance of HDSR. We varied the number of FNs in the 50 node network where the mobile nodes are moving with zero pause time. We observed that increasing the number of FNs in the network improves the throughput up to a certain point. After that point(9-11 FNs), increasing the number of FNs will increase the routing overhead and deteriorate the performance, which is depicted in Figure 8. That is why we think that distribution of FNs in the network is important for optimization of the performance figures, but this study does not focus on the optimization problem. 4. Conclusion

We presented a new routing protocol based on Dynamic Source Routing (DSR) for heterogeneous ad hoc networks. HDSR limits the role of nodes during the routing discovery phase of the protocol, consequently increases the routing efficiency. We have shown via computer simulations that the HDSR improves network performance figures, namely throughput, delay and packet delivery ratio significantly. Now FNs are randomly distributed but we are working on the efficient and adaptive FN selection mechanisms for HDSR in homogeneous networks as well as optimization of number of FNs in a given network scenario.

References 1. C.E. Perkins and P. Bhagwat, “Highly dynamic destination-sequence distancevector routing (DSDV) for mobile computers”, Proc. of ACM SIGCOMM 94, pp. 234-244,London, UK, August 1994.

132 2. C.E. Perkins, “Ad Hoc On Demand Distance Vector (AODV) routing”, IETF Internet-Draft, draft-ietf-manet-aodv-OO.txt, November 1997. 3. J. Broch, D. B. Johnson, and D. A. Maltz, “The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks”, IETF Internet-Draft, draft-ietf-manetdsr-OO.txt, March 1998. 4. K. Fall and K. Varadhan, “Ns Notes and Documentation Technical Report”, University of California Berkeley, LBL, USC/ISI, and Xeron PARC, 2003. 5. S. Ni, Y. Tseng, Y. Chen, and J. Sheu, “The Broadcast Storm Problem in a Mobile Ad Hoc Networks”, in Proc. MOBICOM 1999, pp. 151-162 Seattle, Washington, August 1999. 6. W. Peng and X. Lu, “On the reduction of broadcast redundancy in mobile ad hoc networks”, Proc. of the First Annual Workshop on Mobile Ad Hoc Networking and Computing, pp. 129-130, August 2000. 7. B. Krishnamachari , S.B. Wicker, and R. Bejar, “Phase transition phenomenon in wireless ad-hoc networks”, Proc. of GLOBECOM 2001, Vol. 5, pp. 29212925, San Antonio, Texas, November 2001. 8. Y. Sasson, D. Cavin and A. Schiper , “Probabilistic Broadcast for flooding in Wireless Mobile Ad Hoc Networks”, Swiss Federal Institute of Technology, Switzerland, Technical Report IC/2002/54. 9. Z.J. Haas, J.Y. Halpern, and L. Li, ‘‘Gossip Based ad hoc routing”, Proc. of INFOCOM 2002, Vol. 3, pp. 1707-1716, New York, June 23-27, 2002. 10. H. Lim and C. Kim, “Multicast tree construction and flooding in wireless ad hoc networks”, Proc. of 3rd ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems,Boston, Massachusetts, August 20, 2000. 11. Y. Tseng , S. Ni, E. Shih , “Adaptive Approaches to Relieving Redundant Storms in a Wireless Multihop Mobile Ad Hoc Networks”, Proc. of IEEE INFOCOM 2001 , April 22-26, Anchorage, Alaska. 12. H. Lim and C. Kim, “Flooding in wireless ad hoc networks”, Proc. ACM MSWIM workshop at MOBICOM, August 2000, Computer Communication J., Vol. 24, No. 3-4, February 2001. 13. C.R. Lin and M. Gerla , “Adaptive Clustering for Mobile Wireless Network”, IEEE Journal on Selected Area in Communication, Vol. 15, No. 7, pp. 12651275 September 1997. 14. Y. Yi, T. Kown and M. Gerla, “Passive Clustering (PC) in Ad Hoc Networks”, Internet Draft, draft-ietf-yi-manet-pac-OO.txt,November 2001. 15. C. E. Perkins, E. M. Royer, S. R. Das, and M. K. Marina, “Performance comparison of two on-demand routing protocols for ad hoc networks“, IEEE Personal Communications, Vol. 8, Issue 1, pp. 16-28, 2001.

Ad Hoc (11)


ANALYZING THE EFFECT OF COOPERATION APPROACHES

M. FRANK, M. HOLSCHBACH, P. MARTINI, M. PLAGGEMEIER INSTITUTE O F COMPUTER SCIENCE IV UNIVERSITY O F BONN, ROMERSTR. 164, 53117 BONN, GERMANY {MATTHEW, HOLSCHBA, MARTINI, MP}QCS.UNI-BONN.DE Mobile Ad Hoc Networks totally depend on cooperation. Each node is involved in forwarding data. Unfortunately, it is not obvious why a node should cooperate: forwarding data costs battery power and bandwidth. Research in this area is focused on approaches making stations behave more cooperatively. In contrast to this, the issue whether those approaches really improve the network performance is not well understood. In this work we present a general model for analyzing the effect of cooperation approaches in mobile ad hoc networks. To characterize the level of cooperation we use several classes of nodes, which forward data with class-specific probabilities. We show that cooperation approaches can improve the network performance in certain circumstances.

1. Introduction

Mobile ad hoc networks are becoming more and more popular. First, routing protocols like AODV or DSR were developed t o get ad hoc networks started. Security and trust were no big issues. However, this has changed. There are a lot of new threats in ad hoc networks. One new point of interest is cooperation. There is no doubt that selfish or malicious nodes may cause significant problems. Several approaches (e.g. which try to solve this problem were proposed. The question whether those approaches really help the network is rarely asked. To our best knowledge, the first work presenting a simple analysis was 5 . In our work, we assumed that each node decides whether to forward a data packet or not. The probability for each node forwarding the packet was assumed to be identical. We have shown that cooperation approaches can be useful for the network under certain circumstances, but: The more selfish nodes are part of the network, the less significant is the improvement by a cooperation approach. The model used in has one drawback: It allows some essential statements on cooperation approaches but does not distinguish between reliable and malicious nodes. 19233,497*8),

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Reliable nodes forward each data packet without delay whereas malicious nodes try t o harm the network and forward packets with a low probability. The most relevant question is: Which kind of nodes form the majority in an ad hoc network? Is the network basically good or a collection of malicious stations. We study several kinds of networks and answer the question how a cooperation approach effects a network. In this paper we present a general approach to analyze cooperation approaches. Our model neither depends on an underlying routing protocol nor on a specific cooperation approach. It is not important whether the cooperation approach is motivation based or detection based. This paper is structured as follows: In section 2, we give an overview of known cooperation approaches. In the next section, we describe how we model an ad hoc network to allow for an analysis. Thereafter, we present our general approach to analyze the effect of cooperation approaches. Section 6 shows some simulation results. Finally, we present conclusions and discuss further work.

2. Related Work

Current cooperation approaches may be classified either as motivation based or detection based. Motivation based Approaches: Most of the motivation based approaches use virtual currency (also called virtual nuggets or beans) to motivate the participants. One approach which can be used with a real currency has been introduced by Lamparter, Paul, and Westhoff 4 . It uses human minor motives and intensifies correct routing behavior in ad hoc networks by promising personal monetary benefits. An AAA-Service is used to secure transactions. To contact such an AAA-Service, the ad hoc network has to be connected to the Internet. In contrast to 4 , B u t t y h and Hubaux use virtual nuggets to motivate the participants t o forward packets. They use tamper resistant hardware to protect transactions. A game-theoretic approach was proposed by Zhong, Chen, and Yang '. Each node gets credit when forwarding packets, the sender is charged when sending packets. The authors provide a formal proof to show that their system is secure. Detection based Approaches: One of the first detection based approaches was the Pathrather/Watchdog model proposed by Marti, Giuli, Lai, and Baker '. It uses the promiscuous mode to detect whether a node forwards a packet or not. It tries to mitigate the effect of selfish nodes by using routes without those nodes. Buchegger and Boudec proposed a

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similar approach CONFIDANT uses a reputation system to rate selfishness of nodes. Selfish nodes can be excluded from routing. In 7, Michiardi and Molva present another reputation based system to increase the level of cooperation. It is based on a collaborative monitoring technique. The first group based approach was presented in (CineMA - Cooperation Enhancement in MANETs). A group of nodes observe the neighborhood and punish malicious nodes by reducing their throughput. CineMA uses the level of cooperation: The more selfish a node, the worse the punishment. 3. Modelling Ad Hoc Networks

In 5 , we only considered one kind of nodes: Each node had the same forwarding behavior in terms of the percentage of packets discarded. This assumption provided us with some essential results. However, this assumption must be expected to be far away from real life behavior. Modelling ad hoc networks is a big challenge. To analyze the effect of cooperation approaches we have to consider numerous classes of nodes. In our model we use classes of dropping behaviors. The more classes we consider, the more accurate the analysis. On the other hand: The more classes we have, the more complex the mathematical analysis. The model presented in this paper is not limited to a certain number of classes. Let us now describe the model in detail. Equation 1 may be used to calculate the probability that a single packet reaches its destination over a pre-established path with h intermediate nodes in absence of a cooperation stimulating approach: n

n is the number of different classes, ui is the percentage of the total nodes belonging to class i, lci (with Ici E [0,1]) is the percentage of packets forwarded by a node belonging to class i. In presence of a cooperation approach - which improves the forwarding behavior of a node - the probability rises to: n i=l

Ak, denotes the gain resulting from a cooperation approach. To quantify the benefit of a cooperation approach in terms of additional stations, a threshold must be used that reflects the percentage of packets which have

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to reach the destination. We call this threshold T . The absolute hop gain Ah, quantifies the number of additional intermediate stations that can be used without falling below this threshold. To get Ah,, equation 1 and equation 2 are equated to T , solved to h and finally substracted. We get:

4. The Accuracy of the Model

As mentioned above, the model presented in this paper is not limited to a certain number of classes. However, to simplify the illustration of the effects, we use three classes, only. In this paper, we refer to the classes as class 1, class 2 and class 3 to make clear that the classes do not reflect a certain kind of nodes. Only the dropping behavior of the class - the percentage of packets forwarded to the next station and the percentage of packets dropped - is important. Each class has a characteristic dropping behavior called Cooperation level. Class 1 forwards 90% of the packets, class 2 forwards 60% of the packets and class 3 only 20% of the packets. We used these values because they reflect the behavior from "forwarding most of the packets" to "dropping most of the packets". In this paper, the cooperation level of the network is denoted as F = (Class l/Class 2/Class 3). Equation 1 is used to calculate the probability that a single packet reaches its destination. This equation is based on an urn model with replacing. So, for each path of the ad hoc network, h - 1 balls of the urn are drawn and replaced. It could be argued that this model is inaccurate because an ad hoc network is modeled by an urn model without replacing - a node cannot be used twice on a route. But with the help of this model, it is much more easier to study the effects of a cooperation approach. This section discusses the accuracy of the model. We will show that the error by our model is very small for most of the networks. Figure 1 shows the comparison of our model and the urn model without replacement. The classification of the classes used in this example is C = (60/10/30) that is there are 60 nodes of class 1, 10 nodes of class 2 and 30 nodes of class 3. All possible probabilities are plotted in this figure, each mark represents the probability of a ball drawn. The y-axis shows the probability of our urn model with replacement, the x-axis shows the probability of the urn model without replacement. The nearer a point to the dashed line the smaller the differences between the two models. Obviously, the difference increases with the number of balls drawn. We

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also see that the deviation of the two models is negligible. It can be shown that the expected maximum deviation of our model is negligible as well. Assuming that the length of a route in an ad hoc network is between three and five hops, then the expected maximum deviation is between 0.0005 and 0.002 per draw. The more nodes are part of the network the smaller is the deviation, 100 nodes are sufficient to have such a small difference. 5 . Analyzing the Effects

We analyze two different effects of a cooperation approach. First, we analyze the effect of improving the forwarding behavior of a single class or all classes. Then, we analyze the effect of increasing the percentage of nodes of a single class only.

5.1. Networks without any Cooperation Approach Figure 2 shows the probability of a packet reaching its destination over five hops. Due to the fact that the sum of all classes must be 1 - the equation ai = 1 must hold - the fraction of one class depends on the fraction of the other two classes. Thus, the figure shown is a surface with three edges. The corners of the surface represent the maximum values. The lower left corner represents the case where all nodes belong to class 3, the upper corner represents the case where all nodes belong to class 2 and in the lower right corner all nodes belong to class 1. Moving in the direction of one axis means to increase the relevance of the corresponding class. For example, moving upwards the y-axis, leads to a higher percentage of class 2. The z-axis represents the probability of reaching the destination. The darker the surface, the lower is the probability of reaching the destination.

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As it is shown in this figure, the probability of a single packet reaching its destination is close to zero in networks consisting basically of nodes belonging to class 3. The more nodes belong to class 1, the higher is the probability of reaching the destination. Figure 2 shows that the surface is curved. That is, the influence of nodes belonging t o class 1 is higher than the influence of the other two classes. This results in the effect that small changes of the amount of class 1 can cause significant increase or decrease of the probability of reaching the destination. For most applications, it is important that many packets reach their destination. Assuming that this threshold is 60% - we believe that this is a pessimistic threshold: normally this threshold should be higher - the number of stations that can be used without falling under this threshold is small. For example, in networks with a classification of C = (90/7/3) the maximum number of hops that can be used without falling under the threshold of 60% is 4.33. In networks consisting basically only of nodes belonging to class 3 (e.g. C = (20/10/70)), only one hop (1.52) - that is a direct connection between source and destination - can be used. These values illustrate that a cooperation stimulating approach may be necessary to improve the cooperation. 5.2. Improving the Forwarding Behavior of one Class Which effect can be achieved when a cooperation approach improves the forwarding behavior of one class of nodes only? Figure 3 shows the effect when increasing the cooperation level of class 3 from 20% t o 60%. The cooperation levels of our classes are now F = (0.9/0.6/0.6). When compared to figure 2 the probability of a packet reaching its destination increases - we see that the surface is raised. The benefit of

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networks where cooperative nodes form the majority is rather small. Assuming the classification of a network is C = (90/7/3). In this case, the probability raises from 0.54 to 0.57. Networks consisting of selfish nodes have a much higher benefit. For example, the probability of a network with a classification of C = (20/10/80) raises from 0.03 to 0.27. However, the overall throughput of good networks is much higher than the throughput of networks consisting of "selfish" stations. The surface is not as curved as the surface of figure 2. This means that the influence of class 1is smaller, the impact of the other two classes is more significant than before. The area where 50% of the packets are discarded on their way to the destination is now wider and is moved to the left. That is, compared to figure 2, networks with more selfish or malicious nodes have a higher probability t o deliver successfully a packet to its destination. Increasing the cooperation level of one class also leads to longer usable routes. Figure 4 illustrates this fact using a threshold of 60%. This means, at least 60% of the packets sent to the destination are successfully delivered. We see that increasing the cooperation level of class 3 leads to a small increase of the route in networks where nodes belonging to class 1 and class 2 form the majority. The more nodes belong to class 3, the higher is the benefit. However, the maximum number of hops that can be used without falling under the threshold of 60% is higher in networks consisting basically of cooperative nodes. Only high values of Ak lead to longer paths in networks where nodes of class 3 form the majority. Conclusion: Improving the forwarding behavior of one class which contains a lot of nodes is worthwhile. This means, improving the behavior of class 1 in good networks is worthwhile, whereas improving class 1 in networks consisting basically of selfish nodes has a small benefit, only. On the other hand: Improving the forwarding behavior of selfish nodes is worthwhile in "selfish" networks. However, t o be as good as good networks, Ak must be very high.

5.3. Improving the Forwarding Behavior of all Classes

Figure 5 and figure 6 show the effect of increasing the forwarding behavior of all classes. In this example, the forwarding behavior of our three classes is F = (0.95/0.8/0.3) that is class 1 forwards 95% of the packets, class 2 forwards 80% and class 3 forwards 30% of the packets. Compared to figure 2, the surface is raised, which means, the probability of receiving a packet over five hops is higher than before. Even for networks only consisting of cooperative nodes (class l),the probability is now higher than

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80%. Without any cooperation approach, the probability was 65%, only. Networks where selfish nodes form the majority have an even higher benefit. The probability of a network with a classification C = (20/10/80) raises from 0.03 to 0.32. Studying figure 6 , it is remarkable that the curves consist of three parts: Values between Ak = 0 and Ak = 0.1, values between Ak = 0.1 and Ak = 0.4 and values larger than Ak = 0.4. This effect is caused by the different classes and the corresponding forwarding behaviors. Whenever the probability with which a node forwards a packet reaches the maximum of k = 1.0, the probability cannot be increased anymore: no additional benefit is possible. From figure 6, we observe that for small Ak the hop gain of networks consisting of cooperative nodes is much higher than of the other networks. This is due to the fact that in such networks the cooperating nodes form the majority. Ak = 0.1 leads to a maximum number of more than 8 hops without falling below the threshold of 60%. In this case, 90% of the nodes forward all packets. Conclusion: Improving the forwarding behavior of all classes increases the probability of receiving packets. The benefit is quit high for all kinds of networks. If Ak is small, the improvement of networks is much better for networks consisting of cooperative nodes than for networks consisting mainly of selfish nodes. 5.4. Increasing the Percentage of one class

Another effect of cooperation approaches may be to increase the percentage of one class. One reason might be the insight of a node to cooperate due to the fact that the punishment by the cooperation stimulating approach is worse. In figures 3 and 5 we observe that increasing the percentage of one class leads to a higher probability of receiving a packet. The more the

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Figure 7. Inc. Class 1, decr. Class 3

Figure 8. Throughput of a Network

surface is curved, the higher is the benefit of networks consisting basically of nodes belonging to class 1. Even small changes of the classes can lead to a high benefit. Figure 7 shows an example where the number of nodes belonging to class 3 is decreased and the number of nodes of class 1 is increased, denoted as Aa3,I. We see that networks consisting of cooperative nodes have a high benefit even for small changes. To get the same maximum number of hops for networks where selfish nodes form the majority, much more nodes have to change from class 3 to class 1. The same statement holds in the case where nodes of class 3 and class 2 are decreased and the number of nodes of class 1 is increased. The curves are similar t o the curves in figure 7. The benefit of the network is somewhat higher and leads to longer paths. Conclusion: Increasing the percentage of class 1 is worthwhile. The relative gain for good and bad networks is all about the same. However, the absolute gain is higher in cooperative networks. To achieve the same effect in selfish networks, Aai must be rather high. 6. Simulation Results

This section presents our simulation results validating our analysis. Furthermore, we show that the simulation yields the same conclusions as the analysis. The parameters of the simulation are as follows: NS2 was used as simulator, the area used was 1000m~1000m,100 nodes equipped with IEEE 820.11b and a transmission range of 200m were used. 6 concurrent flows with 256 Bytes each 0.2 seconds were sent from a randomly choosen source to a randomly chosen destination. In addition, a Random Direction Model was used to ensure that a uniform distribution of the nodes in the area was maintained. Simulations using static scenarios were also performed. The

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results are similar to those presented here, therefore they are not presented. First we show that with the help of our analysis we are able to compute the throughput of an ad hoc network. In figure 8 we see that the throughput measured in the simulation was close to our analysig using equation 1. The difference between the two curves is negligible. Table 1 summarizes our results. Increasing the percentage of a class which contains a lot of nodes is worthwhile. For example, increasing the percentage of class 1 from C = (80/10/10) to C = (90/10/0) leads to a benefit of 11.16%. Increasing class 1 in networks consisting of a few selfish nodes leads to a benefit of 3.49% only. The more nodes belong to a class the higher the benefit. Increasing the cooperation level of class 3 yields a better benefit in selfish networks. From Table 1 we see that the throughput of the network C = (80/10/10) increases from 53.76% to 59.93%. This is a gain of 6.17%. Increasing the cooperation level of selfish nodes improves the network much more. In our example, the throughput increases from 19% to 34.80%, that is a gain of 15.8%. However, the throughput of selfish networks is much smaller than the throughput of cooperative networks. The analysis has shown that in a network with the classification C = (20/10/70) the cooperation levels of F = (0.9/0.6/0.6) is similar to a network with the classification C' = (60/10/30) and the cooperation levels of F' = (0.9/0.6/0.2). In fact, this result was confirmed by the simulations. The first network has a throughput of 37.47%, whereas the throughput of the second network is 34.80%. That is a difference of 2.67%, only. Table 1. Throughput and mean Number of Hops

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The answer t o the question whether cooperation stimulating approaches have a significant effect on mobile ad hoc networks was shown t o be: "It depends on the network. In case of networks where most of the participants are cooperative, a cooperation approach has a strong effect and yields longer possible paths whenever the cooperation approach increments the probability of the cooperative nodes. Improving the forwarding behavior of selfish nodes does not lead t o a significant benefit. On the other hand, improving the forwarding behavior of selfish nodes yields longer pathes in networks where selfish nodes are in the majority. Increasing the percentage of cooperative nodes improves the network as well. In case of basically cooperative networks, the effect is much stronger than for networks with a lot of selfish nodes. In networks with many reliable nodes, even a small increment yields significantly longer paths. Is the network a collection of malicious stations, the increment must be very high. A mathematical analysis of complex networks with hundreds of stations and many different classes of nodes is very difficult. The impact of the MAC-Layers (e.g. wireless LAN), routing protocols, and interface queues have t o be discussed as well. These issues are subjected t o further research.

References 1. S. Buchegger, J. -Y. L. Boudec, Performance Analysis of the CONFIDANT Protoco, Proc. of IEEE/ACM Symposium On Mobile Ad Hoc Networking and Computing, 2002 2. L. ButtyAn, J. P. Hubaux, Stimulating Cooperation in Self-organizing Mobile Ad Hoc Networks, ACM Journal For Mobile Networks and Applications, Spec. Iss. On Mobile Ad Hoc Networks, 2002 3. M. Fkank, P. Martini, M. Plaggemeier, CineMA: Cooperation Enhancement in MANETs, Conference on Local Computer Networks, 2004 4. B. Lamparter, K. Paul and D. Westhoff, Charging Support For Ad Hoc Stub Networks, Elsevier Journal of Computer Communication, 2003 5 . B. Lamparter, M. Plaggemeier and D. Westhoff, Estimating the Value of Cooperation Approaches for Multihop Ad Hoc Networks, Elsevier Ad Hoc Networks Journal, 2005 6. S. Marti, T.J. Giuli, K. Lai, and Mary Baker, Mitigating Routing Misbehaviour in Mobile Ad Hoc Networks, 6th International Conference On Mobile Computing and Networking, 2000 7. P. Michiardi and R. Molva, CORE: A Collaborative Reputation Mechanism to Enforce Node Cooperation in Mobile Ad Hoc Networks, Sixth IFIP Conference On Security Communications, and Multimedia, 2002 8. S. Zhong, S., J. Chen and Y.R. Yang, Sprite: A Simple, Cheat-Proof, CreditBased System For Mobile Ad-Hoc Networks, Proc. of IEEE INFOCOM '03

MOBILITY MANAGEMENT IN MULTIHOPS WIRELESS ACCESS NETWORKS

FABRICE THEOLEYRE AND FABRICE VALOIS CITI - INRIA ARES, INSA Lyon 21, Avenue Jean Capelle, 69621 Villeurbanne Cedex, fiance Email: {fabrice. theoleyre, fabrice. valois) @insa-lyon.fr Tel: ( f 3 3 ) 4 72 43 63 27 and (+33) 4 72 43 64 18 Fax : ( t 3 3 ) 4 72 43 62 27 Ad hoc networks are spontaneous networks of mobile nodes which use wireless communications. The nodes must collaborate to route and forward data packets from a source t o a destination. We consider multihops wireless access networks which are ad hoc networks connected to the Internet via Access Points (AP). A terminal can send and receive data packets to and from the Internet. To achieve this goal, we propose here a micro-mobility management solution. The solution uses a virtual backbone to centralize information and t o limit the overhead. The proposition mixes the reactive and the proactive approaches to propose a trade-off between the delay and the overhead. In upload, each node has a default proactive route toward the AP introducing no latency. In download, the AP initiates a reactive localization t o find a route to the destination inside the ad hoc area, reducing the overhead. A solution of paging with several AP and a solution of power-energy saving are also proposed.

1. Introduction

Mobile Ad hoc networks (MANET) could be defined as spontaneous networks: a collection of terminals organizes itself to exchange packets with each other via wireless communications. A source is not always a neighbor of the destination, so a route must be provided. Moreover, neither wired nor wireless router exists to manage the network. Thus, some terminals must collaborate t o forward the data packets from the source to the destination. Ad hoc networks do not distinguish the routers and the clients: a terminal plays both roles. In a MANET, all terminals are independent and can move freely. MANET could be interconnected to the Internet via special gateways: the Access Points (AP). Such networks are often called Hybrid Networks or Multihops Wireless Access Networks. Routing is one of the major issues in the MANET: a packet must be forwarded from a source to a destination without loss and with reduced 146

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delay and overhead. Many protocols were proposed and can be classified in 2 major categories: the reactive protocols discover routes on-demand, flooding the network t o find a route, whereas in proactive protocols, each node floods the network to create proactively routes toward it. Because of the particular constraints of MANET, we think that a self-organization is required. Some nodes must be chosen to manage the network, helping weakest nodes. These managers will stabilize the logical network view and will allow to reduce the control traffic. A self-organization6 was already proposed: strongest nodes form a virtual backbone, acting as routers and managers. This backbone can optimize floodings and reduce the control traffic for other nodes. We propose to use this virtual backbone to provide a new solution for mobility management. The data packets are forwarded proactively from the source toward the AP, constituting a default route to Internet. Inversely, routes from the AP toward a node could be discovered reactively. A solution of paging and power-energy saving are also proposed. Next, we expose related work about routing in hybrid networks. Section 3 presents briefly the virtual backbone and the routing solution. Paging and power-energy saving solutions are also described. Section 4 presents some simulation results, before the conclusion and perspectives.

2. Related Work

MIPMANET3 proposes to integrate a reactive routing protocol (AODV) and Mobile IP. AODV is used for internal communications whereas external communications use Mobile IP. The Access Point acts as Mobile IP gateway (Foreign Agent): it must periodically flood Agent advertisements in the network, giving some Mobile IP parameters. Floodings present many problems in MANET4: redundancy of transmissions exists and collisions occur creating a lack of reliability. The advertisements could be discovered reactively by clients5, but inducing a delay. A proactive and reactive combination is also possible, but the trade-off is complex to set. Moreover, a node must Aood several times the network before deciding that the destination is outside the MANET area. To the best of our knowledge, only MEWLANA' proposes a solution optimized only for hybrid networks. The authors present 2 approaches. MEWLANA-TD allows both internal and external communications and is inspired from MIPMANET, but using a proactive routing protocol (DSDV). Each node knows instantaneously a route toward each node in the MANET area. However, the overhead for Agent Advertisements re-

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mains unchanged. Moreover, the ratio of internal communications must be sufficient to justify the important control traffic amount required by DSDV. The second approach, MEWLANA-RD, is specifically designed for hybrid networks. The AP sends periodically Agent Advertisements. Each node registers the forwarder as the default route to the AP and forwards the Agent Advertisement. In parallel, each node answers in sending a Registration to the AP, on the new default route. However, the periodical Agent Advertisements and Registrations overload the radio medium, and many collisions occur, inducing many packet losses. Moreover, the systematic periodical reconstruction of the tree is suboptimal.

3. Proposition Our solution mainly focus on the problems of both routing and mobility management in hybrid networks. The AP being the gateway to the Internet, it may constitute the default route, routing all the traffic, and acting as a Mobile IP Foreign Agent. If the AP has packets to deliver to one of its nodes with no associated route, one is discovered reactively. We use a virtual backbone6. The backbone nodes, the dominators, are selected according t o a stability weight representing their aptitude to act as network managers. Some nodes are elected dominators to form a connected structure where each normal node (or dominatee) is at most Ic hops far from its dominator. The backbone constitutes a tree of dominators where the leaves are the dominatees. Each dominator maintains the identity of its parent (except the AP) and the identity of the dominators for which it is a parent: they constitute its children. MANet are volatile environments. Hence, we have proposed a maintenance protocol to maintain the efficiency of the virtual backbone. To maintain the backbone connectivity, ap-hello are periodically sent by the AP, but forwarded only by dominators, limiting the overhead. Procedures for backbone reconnections are also proposed. 3.1. Mobility Management 3.1.l. Upload

The AP can represent a suited default router. When a node wants to send a packet, it delivers it to the AP. Then, the AP acts as a proxy t o find a route in the Internet, to do Network Address Translation if required,. . . The backbone is a tree rooted at the AP where a parent represents the next hop through the backbone to reach the AP. Each nodes maintains proactively

149 the identity of its k-neighbors, with classical h e l l o packets. A dominatee knows the identity of its dominator, its distance, and a next hop toward it. This next hop can appears to be the default route. A dominator maintains the identity of its parent in the tree, from which it receives the ap-hellos. This parent represents its default route. We assume that communications will mainly be initiated by the nodes creating an efficient proactive feature: the route knowledge requires no latency and no additional overhead. An inverse route can be learned gratuitously, as described further.

3.1.2. Download When a data packet is received from the node N , the node registers N as the next hop toward the source before forwarding the packet. Hence, an inverse route, i.e. in download, can be gratuitously learned when a node sends a data packet. Each data packet refreshes this proactive route, in triggering the associated timer in the cache of intermediary nodes. However, if the node did not send a data packet, or if the timer of the route expired, the AP must implement a localization process. Because we assume this case seldom, we propose a reactive solution. When the AP receives a packet, and no route to the destination D is known, it buffers the data packet, the memory of the AP being supposed to be important. Then, the AP sends in multicast to its children in the backbone a Route Request. Each dominator N which receives this packet forwards the request if D is unknown. Else, N acts as proxy and sends a Route Reply t o the AP, the source being seen as D. The Route Request is forwarded along the backbone until D is found. If D is a dominator, one of its dominator neighbors or in the worst case the node itself will send a Route Reply. In the same way, if D is a dominatee, its dominator is a t most k hops far. Hence, D is in the neighborhood table of its dominator: a Route Reply will be generated. To limit the impact of the backbone disconnections, any dominator is allowed t o act as proxy for Route requests. This limits the Route Request failure, at the cost of a negligible overhead. The Route Reply is sent on the default route, to the AP. A route is hop by hop created in the cache of each intermediary dominator, like with data packets. A distributive route cache is created, a timer being associated t o this route. To adapt the solution t o topology changes, a dominator which loses one of its children sends a Route Delete. This packet, forwarded t o the AP, deletes all outdated routing entries.

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3.1.3. Ad HOC Routing As an extension, ad hoc connectivity can be proposed. A source 5’ sends a data packet t o the destination D via its default route toward the AP. If a node receiving it knows a route to the destination, it sends directly the packet along this route. Else, the AP will finally receive the data packet. The AP will know if D is in the Internet or in the ad hoc area (with the address prefix, its own paging cache.. .). If D is in its covering area, it adds the data packet to the data buffer, and sends a Route Request. When the Route Reply arrives, the AP sends the buffered packets. Other data packets to D will be forwarded until it reached the first common ancestor of D and the source in the backbone. The route length and the delay are in conclusion not optimal. 3.2. Paging and Power-energy saving Schemes

Paging is used in cellular networks to limit the overhead of registrations. The node registers itself less frequently in its paging zone than in its AP. Paging consists in finding the AP serving a destination. A Paging Master (PM) is connected t o all the AP constituting the paging zone. The Paging Master adds the mobile in its Membership Cache with a long timeout. When a packet arrives, the PM verifies that the destination is present in its Membership Cache. Then, it searches an associated entry in the Paging Routing Cache associating an AP to a node. If an entry is found, the data packet is directly sent to the corresponding AP. Else, the P M buffers the data packet and sends a Paging Request to all AP of its paging zone. These AP will send a Route Request. When an AP receives a Route Reply, it sends a Paging Ack to the PM. The PM adds an entry in its Paging Routing Cache with a short timeout and sends the buffered packet. Terminals have limited energy reserves. However, turn off its radio is the only way t o economize its energy2. Such a node does not participate in the network life, it sleeps. The backbone is particularly suited for such a feature. Elections are based on a weight depending on the energy reserve. Thus, a node with a too low energy reserve will not be elected as backbone member. Moreover, dominatees have a role of clients, they can spare their energy. The degree must be sufficient so that eventual backbone reconnections could be forwarded. Thus, a dominatee is allowed to sleep if the number of not-sleeping 1-neighbors is superior or equal to 6. Finally, a dominatee is allowed to sleep according to the probability l / n , n being the number of 1-neighbors with a lower weight.

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4. Performance Evaluation

We simulate our solution wit OPNET Modeler 8.1, with the WIFI standard model (300m radio range), and the random waypoint mobility model. The default parameters are a speed of 5m.s-l, 40 nodes and a degree of 9. Data flows of 8 data packets interspaced by 0.25s are sent according to the exponential distribution with a mean value of 2s. Data flows are sent in the same way from the AP to one random destination, and from a random source to the AP. The maximum distance from one node to the backbone is 2 hops. To evaluate the solution, we investigate the behavior according to the mobility, the load, the number of nodes, with the paging and the power-energy saving activated or not. We compare MEWLANA-RD and the proposed solution, labeled cdcl.

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Our solution is robust according to the mobility: the delay increases slowly, even with a very high speed of 30m.s-l. The delay of MEWLANARD follows the same tendencies. Our solution mixing the reactive and the proactive approaches doesn't suffer from the delay compared to the fullproactive approach. The delivery ratio is almost symmetrical in upload and in download. Oppositely, the delivery ratio in download is in MEWLANARD 10% lower than in upload. MEWLANA-RD reconstructing periodically the whole backbone, collisions occur. Finally, the delivery ratio in both directions is higher for cdcl thant for MEWLANA-RD. Figure 2(a) presents the horizontal scalability of both solutions, i.e. the performances when the number of nodes increases. The delivery ratio of cdcl is higher than that of MEWLANA-RD. Moreover, the symmetrical property of the delivery ratio remains identical for cdcl. Both solutions are simulated with a constant degree when the number of nodes increases. Hence, the average route length growth when the number of nodes increases, increasing the delay too. However, this increasing remains acceptable.

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Figure 2(b) presents the performances of both solutions according to the load of the network, i.e. the number of simultaneous communications. We can observe that both approaches are very scalable according to the load for the delivery ratio, although MEWLANA-RD keeps on presenting more packet losses. The delay increases lightly when the load becomes very important, when every node is in communication. However, such an augmentation remains below 50ms. Finally, we can note that the overhead of MEWLANA-RD (1.4 packet per second) is higher than for cdcl (0.8 pps). We implement our solution of paging, creating a Paging Master. We place 2 AP on the surface, on the top left and top right corners. This network can deal with a more important load, the data packets being on average distributed among the AP, which is the requested property (fig.3(a)). However, the delivery ratio suffers more from the mobility. The delay of convergence of all routing caches and paging caches could be important, some data packets are dropped.

(a) Paging (b) Power-energy saving Figure 3. Impact of the Paging and the Power-energy-saving solutions

The power-energy solution was simulated. Third of the nodes don't generate or receive data packets, they can sleep if they are dominatees.

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This power-energy saving solution has almost no impact on the delay, but the delivery ratio decreases slightly. If a collision occurs for an hello, the neighbors can choose as next hop a sleeping node, creating data packets losses. A non-communicating node is sleeping on average 25% of the time.

5. Conclusion We propose here a routing and localization solution optimized for hybrid networks. This solution uses a virtual backbone t o structure routing caches. In upload, information to maintain the backbone is used to find a route to the AP, constituting a gratuitous default route. In download, the AP implements a localization procedure t o discover a route reactively. To minimize the frequency of the localization process, an inverse route is created on the fly when a node sends a Data Packet toward Internet. The s e lution presents a very high delivery ratio, and a limited delay although a reactive solution is used besides the proactive part. A solution of paging and power-energy saving are also proposed, taking into account the natural heterogeneity of the hybrid network. The backbone hides many topology changes, offering a stable view of the topology. Next step of this study will be the implementation of new functions like the handover with the choice of the optimal AP, the load balancing among the AP according t o the number of nodes and a multicast routing solution.

References 1. Mustafa Ergen and Anuj Puri. Mewlana-mobile ip enriched wireless local area network architecture. In 56th Vehicular Technology Conference, Vancouver, Canada, September 2002. IEEE. 2. L. Feeney and M. Nilson. Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. In INFOCOM, Anchorage, USA, April 2001. IEEE. 3. Ulf Jonsson, Fredrik Alriksson, Tony Larsson, Per Johansson, and Gerald Q. Maguire. Mipmanet - mobile ip for mobile ad hoc networks. In Proceedings of the first A C M international symposium on Mobile and ad hoc networking and computing, pages 75-85, Boston, USA, May 2000. ACM, IEEE Press. 4. S.Y. Ni, Y.C. Tseng, Y.S. Chen, and J.P. Sheu. The broadcast storm problem in a mobile ad hoc network. In MobiCom, Seattle, USA, August 1999. ACM. 5. Yuan Sun, Elizabeth M. Belding-Royer, and Charles E. Perkins. Internet connectivity for ad hoc mobile networks. International Journal of Wireless Information Networks, 9(2), April 2002. 6. Fabrice Theoleyre and Fabrice Valois. A virtual structure for mobility management in hybrid networks. In Wireless Communications and Networking Conference (WCNC), pages 1035-1040, Atlanta, USA, March 2004. IEEE.

LOCATION UPDATE PROTECTION FOR GEOGRAPHIC AD HOC ROUTING

Z. ZHOU AND K. C . YOW School of Computer Engineering Nanyang Technological University Singapore 639798 E-mail: (pg04570215,askcyow) Ontu. edu.sg Security research in mobile ad hoc networks(MANETs)has been receiving increasing attention due to its evident importance in practical deployments. The main progress achieved so far is the protection of the critical Route Discovery component of on-demand routing protocols. In this paper, we identify that Location Update is the key component for geographic routing. To focus on securing the basic local location update, we propose three schemes in this paper with a detailed discussion.

1. Introduction

One of the most active areas of security research in MANETs is securing ad hoc routing. Considerable work has been done to deal with security issues of some prominent on-demand routing protocols The main effort of their work is to design efficient security mechanisms to protect the Route Discovery procedure from malicious attacks. However, we are aware of a severe missing of proper study in geographic routing security. With the increasingly easier acquisition of location information, geographic routing schemes are attracting more attention. It utilizes location information to make localized forwarding decisions, and is considered to be more scalable and resilient to node mobility compared with on-demand protocols. Our work focuses on securing geographic routing. We found that the counterpart of route discovery in geographic routing is location update, which is extremely vulnerable to a variety of attacks that compromise the security and performance brought by geographic routing. Our paper is organized as follows. In Section 2, we identify the importance of Location Update and its vulnerabilities. To protect against malicious attacks, we propose three schemes for securing location update in Section 3. Section 4 discusses the security and gives the conclusion. 1921314

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2. Threats of Geographic Routing

We consider Location Update as the major target for attacks in geographic routing, since it essentially drives the routing functionality. Basically, two underlying mechanisms make a typical geographic routing work: neighboring exchange and location service 8,9. We further generalize the tasks of geographic routing into one issue, i.e. location update. Neighboring exchange is essentially a constant update process to the node’s neighborhood, while location service could be generally viewed as a constant update to some remote nodes who behave as proxies of the node, or need the node’s location directly. The main difference of the two update processes is the “distance” between who updates and who is updated. Therefore, in another way, we define neighboring exchange as local location update (LLU), and location service as remote location update (RLU). Obviously, location update including LLU and RLU will be the main target of attacks that threat the correct functionality of geographic routing. One of the attack instances is the Black hole attack 2 , which is quite common in on-demand protocols, and it becomes much easier to launch in geographic routing. Basically, black hole attack causes the traffic to go through the attacker or certain point of the network, where most possibly the traffic is suppressed. To achieve this goal, the attacker may simply send a local update message with such a forged location that makes it compelling in the next hop selection of a certain traffic flow. In this case, the future traffic will be redirected to the attacker node. Another typical attack that may happen is routing loop. A simple attack instance is illustrated in Figure 1. The notation of X ( z , y) means that the coordinate of node X is (z,y). M keeps on refreshing the spoofed location of E and A , causing the traffic destined for D to a routing loop A , E , B ,

A.

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Figure 1. Routing loop: A-E-B-A. The malicious node M moves in the range of A but out of B to spoof as E and moves out of A but close to B to spoof as A.

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Identity spoofing is the main means for routing loop attacker to succeed. If spoofing of network identities is exploited excessively, it may even cause denial of service to a certain victim. The attacker can simply fabricate enough nodes around the edge of the victim’s radio range, leading to all the traffic from that node to the forged nodes controlled by the attacker. Suppression of all packets, in fact, denies the access of the node. Threats of location update is not limited to those mentioned above. For more comprehensive discussion, please refer to our prior work lo. Since LLU and RLU share most of security problems and required properties such that solutions to LLU could be potentially extended to RLU scenarios. So, in this paper, we will focus on securing local location update. 3. Location Update Protection

In this section, we propose three secure LLUs to prevent malicious attacks and provide desirable security goals such as authentication, integrity and confidentiality. Schemes are denoted as LLU-2.

3.1. LL u-0 In LLU-0, each node periodically broadcasts an update message with its current location to its neighbors. The update message should be digitally signed by the sender with its private key, and its certificate is also attached with the message. The main fields of LLU message include message identifier, identity of the sender, current location of the sender, timestamp of the message about to send, a digital signature computed over all fields ahead, and the sender’s certificate. We denote the LLU message like . Each recipient of the message first verifies the attached certificate by using the public key of the trusted third party. If it is valid, it means the public key inside the certificate is genuine and truly associated with the claimed sender, and could be used to verify the signature of the message. F‘urthermore, the timestamp included can be used to determine if it’s a message replay or not. The location update will be finally accepted to update the recipient’s neighbor table if there is nothing wrong in all verifications. In this case, the node without a valid certificate is denied by the network since it will not be able to sign the update message properly. And any fabrication or spoofing of identities is not possible either. LLU-0 applies the digital signature scheme to make the solution quite straightforward. It provides most of the expected security properties, such as the authenticity of the sender, integrity of the location, and even sender

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non-repudiation, which is potentially useful as a deterrent of misbehavior since only the node with the corresponding private key can generate the signature. However, performing public-key cryptographic operations might be computationally expensive to low-end mobile devices. A node has to periodically sign its update messages, and has to verify all its received messages. And potentially it is more vulnerable to resource consumption attack, where a malicious node injects a large number of fake LLU messages that need processing. The computing resource or energy of innocent recipients may be exhausted in a short time. In fact, this problem exists in almost all cryptographic protocols that perform usually heavy cryptographic operations. Our defense solution is to mount an incoming traffic monitor, and each node could set a proper threshold value for incoming traffic volume that it can bear. Once the packet rate exceeds the threshold, it indicates that either a network exception or an attempt of resource consumption attack is taking place, and the node could respond accordingly to the event.

3.2. LLU-1

LLU-1 targets on reducing the computation cost and accordingly the risk of potential consumption attack when a pure public key scheme is applied. LLU-1 utilizes symmetric cryptography for authentication of update messages while it avoids the impracticality of using pairwise shared key. It’s basically an authenticated key exchange approach based on key transport. The goal of LLU-1 is that every node generates a “group decryption key” within its own domain, i.e. its neighborhood. The group decryption key will be used for neighbors to authenticate and decrypt its update messages. We require a periodic beacon message to detect new neighbors and to remove “dead” ones. Each beacon message includes the sender’s certificate. The event of new neighbor coming into node A’s range triggers a direct group key transport of A. Thus, the group decryption key is delivered to the new neighbor so that it will be able to decrypt messages from A. A neighbor table will be maintained at each node with each entry at least including neighbor id, status of key distribution, group key of this neighbor, location, timestamp of location. An entry is added when a new neighbor is detected by receiving the update message, and is deleted when no group key is timely distributed or when timeout happens on this neighbor due to the node failures or a broken link. Note that every node is able to receive messages to maintain its neighbor table, but will not be able to verify the

158 update message and the source until it receives the corresponding group key from the source. LLU-1 combines periodic beaconing and location update into a single update message, which maximally reduces the required messages exchanged to save power and bandwidth. A basic instance of this scheme is illustrated in Table 1. A and B are neighbors. Ek denotes the encryption operation with key k. gkx denotes node X ’ s group key. As a regular base, A broadcasts its update message to its neighbors. B found A is not in its neighbor table and started to transport its group key to A. To securely deliver the key, B extracts A’s public key from its certificate and encrypts the message after signing. On receiving B’s key transport, A was aware that B is also a new neighbor, and delivered its group key to B in a similar way. By exchanging group keys, they are able to authenticate each other and be updated with fresh and reliable location of neighbors. Although LLU-1 also involves public key operations, yet they are not performed periodically but reactively as needed.

Another feature of LLU-1 is that group key rity becomes easier. It is simply another round in order to avoid potential incompatibility of key, sequence number could be introduced to key applied or transported.

update for improving secuof key transport. However, different versions of group identify the specific group

3.3. LL u-2 Traditional symmetric authentication is not able to provide broadcast authentication property as LLU-0 can, i.e. a broadcast message can be directly verified by all recipients. If this property is available, multiple unicasts of key distribution in LLU-1 could be avoided. We explore, in LLU-2, the way of applying TESLA to achieve efficient broadcast authentication property based on symmetric cryptography. Each node using LLU-2 generates a chain of keys in advance by hashing an initial random value consecutively. These keys are used in a reverse order as generated, and will be disclosed in a few time slots after used. An update message includes these fields,

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< “LLU”, id, Etki (id, loc, ts), i , tk+l >, where E, means the encryption operation with key x, and tki is the ith TESLA key. Each node in the network is supposed to send LLU messages periodically. In order to correctly determine the sender’s key disclosure, a recipient needs to know a few parameters that constructs a key disclosure schedule. It includes To, Tint,and d, where To is the starting time of the first time slot, Tint is the duration of a time slot, and d is the number of slots to pass before the key is disclosed. The very first commitment of the key chain is the last value of hashing. Assuming that any node has reliable key disclosure schedule information and the commitment of key chains of other nodes, the authentication at each receiver has the following two steps: (1) In order to authenticate the message, the receiver has to know the sender’s key disclosure schedule, where it checks if the TESLA key tki used in the received message is disclosed or not. If the key is disclosed, the update will not be accepted. Otherwise, the message is buffered for future verification. (2) Authenticating the disclosed key tki-1 requires the commitment of the sender’s key chain, and certain early-verified keys of the sender’s key chain. With the commitment, TESLA keys are completely selfauthenticated, because hashing any disclosed key a certain number of times is supposed to be equal to the commitment (recall that TESLA keys are used in the reverse order of the chain generated by consecutive hashing). The authenticated key could be used to verify the old update message buffered. Furthermore, the disclosed key is qualified as a new commitment to replace the old one.

By using TESLA, LLU-2 allows nodes to efficiently generate and verify MACs over the periodic location updates. But LLU-2 presents another problem due to the temporal effect of TESLA for broadcast authentication, i.e. the node will always have a list of neighbors with old location information authenticated. How old is the location is tightly dependent on the frequency of location update as well as the key disclosure schedule. In a low mobility environment, it may have little impact. Our solution to this issue is that the sender could anticipate its future position based on its current speed and direction, and send the update message with the anticipated future location after key disclosure time. We consider that movement prediction should not be a difficult job, especially when only predicting into a several-seconds future. In this case, every node will have a reasonably accurate and authenticated position estimate of its neighbors.

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As readers may have noticed, the LLU-2 scheme we discussed above has an assumption that the key disclosure schedule and the key chain commitment are the prior knowledge of every verifier. That means TESLA requires a prior receiver setup, i.e. these critical information has to be securely distributed to all expected receivers. It is not a good idea that key schedules for all nodes are fixed throughout the network lifetime and are pre-distributed to all expected receivers, since the storage requirement is similar to pairwise shared key which is linear to the network size. Therefore, LLU-2 also employs digital signature to secure the distribution of key disclosure schedule. But potentially dynamic neighborhood membership also requires this distribution to be periodic. However, LLU-2 does not require the node signing messages periodically. The signature generation for key disclosure schedule and key chain commitment could be an offline operation.

4. Conclusion

The schemes we proposed can thwart all attacks we mentioned in Section 3. Without valid authentication data, attackers are not able to masquerade as other nodes and to disseminate false location information. In addition to the authentication and data integrity that LLU-0, LLU-1 and LLU-2 can provide, in fact, some applications require the prevention from unauthorized eavesdropping, especially when location information becomes sensitive for individuals or the whole task. In this case, the confidentiality of LLU is required. LLU-0 and LLU-1 have the inherent ability to provide confidentiality while LLU-2 does not. Therefore, in terms of efficient confidentiality the tendency of using group key to encrypt messages becomes obvious. As we can see, all three proposed schemes assume that each node has a prior certificate which is typically issued by an external CA. We consider this inevitable because most of the practical security proposals more or less rely on a public key infrastructure or certificate scheme. Providing PKI support for MANETs is also another important research track for MANET security. There are several distributed PKI schemes l 1,12 proposed, which could be applied in our context besides having an external CA. The main difference of applying different prior security setup system is that they have different underlying trust relations in nature, which may fit into different applications or scenarios.

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References 1. M. G. Zapata and N. Asokan, “Securing Ad Hoc Routing Protocol,” in Proceedings of the 2002 ACM Workshop on Wireless Security ( W i s e 2002), September 2002, pp. 1-10, 2. Y.-C. Hu, A. Perrig, and D. B. Johnson, “Ariadne: A Secure On-Demand Routing Protocol for Ad Hoc Networks,” in Proceedings of the Eighth Annual International Conference on Mobile Computing and Networking (MobiCom 2002), Sept. 2002. 3. K. Sanzgiri, B. Dahill, B. N. Levine, C. Shields, and E. M. Belding-Royer, “A Secure Routing Protocol for Ad Hoc Networks,” in Proceedings of IEEE International Conference on Network Protocols(ICNP), November 2002. 4. P. Papadimitratos and Z. J. Haas, “Secure Routing for Mobile Ad Hoc Networks,” in Proceedings of the SCS Communication Networks and Distributed Systems Modeling and Simulation Conference (CNDS 2002), 2002. 5. D. B. Johnson and D. A. Maltz, “Dynamic source routing in ad hoc wireless networks,” in Mobile Computing, Imielinski and Korth, Eds. Kluwer Academic Publishers, 1996, vol. 353. 6. C. Perkins and E. Royer, “Ad-hoc on-demand distance vector routing,” in Proceedings of the 2 nd IEEE Workshop on Mobile Computing Systems and Applications, Feb 1999. 7. A. Perrig, R. Canetti, D. Tygar, and D. Song, “The TESLA Broadcast Authentication Protocol,” R S A CryptoBytes, vol. 5, no. 2, pp. 2-13, 2002. 8. J. Li, J. Jannotti, D. De Couto, D. Karger, and R. Morris, “A scalable location service for geographic ad-hoc routing,” in Proceedings of the 6th A C M International Conference on Mobile Computing and Networking (MobiCom ’00), 120-130 2000. 9. Y. Xue, B. Li, and K. Nahrstedt, “A scalable location management scheme in mobile ad-hoc networks,” in 26th Annual IEEE Conference on Local Computer Networks (LCN’Ol), 2001. 10. Z. Zhou and K. C. Yow, “Geographic ad hoc routing security: attacks and countermeasures,” to appear in Ad HOC€4 Sensor Wireless Networks, Old City Publishing, 2005. 11. S. Capkun, L. Buttyan, and J. Hubaux, “Self-organized public-key management for mobile ad hoc networks,” in ACM International Workshop on Wireless Security( W i s e ’02), 2002. 12. H. Luo, J. Kong, P. Zerfos, S. Lu, and L. Zhang, “Self-securing ad hoc wireless networks,” in IEEE Symposium on Computers and Communications), 2002. 13. B. Karp and H. T. Kung, “GPSR: greedy perimeter stateless routing for wireless networks,” in Proceedings of Mobile Computing and Networking, pp. 243-254, 2000. 14. P. Bose, P. Morin, I. Stojmenovic, and J. Urrutia, “Routing with Guaranteed Delivery in Ad Hoc Wireless Networks,” Wireless Networks, vol. 7 , no. 6, pp. 609-616. 2001.

PEDCF: PREDICTIVE ENHANCED SERVICE DIFFERENTIATION FOR IEEE 802.11 WIRELESS AD-HOC NETWORKS BASED ON AUTOREGRESSIVE-MOVING AVERAGE PROCESSES NABIL TABBANE Mediatron, SUPCOM, Route de Raoud Cite' El Ghazala, Ariana 2083, Tunisia

SAM1 TABBANE Mediatron, SUP'COM, Route de Raoud Cite' El Ghazala, Ariana 2083, Tunisia

AHMED MEHAOUA Prism, University of Versailles St-Quentin-en- Yvelines, 45, av. des Etats- Unis Versailles, 78035, France In this paper, we tray to reduce the degree of QoS degradation in IEEE 802.1 1 wireless ad-hoc network while enhancing at the same time the estimation of the quality of service of the networks. We shall present PEDCF: Predictive Enhanced Service Differentiation methods for forecasting resources to meet the QoS requirements for real-time service support based on AutoRegressive Moving Average processes: ARMA. These processes provide a range of models, stationary, that adequately represent the time Contention Window (CW) variations. The results obtained (in terms of throughputs and end-to-end delays) show that the PEDCF protocol, based on ARMA processes, performs better than conventional EDCF.

1. Introduction

We present methods for forecasting resources to meet the QoS requirements in IEEE 802.1 1 wireless ad-hoc network [ 11. Our approach, called Predictive Enhanced Distributed Coordination Function (PEDCF), is derived from the EDCF introduced in the IEEE 802.1 l e standard. It is combined with Mixed AutoRegressive-Moving Average (ARMA) processes to give forecasting about the behavior of contention window. We have implemented PEDCF in the NS-2 network simulator. Several simulation scenarios have been used to evaluate its performance and to determine the optimal value of certain parameters. We analyze through 162

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simulations the efficiency of the slow and predictive decrease of the CW after each successful transmission and compare it with the classic scheme proposed in the standard. Results show that PEDCF outperforms the basic EDCF, especially at high load conditions. Indeed, our scheme increases the medium utilization ratio and reduces considerably the collision rate. While achieving delay differentiation, low jitter is also maintained, and the overall goodput obtained is up to 30% higher than EDCF. Moreover, the complexity of PEDCF remains similar to the EDCF scheme, enabling the design of cheap implementations. 2. The Predictive EDCF (PEDCF) scheme Let n the number of active stations, and i the priority class. The flows sent by each station may belong to different classes of service with various priority levels. In each station and for each class i, the following parameters are defined: CW[I']: the current contention window, CW,,,,[I']: the minimum contention window, and CW,,[iJ: the maximum contention window. Note that i varies from 0 (the highest priority class) to 7 (the lowest priority class). In order to efficiently support time-bounded multimedia applications, we use a dynamic procedure to change the contention window value after each successful transmission and after each collision. We believe that this adaptation will increase the total goodput of the traffic which becomes limited when using the basic EDCF, mainly for high traffic load. After each successful transmission, the EDCF mechanism resets the contention window of the corresponding class i to CW,,,[I'] regardless the network conditions. Motivated by the fact that when a collision occurs, a new one is likely to occur in the near future, [ 6 ] propose to update the contention window more slowly (not reset to CW,,,,,[I']) after successful transmission to avoid bursty collisions. The simplest scheme to update CW[I']is to decrease it by a multiplicative factor such as O.5*CWold. This approach is denoted the Slow Decrease (SD) scheme. However, a static factor cannot be optimal in all network conditions. In our scheme, we propose that every class updates its CWparameter in a predictive way taking into account the previous CWs in each station. Indeed, the past values of C W can give an indication about network states. The predictive C W is calculated during a constant period (i.e. a fixed number of slot times). This prediction is based on ARMA: mixed AutoRegressive Moving Average model.

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2.1. ARMA: mixed AutoRegressive-Moving Average model

A stochastic model which can be extremely useful in the representation of certain practically occurring time CW is the so-called autoregressive model [7]. In this model, the current value of the process is expressed as a finite, linear aggregate ofprevious values of the process and an error term. Let us denote the values of a process at equally spaced times t, t-1, t-2, ... by z,, zt-l,zt-2,... ; Also let it, &, ... be deviations from p (the mean about which the process varies), it = Z, - p. it represents the estimated contention window at time t. To achieve greater flexibility in fitting of actual time contention window, it is sometimes advantageous to include both autoregressive and moving average terms in the model [3]. This leads to the mixed autoregressive-moving average model [4] iIt = cD18t-l+ ... + (Dp8t-p+ a, - 81a,-l- ... - 8,a,., (1) which employs p+q+2 unknown parameters p; cDl, ..., cDp; el, ..., 8,; 6; (the variance of the error term at), that are estimated from the data. In practice, occurring stationary time CW can be obtained with autoregressive, moving average, or mixed models, in which p and q are not greater than 2 (to obtain a parsimonious model). Eq. (1) will define a stationary process, provided that the characteristic equation has all its roots lying outside the unit circle. For the ARMA(p,q) process, there will be q autocorrelations p,, pq+ .. ., pI whose values depend directly, on the choice of the q moving average parameters 8, as well as on the p autoregressive parameters cD. Also, the p values p,, pql, .. ., pq-p+lprovide the necessary starting values which then entirely determines the autocorrelations at higher lags. By substituting estimates r, for pi, initial estimates for the parameters cDi and 8i can be obtained [S]. In the following sections, based on simulation, we analyse the performances of the ARMA models combined with EDCF protocol in congested environment. 2.2. Setting CW after each Update Period TepdO,,

The predictive CW is computed dynamically in each period TuPdureexpressed in time-slots. This period called Update Period should not be too long in order to get good estimation and should not be too short in order to limit the complexity. For each update period, the predictive CW is computed according to Eq. (1).

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We have fixed these parameters according to a large number of simulations done with different scenarios. In PEDCF and after each successful transmission of packet of class i, CW[g is then updated as follows: CWnav[g = mm(CWmJi/, CWpmi[g) (2) Eq. (2) guarantees that CW[g is always greater than or equal to CWm,,[g, so the priority access to the wireless medium is always maintained. In the current version of EDCF, after each unsuccessful transmission of packet of class i, the CW,,,,,[g is then doubled, while remaining less than the maximum contention window CWmux[iJ: CWnav[g= min(C Wmm[g,2 * C Wc,,d[iJ (3) We propose in PEDCF, after each unsuccessful transmission of packet of class i, the new CW of this class: Cwne,v[g = min(CWmux[U, CWppedil) (4) 2.3. Complexity of PEDCF The complexity of PEDCF is similar to the complexity of EDCF, only a few more resources are required. Some registers are necessary to buffer the parameters defined above: p, cDl , cD2 , , O2 , 6; in ARMA(2,2) parsimonious model. The calculation of each CWpmd[g requires four additions, four multiplications and one comparison for each active class. Finally, during the update period, two counters are needed to increment collisions and data sent. 3. Simulation Methodology and Results

We have implemented the PEDCF scheme in the ns-2 simulator [ 5 ] . In this section, we investigate and analyze the performance of PEDCF under several scenarios. As mentioned before, the PEDCF adapts the contention window values according to the previous CW each T,,pdure time slots (Tupdarevalue equal to 5000 time-slots [6]). We have n stations indexed from 1 to n. Each station generates the same traffic consisting of three data streams labeled according to their priorities with high, medium and low. Station n sends packets to station 1. Station i (i < n) sends to station i + 1 three flows of different classes: Audio (high priority), Video (medium priority), and Background Traffic (denoted by BT of low priority). In the following simulations, we assume that each wireless station operates at IEEE 802.11a PHY mode-6 [2].

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3.1. Impact of the trafflc load

To evaluate the performance of PEDCF, we investigate in this section the effect of the traffic load and compare it with EDCF and SD schemes. Our simulations use different types of traffics to evaluate service differentiation. Three queues are used in each station. The highest priority queue in each station generates packets with packet size equal to 160 bytes and interpacket interval of 20 ms, which corresponds to 64 Kbit/s audio flow. The medium traffic queue generates packets of size equal to 1280 bytes each 10 ms which corresponds to an overall sending rate of 1024 KBit/s. The low priority queue in each station generates packets with sending rate equal to 260 KBit/s, using a 200 bytes packet size. To modify the load of the network, we have used a different number of stations, which gradually increases during the simulations. Every station is able to detect a transmission from any other station, they are not moving during the simulations. We start simulations with two wireless stations, then we increase the load rate by incrementing the number of stations by one every eight seconds. Figures 5 - 9 show the averages of delay, goodput gain, medium utilization and collision rate over 5 simulations. The number of stations is increased from 2 to 44 which correspond to load rates from 7.5% to 160%. The relationship between the load rate and the number of stations is shown in Table 1. To evaluate the performance of the different schemes, the following metrics are used: Gain of goodput, Mean delay, Latency distribution, Medium utilization and Collision rate. Table 1. Correspondence between number of stations and load rate

Number of stations 2 5 10 15 20 25 30 35 40 42 44

Load rate 7.5 Yo 19 % 37 % 56 % 75 % 94 % 110% 131 % 150 % 160 % 170 %

Figure 1 shows the average delay for the audio flow corresponding to the high priority class. The PEDCF scheme is able to keep the delay low even when

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the traffic load is very high, i.e., with a large number of stations. We can observe that the average delay of audio for PEDCF is 50% smaller than that for the basic EDCF when the load rate is up to 100% (26 stations). Moreover, when the number of stations is more than 17, the delay obtained by the SD increases faster than PEDCF and EDCF schemes, while PEDCF always keeps a lower mean access delay less than 8 ms. We can also note that PEDCF offers an average delay 45% less than the SD scheme and 40% less than the EDCF scheme when the load rate reaches 170%. In Fig. 6, we plot the gain on goodput as a function of the traffic load of PEDCF and SD schemes. We observe that the goodput gain of PEDCF increases when the traffic load increases. It reaches about 23% when the load rate is about 130% (i.e. for 35 stations). Moreover, the goodput of PEDCF is 15% higher than the SD scheme when the load rate is 170%. Indeed, PEDCF is much more efficient during high load rate.

+PEDCF

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Figure 2: Gain on goodput

Figure 3 shows the medium utilization as a function of the traffic load. For the three schemes the medium utilization decreases when the traffic load increases. However, PEDCF achieves the highest medium utilization whatever the number of stations. Indeed PEDCF offers 15% of resources moreover than SD and 30% moreover than EDCF.

Number of stations

Figure 3: Medium utilization

Number of stations

Figure 4: Collision rate

The corresponding collision rate is shown in Fig. 8. Collision rates achieved by the three schemes are similar when the traffic load is low, i.e. the number of

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stations is less than 8. However, when the traffic load increases, PEDCF is able to maintain a lower collision rate than EDCF and SD schemes. We can explain this behavior by the fact that PEDCF uses an predictive technique (which approaches reality) to change the contention Windows according to the collision rate. The reduction of collision rate of PEDCF leads to significant goodput improvement and reduces the delay. We have used a different simulation to study performance on delay and jitter. This new experiment has the same topology than before, but the number of stations is increased from 2 (4 sec) to 25 (100 sec) and the simulation stops at t = 1 15 sec. The delay variations of both EDCF and PEDCF schemes are plotted in Fig. 9. PEDCF meets target by maintaining the delay lower than EDCF and stable during the live of audio sessions. However, we can note that both delay and jitter for EDCF are three times higher than PEDCF, which degrade the quality of audio flows.

0

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(a) Audio delay for EDCF (b) Audio delay for PEDCF Figure 5: The audio class delay variation

From the simulations, we can conclude that both PEDCF and SD schemes outperform EDCF. Using a predictive algorithm, PEDCF get much higher goodput than the SD scheme. Moreover, the PEDCF scheme can improve the goodput and delay performance of all types of traffics. 4. Conclusion

This paper has described an efficient means for predicting Quality of Service for IEEE 802.1 1 ad-hoc WLANs, to support real-time services. Our approach, called Predictive Enhanced Distributed Coordination Function (PEDCF) combined with forecasting contention window Method based on Mixed AutoRegressive-Moving Average ( A M ) processes improves drastically the performance of the real-time service support. Results show a clear advantage of QoS mechanism based on PEDCF over EDCF or SD mechanisms. The PEDCF scheme is able to keep the delay low even when the traffic load is very high. We observe that the average delay of audio for PEDCF is 50% smaller than that for the basic EDCF when the load rate is up to 100%.

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PEDCF is much more efficient during high load rate. Indeed, it reaches about 23% when the load rate is about 130%. Moreover, the goodput of PEDCF is 30% higher than the EDCF when the load rate is 170%. The protocol PEDCF allows more resources than EDCF or SD. Indeed PEDCF offers 15% of resources moreover than SD and 30% moreover than EDCF. We note that when the traffic load increases, PEDCF is able to maintain a lower collision rate than EDCF and SD schemes. The reduction of collision rate of PEDCF leads to significant goodput improvement and reduces the delay. we can note that both delay and jitter for PEDCF are three times lower than EDCF, which ameliorate the quality of audio flows. In a future work, we will study EDCF protocol combined with Seasonal Processes which provide a non stationary models, that can represent the optimal forecasting procedure of QoS to support real-time services in IEEE 802.1 1 adhoc WLANs. References 1. IEEE WG, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE 802.I I Standard, (1 999). 2. IEEE 802.1 la, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: High-speed Physical Layer in the 5 Ghz Band, Supplement to IEEE 802.I I Standard, (Sep. 1999). 3. N. Tabbane, S. Tabbane, A. Mehaoua, "Stationary Stochastic Models for Forecasting QoS in Ad Hoc Networks for Real Time Service Support", WSEAS Transaction (2004). 4. N. Tabbane, S. Tabbane, A. Mehaoua, "Autoregressive, Moving Average and Mixed autoregressive-moving average processes for Forecasting QoS in Ad Hoc Networks for Real Time Service support", in Proc. of VTC Spring, Milan, Italy, (May 2004). 5. K. Fall, K Varadhan, "NS Notes and Documentation," A Collaboration between researchers at UC Berkeley, LBL, USCLSI, and Xerox PARC, (February 25,2000). 6. L. Romdhani, Q. Ni, T. Turletti, AEDCF: Enhanced Service Differentiation for IEEE 802.1 1 Wireless Ad-Hoc Networks, INRIA, Rapport de recherche No 4544, (September 2002). 7. G. E. P. Box and G. M. Jenkins, "Discrete models for feedback and feedforward control," The Future of Statistics, ed. D. G. Watts, 201, Academic Press, New York, (1968). 8. G. E. P. Box and G. M. Jenkins, "Discrete models for forecasting and control," Encyclopedia of Linguistics, Information and Control, 162, Pergamon Press, (1969).


IEEE 802.11


A CARRIER-SENSE BASED TRANSMISSION POWER CONTROL PROTOCOL FOR 802.11 NETWORKS JAYANTHI RAO, SUBIR BISWAS Department of Electrical and Computer Engineering, Michigan State University @yanihi,sbiswas}@egr.msu. edu This paper presents a new transmission power control protocol, CSNE-PC that addresses the energy inefficiency of basic power control protocols (BPC) in which RTS and CTS packets are exchanged at full power while data and acknowledgement transmissions are carried out at low power. This is accomplished by reducing hidden data collisions which is responsible for energy inefficiency in BPC. CSNE-PC implements a mechanism for inference of low-power data transmissions in the presence of power control. A node measures the duration of its carrier-sense activities and analyses it for detecting 802.11 CTS packets. Upon detecting a CTS packet, the node sets or extends its NAV and enters into a silence mode for a pre-determined packet duration. This enables transmission power control without incurring additional data collisions as described above. Evaluation of CSNE-PC through simulation demonstrates that under sustainable loading conditions, reduction in combined transmit and receive energy can be as high as 38%, and that is while retaining the throughput and delay characteristics of the regular 802.11 protocol.

1. Introduction

A number of Transmission Power Control (TPC) schemes have been proposed with the intention of minimizing the energy spent on transmission and potentially increasing spatial channel reuse at the same time [2][3][6][7]. The idea behind TPC is to use an optimal transmission power to reach an intended receiver. In addition to reducing the transmit energy consumption, TPC implies that the reception and the carrier sense zones for packets transmitted at lower power are also reduced. Hence, the number of nodes that overhear [S] and carrier-sense the packets are fewer. Thus, as a second order effect, TPC can lead to a reduction of reception and carrier sensing energy expenditure as well. Problems with Basic Power Control (BPC): In this scheme [4][5][6],transmitter and receiver transmit RTS and CTS at the maximum power level supported by the wireless interface. But data and acknowledgement are sent at the minimum required power level. It can be shown [l]that power control for DATA and ACK packets in BPC can significantly increase packet collisions. As reported in [ 11 and Section 4,such collisions can severely impact the energy performance of BPC. In the example presented in Figure 1, node A transmits an RTS at maximum power and node B responds with a CTS. The circular regions around 173

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the nodes indicate the reception and carrier sensing zones for maximum power transmissions. Nodes in the carrier-sensing (CS) zone of the transmitter and the receiver such as nodes C, E and D sense the RTS/CTS and defer their own transmission as needed. Upon receiving the CTS from B, the transmitter A transmits the DATA at the minimum required power level. In this example, the hexagonal regions around the nodes A and B represent the reception and carriersensing zones for the low-power DATA and ACK transmissions. DATA Collisions: These collisions can be caused by nodes in the carrier-sense zone of the receiver and they are not completely prevented even in the plain 802.1 1 protocol without power control [ 13. However, in BPC, DATA is more prone to collisions due to the following reasons. CS zone for max-

First, consider the example in Figure 1 executing plain 802.11 without power control. When node A transmits DATA to node B, node C and node E sense the transmission and defer their own transmissions for EIFS duration. So, node C and node E do not power transmission cause a DATA collision at node Figure 1. Collision Scenano for Basic Power Control B. However, node D is not in the carrier sense range of node A and hence is not aware of the DATA packet being transmitted from A to B. Therefore, only node D can cause a DATA collision at B. Now, consider the example in Figure 1, when BPC is used. Nodes C and D are outside the low-power carrier-sensing zones of A and B. Therefore, they are not able to sense the low power data transaction between A and B. If either node D or node E has a packet to transmit, it will transmit an RTS at maximum power which may collide with the ongoing data transmission from node A to B. As a result, in BPC, node E can also cause a DATA collision in addition to node D. ACK Collisions: In 802.11 without power control, collisions of ACK packets at the transmitter by the nodes in its carrier sense zone are prevented [ 11. In Figure 1, even though node C is not in the carrier sense zone of node B, it cannot cause an ACK collision at node A. This is because node C had set its NAV for EIFS duration when it sensed the DATA and EIFS is long enough to ensure that the ACK packet is successfully received at the source. Now, consider the case when BPC is used. Node D, E and C sense neither the DATA from A to B, nor the ACK from B to A. Therefore, nodes E and node C can start transmitting

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RTS at maximum power and can cause an ACK collision at A. Both DATA and ACK collisions, as described above, can affect the energy performance of BPC. 2. Related Work

Transmission power control has been incorporated at the MAC layer in two classes of protocols. In the first class, unlike BPC, data, acknowledgement and control packets (CTSRTS) all are transmitted at the minimum required power level [7]. A node uses distinct power levels for transmissions to each of its neighbors. This scheme is prone to collisions due to a special hidden node problem, which is occurs because the reserved floor does not include all the nodes that can potentially interfere with the transmission [2]. Such collisions can reduce the effectiveness of this class of power control protocols. In the second set of protocols, which are designed around the Basic Power Control (BPC) scheme, RTS/CTS control packets are transmitted at the maximum power level while DATA/ACK packets are transmitted at the minimum required power level [4][5][6]. By transmitting RTS/CTS at maximum power, these protocols reserve the same floor for a transmission as in the plain IEEE 802.1 1 without power control. For every packet transmission, RTS/CTS packets are used to determine the minimum required power level for the DATA/ACK transmissions. Protocols that use the above scheme suffer from the collision problem described in Section 1. PCM protocol [l] attempts to avoid these collisions using the following approach. It requires that irrespective of the minimum required power level, the data be transmitted periodically at the maximum power just for a duration that is long enough for the nodes in the carrier sensing region of the transmitter to lock on it. This approach aims to avoid the ACK collisions at the transmitter that are introduced by BPC. In the process, protection of DATA packets also becomes similar to plain 802.11. Note that since the reserved floor is same as that of IEEE 802.11, PCM delivers throughput comparable to plain 802.11.

3. CARRIER-SENSE BASED NAV EXTENSION FOR EOWER CONTROL (CSNE-PC) In this paper we propose an approach that is different from PCM but it attempts to accomplish similar goals. 3.1. Protocol Description

The basic goal of CSNE-PC is to ensure that all nodes in the carrier-sensing (CS) zone of the receiver are able to infer the presence of any low power data transmissions between that transmitter-receiver pair. To achieve this, in CSNEPC, a node measures its surrounding channel activities by monitoring the received control and data packets as well the carrier-sense signals that it cannot

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decipher. Based on the durations of the carrier-sense signals, the node attempts to determine if the carrier-sense matches the duration of an 802.1 1 CTS packet. If the node decides that it has sensed a CTS, it sets or extends its NAV. Consider the scenario depicted in Figure 2, in which node Q sends a maxpower RTS to node P. Node C does not receive or sense the RTS as it is out of the carrier-sensing range of Q. Node P responds to the RTS by transmitting a CTS at maximum power. Note that node C is outside the reception zone but within the CS zone for P’s transmission. Therefore, Node C senses the CTS signal from Node P, but it cannot decipher it. As a result, in the plain 802.11 protocol, no action would have been taken by node C in this situation. I I In CSNE-PC. however, based on the carrier-sense duration, C concludes that the signal is a CTS, and then it sets its NAV for a pre-defined packet duration. Now, Q starts transmitting the data to P at the minimum ACK required power. Even though C is not able to sense or receive DATA Figure 2. Timing-diagram of CSNE-PC from Q, it does not transmit for the DATA duration because of the NAV set based on the carriersense of CTS. Thus, CSNE-PC avoids the DATA collisions that exist in BPC. Now consider the following scenario. As the data is being transmitted from Q to P, node A sends a max-power RTS to B. Node B sends a CTS at maximum power which is not received, but carrier-sensed by node C. Based on the duration of the carrier-sense signal, node C determines that the sensed signal is a CTS, and then it extends the already set NAV for another pre-defined packet duration. This ensures that node C defers its transmission till both Q and A complete their transmissions. This NAV extension in CSNE-PC ensures that node C does not prematurely initiate a transmission and cause collisions with A’s transmission at node B or Q’s transmission at P. Carrier-sense based NAV extension could be based on detection of RTS packets, CTS packets or both. In CSNE-PC, a CTS based NAV extension (and hence DATA collision avoidance) is chosen for the following reasons. First, in 802.11, an RTS transmission does not necessarily result in a data. But, by the time a CTS is transmitted, the first hurdle namely RTS collision has been overcome, and therefore it is logical to assume that a CTS is more likely to be

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followed by data than an RTS. Second, due to their large size difference, the likelihood of a DATA to experience collision at a receiver is much larger than that of an ACK to experience collision at the transmitter. Hence, by performing a CTS based NAV extension we expect to reduce DATA collisions and hence energy consumption. During simulation experiments, NAV extension based only on RTS packets were performed. We found that NAV extension based on RTS impacts throughput adversely as nodes can needlessly defer their transmissions. 3.2. Measurement based CTS Detection

Detection of CTS packets based on carrier-sensing is the key for the CSNEPC protocol. In practice however, it is not always possible to detect all CTS packets based on carrier-sense duration. The reason for this is that often the carrier-sense signals from multiple nodes can overlap at a receiver, and that makes it particularly hard to isolate the leading and trailing edges of individual signals representing CTS packets. Keeping this in mind, we implemented a pattern extraction algorithm that detects a CTS signature when a carrier-sense signal corresponds to a pure CTS without any overlapping signal, or a CTS is the most recent among a set of overlapping signals. Other than transmitting, at any given time an 802.11 wireless interface can be in one of PHY-Idle (Idle), PHY-CS (Carrier-Sense) or PHY-RX (Receive). In order to detect CTS packets from carrier-sense (CS) signals, the wireless interface keeps track of the times at which all state transitions occur. Transition times TidleTocs, K d , e T o , T ~ s Tand ~ ,TcsToIdle are used for the detection of CTS signature. Note that a CS to CS transition happens when the wireless interface is in CS state and a new signal is sensed. The sensing of a new signal can be inferred based on an increase in the strength of the resultant carrier-sense signal. When a state transition occurs from carrier-sensing to idle, the wireless interface checks if (TCSToIdle - TidleToCS) or (TCSToIdle - TidleToRX) or (TCSToldle - TCST~CS) equals the CTS duration (TCTS+ E ) where E is a small value. If the outcome of the condition check is true then a CTS signature is assumed to be detected. Note that duration-based CTS signature detection, as explained above, requires that the CTS packet size be distinct from all other packets in the network. 4. Performance Analysis

The performance of CSNE-PC has been evaluated and compared with plain 802.1 1 and Basic Power Control (BPC) using Qualnet 3.6 network simulator. Simulation Setup: The network topology used for our experiments consisted of 25 nodes randomly placed in an area of 1000x1000 meters. The traffic model is similar to that in [I], where each node generates a 5 12 byte packet destined to

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its nearest neighbor according to a Poisson process. A maximum transmit power of 15db has been used. Receive threshold and carrier-sensing threshold are set to -84db and -94db respectively. The rate at which data is transmitted is 2Mbps. The minimum required transmission power is computed using the procedure described in [ 1,3]. Results: Figure 3 shows the transmission and total communication energy consumption per packet as a function of varying network load. Transmission energy indicates the average energy consumed for delivering a packet successfully. Communication-related energy includes not only transmission energy, but also energy spent on receiving, overhearing, and carrier-sensing. Performance data shown in Figure 3 are obtained by gradually increasing the load till the MAC drop rate becomes unacceptable (exceeds 5%). This cut-off point was found to be 65 Kbps per flow. At low loads, BPC results in modest energy savings that is expected as a result of TPC. However, as the load increases, the energy savings of BPC are overtaken by additional energy expended due to collisions caused by nodes in the CS zone (see Section 1). As a result, for moderate and high load situations, BPC ends up consuming more communication related energy than plain 802.1 1. Note that the energy consumed per packet drops as load increases for 802.11. Experimental data indicate that energy that is spent on overhearing and carriersensing go down as load increases while energy spent on transmission and on receiving packets goes up. Since nodes have more data to transmit as load increases, they tend to (a) Transmit Energy (b) Total cammication Energy spend relatively more Figure 3. Energy consumption as a function of load energy on transmitting and receiving data than carrier-sensing/overhearing remote transmissions. Hence, more packets are delivered, and therefore total energy per packet goes down. CSNE-PC on the other hand consistently consumes up to 24% less transmit energy and up to 38% less total communication energy and that is irrespective of the loading conditions. This savings can be attributed to the reduction of the collisions from nodes in the CS zone. As described in Section 3, in CSNE-PC, nodes in the CS zone detect CTS packets, infer low-power remote transmissions and extend their NAV for the duration of those transmissions. This results in lower number of collisions and hence retransmission costs.

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Figure 4 (a) shows throughput of the three protocols under consideration, and 4 (b) shows the MAC layer drop rate. As the load increases, network throughput for 802.11 increases linearly up to a certain point (about 65kbps per flow in our experiments) and then it starts to saturate. The saturation is a result of increased collisions at MAC layer and packet overflow at the link layer buffer. At higher loads, the ability of BPC to successfully deliver packets erodes as the network spends considerable amount of time on back-offs and packet retransmissions resulting from collisions. Packet drops due to failed retransmissions and 0 50 100 0 20 40 60 Load (Lbps per Bow) Load &bps pa Bow) overflow of the link layer buffers escalate and lead to lower throughput. The MAC layer drop rate shown in Figure 4 (b) further corroborates this reasoning. Throughput of CSNE-PC, on the other hand, is comparable to that of 802.11 at all loading conditions. This is because the floor reserved for a transmission is same for CSNE-PC and 802.11. Packet delivery delays for all three protocols are depicted in Figure 5. Llke the other metrics, delay for BPC is much larger than both 802.11 and CSNE-PC due to higher collisions for the former. Higher collision rates in BPC cause higher delay due to back-offs and retransmissions. In CSNE-PC, Figure 5 . Delivery delay as a function of load limiting the co~lis,on rate helps it to preserve the delay performance of 802.11 and even achieve lower delays at some loads in the vicinity of the point of throughput saturation. For example, at the load of 6Okbps per node, 802.11 has an average delay of 257 ms, whereas CSNE-PC has delay of only 89 ms. 5. Conclusions

In this paper we have proposed and evaluated the performance of a new transmission power control protocol, CSNE-PC. This protocol addresses the energy inefficiency of basic power control protocols (BPC) in which RTS and

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CTS packets are exchanged at full power while data and acknowledgement transmissions are carried out at low power, This is accomplished by reducing data collisions which are responsible for energy inefficiency in BPC. In CSNEPC, DATA collisions are reduced by analyzing carrier-sense signals detected at a node to infer the presence of remote low-power data transmissions, and by using that information to avoid DATA collisions at the receiver. Simulation experiments demonstrate that such a mechanism can result in up to 38% savings in combined transmit and receive energy under a wide range of loading conditions. Also, this energy saving is achieved while maintaining the delay and throughput performance of regular 802.11 MAC protocol. Results also indicate that CSNE-PC can be used in networks with different packet sizes.

References 1. Yo E. Jung and N. Vaidya, “A power control MAC protocol for Ad hoc networks.” ACM International Conference on Mobile Computing and Networking (MobiCom), September 2002. 2. A. Muqattash and M. Krunz, “A distributed transmission power control protocol for mobile ad hoc networks.” IEEE Transactions on Mobile Computing, 2003. 3. J. Monks, V. Bhargavan, and W.M.Hwu, “A power controlled multiple access protocol for wireless packet networks.” In Proceedings of the IEEE INFOCOM Conference, volume 1, pages 2 19-228,2001. 4. M. B. Pursley, H. B. Russell, and S. Wysocarski, “Energy-efficient transmission and routing protocols for wireless multiple-hop networks and spread spectrum radios.” In Proceedings of EUROCOMM Conference, pages 1-5,2000. 5. D. Qiao, S . Choi, A. Jain, and K. G. Shin, “Adaptive Transmit Power Control in IEEE 802.11a Wireless LANs.” In Proceedings of lEEE VTC 2003-Spring, Jeju, Korea, April 22-25,2003. 6. S.-J.Park, R.Sivakumar. “Load sensitive transmission power control in wireless ad hoc networks.” IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, November 2002. 7. S . Agarwal, R.H. Katz, S.V. Krishnamurthy, and S . K. Dao. “Distributed power control in ad-hoc wireless networks.” In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, volume 2, pages 59-66, Oct. 2001. 8. S . Datta and S. Biswas. “Limiting carrier-sense energy consumption by low-power interface idling in 802.11 protocols.” In proceedings of IEEE ICWN, Phoenix, Arizona, April, 2004.

CHAOS SHIFT KEYING AND IEEE 802.11a G . PLITSIS Aachen University of Technologv, Germany Chaos engineering for mobile communications is an emerging field of research. Chaosbased modulation schemes such as Chaos Shift Keying (CSK) and Differential Chaos Shift Keying (DCSK) have been proposed as alternatives to conventional digital communication systems. This paper introduces a way to apply CSK in broadband wireless access systems such as IEEE 802.1 la. As well as this, it provides in brief the advantages and disadvantages of such a system. Results concerning spectrum are provided with the use of a link-level simulator with basic functions.

1. Introduction IEEE standardized 802.1 l a [I] with an aim to provide a combination of high bit rates and mobility. Nomadic users can have with the use of IEEE 802.1l a wireless access to the core network while in business, at work, or at home. Chaos-based mobile communications is an emerging field of research that also tries to integrate chaos-based modulation schemes in conventional ones. As well as this, investigations are being made to take advantage of chaotic oscillations for spread spectrum communications. The idea of using chaotic signals for digital communications has been triggered and inspired by L.M. Pecora (Pecora and Carroll 1990) that observed that two identical chaotic electronic circuits that start with different initial conditions can synchronize. Different modulation schemes based on chaos have been proposed during the last years. Some of them such as the Differential Chaos Shift Keying (DCSK) do not use the property of synchronization and transmit reference as well as modulated signals. Others such as Chaos Shift Keying rely on the property of synchronization in order to transmit information but are susceptible to channel noise. 2. IEEE wireless LAN overview

Wireless Local Area Networks (WLANs) is an existent technology in many sectors of our everyday life. While in the office, the production line, in hospital, warehouse etc., WLAN technology provides mobile users with high-bit rate data. The IEEE 802.1 1 family embraces many different protocols such as the 181

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802.11a [l]. It is optimized for a coverage area of about lOOm with a typical transmitter power of 100mW. It was first designed for quasi-static network deployment and is basically centralized. It typically uses Orthogonal Frequency Division Multiplexing (OFDM), Direct Sequence Spread Spectrum (DS-SS), Carrier Sense Multiple Access Collision Avoidance ( C S W C A ) Medium Access Control (MAC) layer scheme, wireless Ethernet, and star topology with an Access Point (AP).By CSMNCA, collisions can be avoided by sending a short “ready to send” message that tells the other nodes the destination and duration of the message that is about to be sent. HiperLAN/2 is the ETSI proposal for broadband wireless access systems, which has some advantages over the 802.1l a such as Quality of Service (QoS) guarantee, greater spectrum efficiency as well as lower interference but came late in the market and thus did not have the expected success. Chaotic modulation schemes aim to spread the transmitted signals in order to reduce the power spectral density and as result to minimize interference with other radio communications operating in the same frequency as well as overcome the problem of multipath propagation. 3. Chaotic modulation schemes

A Chaos Shift Keying (CSK) communications system typically contains chaos generators for the data sequences and some filters and equalizers for the modulation and demodulation process. The complexity of the hardware [6] needed for the implementation of such a device is much less than that required by conventional systems. Nevertheless, no synchronization methods have been yet found. Chaos

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Multiple-access CSK can be created by either slightly changing the initial conditions (perturbation-based) or by using a different chaotic map for each user (e.g. logistic, quadratic, exponent, bernoulli, sine circle, tent, henon, lozi, baker, etc.) A theoretical example of perturbation is the amplitude-phase perturbation for the pendulum equation. Considering, (1) X " + x = E ( Y C 0 S Z - Mc' - @ + x ' ) It can be shown that the solution is equal to X(&,Z)

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4. The simulation model using chaos generators

The simplified simulation model consists of a Base Station (BS) as well as Mobile Terminals (MTs) that have chaos generators. Data is broadcast to MTs and once the MTs are synchronized, they can communicate with each other and with the BS. For synchronization purposes, VCOs and PLLs are used. The channel is modeled as AWGN [3]. The chaos generator can use different maps for different users such as cubic map, skew tent map, logistic map, and bernoulli-shift map given by, Xk+,

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5. Spectrum results

The results that appear in the following graphs present the spectrum provided by link level simulations. As far as the advantages and disadvantages of a system based on the link level simulator with chaos generators is concerned, its hardware implementation is easy as well as cheap and it provides high encryption but the synchronization of CSK is still an open question and the Bit Error Ratio (BER) of such a system is inferior to that of the conventional. On

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the other hand, DCSK provides easy synchronization but with a lower data bitrate when compared to CSK.

Figure 5. Rx power spectrum of 802.1 la PHY (simulation results).

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6. Conclusions

Chaotic modulation schemes have been described and have been proposed ways to be used in contemporary systems. A brief overview of IEEE 802.1 l a has been provided as well as a comparison with chaos-based communication systems. Possible suggestions for W h e r research could be the invention of a synchronization mechanism for CSK and its hardware implementation as well as the circuit design of the various VCOs and PLLs.

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References

1. IEEE Std 802.11a-1999, “Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: High-speed Physical Layer in the 5 GHz Band,” ISO/IEC 8802-11:1999/Amd 1:2OOO(E). 2. G. Burel and X. Sammut, “Digital Transmissions with Chaotic Signals: Fast Receiver Synchronization Using Duplicated Chaotic Oscillators,” ISPACS 2000, Honolulu, USA. 3. M. Hasler and T. Schimming, “Chaos communication over noisy channels,” Int. J. Bifurc. Chaos, vol. 10, pp. 719-735,2000. 4. IHP, “Protocol Stack for the IEEE 802.1l a MAC Protocol”, Datasheet, Nov. 2002. 5. A.S. Dmitriev, M. Hasler, A.I. Panas, and K.V. Zakharchenko, “Basic Principles of Direct Chaotic Communications,” Nonlinear Phenomena in Complex Systems, 4: 1 (2002) 1 - 2. 6 . G. Kolumban, J. Schweizer, J . Ennitis, H. Dedieu, B. Vizvari, “Performance evaluation and comparison of chaos communication schemes,” Proc. NDES‘96, pp. 105-110, Sevilla, Spain, June 1996. 7. A.S. Dmitriev, B.Ye. Kyarginsky, A.I. Panas, and S.O. Starkov, “Direct Chaotic Communication Schemes in Microwave Band,” Radiotehnika I Elektronika. 46, no. 2,2001 224-233. 8. A. Dmitriev, B. Kyarginsky, A. Panas, and S. Starkov, “Direct Chaotic Communication System Experiments,” Proc. of NDES’OI, Netherlands, 2001 157-160. 9. F. C. M. Lau, M. M. Yip, C. K. Tse, and S. F. Hau, “A multiple access technique for differential chaos shift keying,” IEEE Trans. Circuits Syst. I, vol. 49, no. 1, pp. 96-104, Jan. 2002. 10. M. Hasler, G. Mazzini, M. Ogorzalek, R. Rovatti, and G. Setti (Eds.), “Special issue on applications of nonlinear dynamics to electronics and information engineering,” Proc. IEEE, vol. 90, no. 5, May 2002. 11. M. Sushchik, L. S. Tsimring, and A. R. Volkovskii, “Performance Analysis of Correlation-Based Communication Schemes Utilizing Chaos,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory andApplications, Vol. 47, No. 12, Dec. 2000. 12. N. F. Rulkov, M. M. Sushchik, L. S. Tsimring, and A. R. Volkovskii, “Digital Communication Using Chaotic-Pulse-Position Modulation,” IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, Vol. 48, No. 12, Dec. 200 1.

EFFECT OF TIME-CORRELATED ERRORS ON POWER SAVING MECHANISMS FOR IEEE 802.11 INFRASTRUCTURE NETWORKS G.A. SAFDAR & W. G. SCANLON School of Electrical and Electronic Engineering, The Queens University of Belfast, Ashby Building, Stranmillis Road, Belfast, Northern Ireland, BT9 5AH, UK The performance of energy-saving protocols for IEEE 802.1 1 wireless local area networks was analysed under time-correlated error conditions. Using OPNET, simulations were performed to compare the performance of the infrastructure power saving mode of IEEE 802.1 1 (PCF-PS) with our proprietary protocol, pointer controlled slot allocation and resynchronisation protocol (PCSAR). The results demonstrate a significant improvement in energy efficiency without significant reduction in performance when using PCSAR. For a wireless network consisting of an access point and 8 power saving stations, energy consumption was up to 28 % lower with PCSAR compared to PCF-PS. The results also show that PCSAR offers significantly reduced uplink access delay over PCF-PS, while modestly improving uplink throughput.

1. Introduction

Energy consumption is a major performance metric for wireless communication networks that support portable devices. Mobile nodes may be in doze, transmit or receive states, and this determines their communication-related energy consumption [ 11. Likewise, system architecture and philosophy also have a role to play in energy efficiency [2]. Considerable research work has been published concerning energy conservation techniques at various layers of the protocol stack, including work on MAC [3], routing [4] and transport control protocols [5]. Regardless of the target standard (e.g., IEEE 802.1 l), a number of general principles have been established. For example, collisions should be eliminated as much as possible within the MAC layer [6] since they result in frame retransmissions and unnecessary power consumption. Likewise, transmission power management is known to improve energy saving in wireless networks and also helps to avoid collisions [7]. The removal of additional overheads [8] also results in improved energy saving by reducing the data transmission time, while for centralised networks higher energy saving may be achieved by avoiding address listening using reservation-based channel access techniques ~91. 189

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In this paper, the performance of a centralised access protocol, Pointer Controlled Slot Allocation and Resynchronisation (PCSAR), is compared under time correlated channel error conditions with the standard point coordination function (PCF) power saving mode (PCF-PS) of IEEE 802.11. PCF is a centralised MAC protocol for infrastructure networks, while PCSAR is a combination of the pointer-based channel access mechanism of [9] and PCF-PS. PCSAR was developed by the authors and has been described in detail elsewhere [lo]. PCSAR achieves a further reduction in energy consumption over PCF-PS by increasing the opportunities for mobile stations to power down their transceiver components. This paper is organised as follows; the main features of PCF-PS and PCSAR are summarised in Section 2. Section 3 describes the simulation setup with the results presented and discussed in section 4. Section 5 concludes the paper and gives suggestions for hture work. 2. PCF-PS and PCSAR Protocols For IEEE 802.11 wireless networks [ 113, infrastructure modes such as PCF-PS provide the greatest opportunity for energy saving. In PCF-PS downlink (DL) traffic destined for power saving (PS) mobile stations is buffered at the access point (AP) allowing stations to save energy by remaining in sleep mode. Frame transfer between the Ap and PS stations under channel error conditions is shown in Figure l(a). PCSAR is a contention-free protocol where each station is assigned at least one DL transmission opportunity within the contention-free period (CFP), normally followed immediately by a corresponding UL opportunity. Each PCSAR beacon and every DL frame includes a next slot pointer (NSP) and resynchronisation slot pointer (RSP) used for slot allocation and association with the picocell respectively, Figure l(b) shows frame by frame scheduling in PCSAR by making use of NSP. In PCSAR, any incoming station wishing to join the wireless picocell extracts the RSP from any DL frame and contends during the CP period for association. Although the implementation considered in this paper is symmetric (with one UL frame following each DL frame), PCSAR can support adaptive asymmetrical operation in the DL direction. More details about these pointers and the architecture of the protocol and can be found in [lo].

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(a) LTN = Channel Listening, SLP = Sleep, err = Error Packet BEACON

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Figure 1: Timing Diagram for 4 PS stations with channel errors (a) PCF-PS, (b) PCSAR

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3. Simulation Details

Using OPNET*, PCSAR was compared with PCF-PS for an IEEE 802.11b network of 8 PS stations and an AP with symmetrical DL and UL traffic conditions. A time-correlated channel error model [ 121 was used to simulate non-ideal transmission conditions. The success or failure of each frame was determined by using the standard OPNET error allocation algorithm. Simulation models were developed for both PCF-PS and PCSAR and the key parameters are given in Table I . Table 1: Main simulation parameters.

I

Parameter

I

Value

I

Slot Time SIFS Time PIFS

I

20 us

Data Rate Layer Three Mean Packet Size Layer Two Payload MAC Frame Time CP Beacon Time CF-END Time PS-POLL Time Centre Frequency Bandwidth Max Doppler Frequency Station Velocity Transmit Power

I

I

I I ~~~

10 ps Slot Time + SIFS Time 11 Mbos 11000bits 7008 bits 36 * Slot Time MAC Frame Time + SIFS Time 3 * Slot Time 3 * Slot Time 2 * Slot Time 2.45 GHz 22 MHz 30 Hz (0.5 - 1.O) m/s -10 dBm

I

In this work, the AP carried all DL traffic and each station had an independent UL traffic source. Network-layer data was simulated by using bursty traffic sources with an exponential inter-arrival rate and a uniformly distributed packet size. To investigate the performance of the network under increasingly poor channel conditions, the number of stations experiencing channel errors was increased from 1 to 8, with all other stations operating error-free. The traffic load on each flow (8 UL and 8 DL) was maintained at 60 % for all of the simulations, the load being specified relative to the useful data capacity of the *

Opnet Technologies, Inc., Bethesda, MD.

193

network evenly allocated across the flows. The useful data capacity was determined by considering the possible network layer data transfer between the transmission of two consecutive beacon frames. Segmentation and reassembly was used in both UL and DL due to the bursty nature of the traffic generated by the traffic sources. In all of the simulations in this paper, the length of the CFP was variable for PCF-PS as it depended on the status of the TIM (i.e., DL traffic) at the time of the beacon transmission, where as CFP was fixed at 128 slots (187.77 ms) for PCSAR. 4. Results and Discussion 4.1. DL and UL Throughput DL throughput, which includes frame retransmissions, was found to be almost constant at 485 kbps for both PCF-PS and PCSAR. Figure 2 shows how the number of stations experiencing channel errors affected the average number of retransmissions, on time, off time in PCF-PS and poll frames in PCSAR respectively. UL throughput for PCSAR and PCF-PS also remained fairly constant (at 472 Kbps for PCF-PS and 500 Kbps for PCSAR, an improvement of 6.2 %). This was due to the bandwidth that was taken by NULL frames being released and reallocated to UL retransmissions for both PCSAR and PCF-PS cases (Figure 3). 1200

800

01

400

4

5

Error Stations — PCF-PS OnJTime — PCSAR_Poll_Frames - PCF-PS DL Re - Tx Frames

PCF-PS Off_Time PCSAR_DL_Re - Tx Frames

Figure 2: Average DL retransmissions, on / off Time and Poll frames for PCF-PS & PCSAR

194

600

E

E

400

200

0

1

3

2

4

5

I

6

8

Error Stations

-PCF-PS-NULL-Frames - - - PCSAR-NULL-Frames PCSAR-UL-Re - Tx-Frames -PCF-PS-UL-Re - Tx-Frames Figure 3: Average UL retransmissions and NULL frames.

4.2. Access Delay

Figure 4 shows the mean access delay calculated across all 8 stations under channel error conditions. DL access delay with PCSAR was significantly higher (60 % averaged for all stations) than PCF-PS due to the fixed CFP duration of PCSAR. However, PCF-PS DL access delay increased at a much higher rate than PCSAR with the increased number of error stations in the picocell. Furthermore, even under channel error conditions, PCSAR mean UL access delay results were on average 91 % lower than with PCF-PS. 10

..... -.

1

0.01 J -

1

-

__ 2

-

-

3

5

4

6

I

_ 7

8

Error Stations

L-PCSAR-DL - -PCSAR-UL

--

'PCF-PS-DL -PCF-PS-UL

Figure 4:DL and UL Access Delay

1

195

4.3. Energy Consumption and Transmission Efficiency

The energy consumption was calculated for both protocols (Figure 5), assuming 290 mA for transmitting, 205 mA for receiving and 62 mA for sleep or doze mode (Prism 2.5, 3.3 volts IEEE 802.1l b network interface card). Regardless of the number of error stations in the picocell, average PCSAR station energy consumption was significantly lower than for PCF-PS. Averaged over all scenarios, PCSAR requires 26 % less energy than PCF-PS. With an increased number of error stations in the picocell, the difference in energy consumption for PCF-PS and PCSAR also increases, e g , averaged over all the 8 stations having channel errors, PCF-PS required 28 % more energy than PCSAR.

2

w

I

100

0 1 1

I

2

3

4

5

6

7

8

Error Stations

I-PCSAR - -PCF-PSI Figure 5: PCSAR and PCF-PS station energy consumption.

5. Conclusion

PCSAR makes use of pointers to increase the degree of control exercised by the AP and reduces energy consumption by removing the need for power saving stations to remain awake and listen to the channel until their transaction with the AP is completed. The results presented demonstrate that PCSAR has consistently improved performance over PCF-PS in terms of energy consumption and transmission efficiency. Uplink throughput and delay performance was much better with PCSAR, regardless of traffic or channel conditions. However, downlink performance of PCSAR was relatively poor, particularly under light traffic and reduced number of stations with errors. PCSAR has the potential to be an extremely effective low power protocol for

196

wireless data networks. However, it remains that further work should address the downlink performance issues, asymmetric operation and improved quality of service performance through intelligent frame scheduling

Acknowledgement G . A. Safdar would like to acknowledge and thank the Association of Commonwealth Universities for providing funding for this work. References 1. Christine EJ, Sivalingam K, Agrawal P, Chen J. A survey of energy efficient protocols for wireless networks. Wireless Networks 200 1; 7: 343358. 2. Hadjiyiannis G , Chandrakasan A, Devdas S. A low power low bandwidth protocol for remote wireless Terminals. Wireless Networks 1998; 4: 3-15. 3. Chockalingarn A, Zorzi M. Energy Efficiency of Media Access protocols for mobile data networks. IEEE Trans. Communications 1998; 46: 14181421. 4. Xu Y, Heidemann J, Estrin D. Geography informed energy conservation for ad hoc routing. Proc. ACM Intl. Conf: mobile computing and networking 2001: 70-84. 5 . Karvets R, Krishnan P. Application driven power management for mobile communication. WirelessNetworks 2000; 6: 263-277. 6. Sivalingam K, Chen J, Agrawal P, Srivastava M. Design and Analysis of low-power access protocols for wireless and mobile ATM networks. Wireless Networks 2000; 6: 73-87. 7. Ebert JP, Stremmel B, Wiederholt E, Wolisz A. An Energy efficient power control approach for wireless LANs. Jnl. of Communication and Networks 2000; 2: 197-206. 8. Jung ES, Vaidya N. A Power Saving MAC protocol for Wireless Networks. Technical Report, Texas A & M University, July 2002. 9. Chui TY, Scanlon WG. A Novel MAC protocol for power efficient short range wireless Networking. IEEE Intl. Con$ on Wireless LANs and Home Networks 2001: 187-196. 10. Safdar GA, Scanlon WG. Pointer Controlled power saving medium access control protocol for IEEE 802.1 1 Infrastructure Networks. Proc. IEEE PIMRC 2004: Barcelona. 11. Gast M, Gast MS. 802.11 Wireless Networks: The Definitive Guide. O’Reilly Networking: 2002. 12. Punnoose R, Nikitin P, Stancil D. Efficient Simulation of Ricean Fading within the Packet Simulator. IEEE VTC. 2000: 764-767.

COMPASS: DECENTRALIZED MANAGEMENT AND ACCESS CONTROL FOR WLANS ARTUR HECKER heckerawave-storm.com, Wavestorm SARL 37-39 rue Dareau. 75014 Paris, France ERIK-OLIVER BLASS erik@erik-blass. de Karlsruhe, Germany HOUDA LABIOD labiod@enstjir, GET-Tklkcom Paris, LTCI-UMR 5141 CNRS, ENST 46 rue Barrault, 75013 Paris, France In this paper, we propose COMPASS, a new decentralized access control architecture for modem WLANs. As traditional centralized access control systems like AAA do not scale well, we propose the use of P2P technologies for the distribution of management data directly between the deployed WLAN access points. Our system COMPASS does not require any additional equipment or central entities. Using auto-organization and fault recovery mechanisms of modem P2P systems, it is robust and easier to maintain. Standard 802.IX mechanisms on the user link guarantee compatibility to the existing user equipment.

1. Introduction Wireless local area networks as defined by the IEEE 802.1 1 standard [ l ] experience a tremendous popularity. However, some deployment hesitation has been observed in the industry which is believed to be due to the security and management issues with such installations. Modern 802.1 1 security is based on the IEEE 802.1X standard [2]. 802.1X defines an extensible authentication framework using IETF’s Authentication, Authorization and Accounting (AAA) protocols [3][4]. Thus, 802.1X is typically used with a central AAA server. 802.1X is the chosen base technology for the expected IEEE 802.1 l i standard [ 5 ] . 197

198

The scalability, robustness, and cost of centralized solutions like AAA become an issue in the broad installations of WLANs ranging from small private networks to big, multi-site WLANs. Indeed, the AAA approach results in a centralized server farm which is either over or under-dimensioned for the most network sizes. In that sense, it does not support an natural evolutionary growth of the installed network. Also, from the robustness perspective, it introduces a single point of failure (SPF). These two points can be partly resolved by introducing redundant servers. This however results in a more complicated, costly and difficult to manage AAA infrastructure. Besides, given the low access point (AP) prices in the 802.11 segment, the cost of the central server and its maintenance can hardly be amortized in a typical WLAN (up to 30 APs). However, 802.1X does not oblige the use of AAA. In this paper, we propose to replace the central AAA server through a distributed P2P based access control architecture in the wired network built directly by the access points. We thus present our Configuration Management P2P-based Access Security System (COMPASS). It integrates P2P technology with the 802.1X access control and does not require any central entity*. It relies on modem P2P technologies which provide highly scalable, efficient and fault-tolerant distributed data retrieval mechanisms [6][7][8]. The rest of the paper is organized as follows. In the next section, we present our distributed access control architecture and discuss several important points like self-organization and user management. Then, we give a qualitative comparison and an outlook to the future work. 2. COMPASS: Our New Proposed Architecture

2.1. Main Idea To reduce costs; we propose to use the access points directly for the storage of data relevant to access control and network management. However, since the resources of access points are limited, the idea is to distribute the administrative load over all access points. Thus, in our proposal every AP holds only a part of the whole management database. The difficult part is to provide a scalable and robust mechanism for distributed data retrieval. Herein, the problem is not the data transfer but locating the data [ 6 ] .Distributed Hash Tables (DHTs) [6][7][8] have been designed to overcome these difficulties. While a radius connects a point on the circumference to the center, a compass directly interconnects the circle points.

199

A DHT is a hash table divided into multiple parts (called zones) and distributed over several nodes. Famous DHT examples are file-sharinglP2P networks such as EDonkey2000 or Kazaa. We have to choose a P2P system tailored to the restricted resources of an ordinary AP. We namely compare CAN [ 6 ] , Chord [7] and Pastry [8]. Without going into structural or design details of these networks, here we present a short overview of properties that seem crucial for our work. Table 1 shows properties of common DHTs for a network with n nodes. ‘‘#Hops for lookup/store” is the expected number of nodes a request for a lookup or store has to pass. The second criterion is the number of elements each node has to store in its neighbor or routing table. Both properties are expected values for a well-balanced DHT network.

Table 1: #Hops for lookup/store #Elements in routing table Used in

O(n”d)

O(ln n )

2d

In n

B*ln n

Secure Service Directory

CFS-file-system

Oceanstore, Scribe

Chord and Pastry seem to perform better regarding the average path length. This means less communication overhead and shorter latencies. On the other hand, CAN’s advantage is a constant O(1) memory consumption which is known at node setup time, prior to network use. For that reason, we choose CAN. 2.2. Basic System Architecture Based on standard TCPIIP networking in the core network, our P2P management network is formed of the deployed 802.1 1 access points. This is illustrated in Figure 1. Every access point acts as a P2P node building a logical overlay network over the physical core network. This overlay stores different logical databases, primarily user and management databases (DB). The user DB stores AAA-like user profiles. The management DB helps the administrator manage all connected APs and stores AP settings expressed in the respective syntax (e.g. 802.1 1 MIB variables, proprietary manufacturer settings, etc). On user demand, the solicited node retrieves the correspondent profile by using overlay’s lookup method. Using the retrieved profile the serving AP follows the usual 802.1X procedure acting as Authenticator with a local Authentication Server [2].

200

Access Point

Access Point

Figure 1 Main entities in COMPASS

2.3. Preliminary AP Configuration

Each AP needs a minimum configuration prior to its deployment in the network. This is necessary for a secure management access to this AP, the overlay discovery and the classical 802.1 1 settings. The trust relationship with the AP is expressed by the installation of a signed certificate on every AP. In addition to the usual 802.1 1 parameters, the administrator supplies the bootstrap-address of the overlay network and deploys the AP in the desired location. 2.4. Bootstrapping (AP join)

The original CAN proposal [6] makes use of a bootstrap method based on a well-known DNS address. In CAN this method guarantees a uniform partitioning of the index space. However, it also means that a physical neighborhood does not result in CAN neighborhood. We want to be able to tie the overlay to the network topology. This can potentially shorten the handoff-delay since the new AP can rapidly retrieve the profile data from the old AP using the overlay (since the old AP is also the overlay neighbor). The other reason for reflecting the physical topology in the overlay is a transparent load balancing: we suppose that the administrator installs an additional AP in the neighborhood of every AP which suffers a heavy traffic. If the APs are CAN neighbors, they also share the administrative load. Otherwise

201

they only share the 802.11 traffic load. CAN’s landmark-ordering method to reflect the physical network topology to the overlay explicitly targets the IP layer topology. We define a 802.11 -adapted landmark-ordering mechanism: 1. Booting up, a preconfigured AP searches the 802.1 1 environment for 802.11 neighbor APs. The necessary mechanisms are defined in the 802.11 standard and include an active and passive discovery of neighboring APs of the same SSID [l]. The joining AP compiles a list of (wireless) MAC addresses of all neighboring APs configured with the same SSID. 2. The joining AP now sends a discovery request to the predefined DNS address of the overlay. The request contains the MAC address list (step 1). If it is not empty, the solicited bootstrapping node chooses from it the node whose zone is the biggest. It is essential to provide a mechanism resolving a wireless link MAC address into the management IP address without global network knowledge. We achieve this by storing these data into the overlay itself, but other mechanisms could be applicable as well. Thus, the solicited node executes a lookup in the overlay for every listed MAC address. The returned value is a pair ( IP-address, zonesize).The solicited node chooses the pair with the biggest zone size. If the received wireless MAC address list is empty, the bootstrapping node proceeds as in CAN randomly choosing a node. In either case it replies with the IP address of the chosen node. 3. The join procedure itself is like in CAN. After the joining procedure, the new AP executes a store command in the overlay, posting its own wireless MAC address, the management IP address and the zone size. Each AP is responsible for the validity of that entry. Following this scheme, the new installed AP automatically becomes an overlay neighbor of one of its 802.11 neighbors. The advantages of this scheme are an equal pre-configuration of all APs and the requested binding of the overlay to the physical topology. Our method does not affect the scalability since the preconfigured overlay address can correspond to several APs. This can be achieved with a round robin DNS or with a multicast address (e.g. “all overlay APs”). Moreover, the join events are expected to be much rarer than the operational procedures like user access. During an initial deployment (or after a complete system failure), no node is available under the bootstrap address. This case is rarer than join events and needs a special treatment. It can be resolved by weakening the equality of the AP configuration (choose some nodes for join requests) or by dynamically updating the round robin DNS on new node joins and departures. Such mechanisms are however out of scope of this document.

202

2.5. APLeave

AP leave events can occur because of an AP failure (e.g. power down) or because of a admin shutdown. If an AP is shut down correctly, it proceeds like a CAN node. In a case of a power down, the zone databases held by this AP are lost. CAN redundancy mechanisms are used in that case. 2.6. User A d d User Delete

To add a new user record to the system, the network administrator executes the commands store (username, profile) or delete (username) on one of the nodes. Herein, the profile is a list of authorizations. Principally, such profile could be in an arbitrary suitable format (e.g. attribute value pairs). The profile defines the authentication method, the restrictions and session parameters A typical profile hardly ever exceeds 1Om.

2.7. User Network Access When a user accesses a COMPASS network, user’s mobile station (MS) and the solicited AP start the typical 802.1X authentication process. Within this process, at some point of time MS sends an EAP Response/Identity message containing the identity string. The solicited AP retrieves the corresponding user profile from the overlay by invoking an overlay lookup for this string as a key. On the receipt of the profile, the AP can continue the EAP conversation as defined in the profile acting as a 802.1X authenticator with a local 802.1X authentication server (AS). The identity used by the AP is an abstract identity of the whole overlay which acts as one logical entity. 2.8. Failure Management and Optimizations

We distinguish two major possible failures: the path failure (i.e. some of the nodes in the lookuplstore path fail) and the end node failure (i.e. the zone database is not available). CAN provides mechanisms to counter the impacts of such failures [ 6 ] . In our particular application the path failure can be countered by increasing the number of CAN’s dimensions d. That increases the number of tried paths. If the overlay stores only one DB copy, the failures of nodes holding a zone database result in associated data not being available. CAN provides data replication methods. By using multiple realities [6] or multiple different hash functions, the same zone database can exist on multiple nodes. We encourage the use of such mechanisms for user DB.

203

We demonstrate this at an example of CAN with k realities [6]. Let f of n nodes fail. Assuming that at least one overlay path still works, the probability that a stored pair can be retrieved is:

Example: in a CAN with 4 realities, if % of all nodes fail without path failures, there is still 99.6% probability that an authorized user can use the service.

3. Discussion CAN technology has been recently used in sensor networks [9]. Compared to sensors, the WLAN APs are powerful machines with about 16-32MB RAM and a CPU of about 15OMHz. Recent APs with an embedded Linux 0s show that the available resource safety margin is sufficiently large for additional tasks. tABLE 2 cOMPARATIVE CHART OF USER MANAGEMENT METHODS IN MODERN wlanS

with onc DB

As mentioned, CAN has constant memory requirements. The management database stores settings valid for every AP. Its size is thus independent of the number of APs. Given a typical profile size of about 1-lOkB, 30-100 user profiles per AP can principally be stored without any impact on AP performance. User profile size can be further reduced by using group management. Redundancy mechanisms can not be reasonably used when the overall number of APs is very low (say for up to 5-10 APs). When using redundancy mechanisms, the overall database size has to be multiplied by the redundancy factor k. For instance, in a network with 10 APs, 100 user profiles and the redundancy factor

204

k 2 , the zone database on each AP is about 5kB*100*2 f 10 = 1MB. These values seem to be realistic requirements for modem APs. In Table 2, we compare different access control architectures in terms of user mobility, administration effort, network extensibility and robustness (expressed by the worst case impact of a partial system failure). 4. Conclusion

In this paper, we propose to integrate P2P technology with access control to provide a system supporting a natural scaling of the management infrastructure. As a P2P architecture, COMPASS inherits the scalability and fault tolerance of the existing P2P technologies. COMPASS is backwards compatible by using standard access methods on the user link and AAA-like user management in the core. It features an easy network extensibility by defining automatic node join. By storing the necessary management settings in the overlay, COMPASS can also be used as network management infrastructure. References

[I] IEEE Standard 802.1 1, “Wireless LAN medium access control (MAC) and physical layer (PHY) specifications,” 1999. [2] IEEE Standard 802.1X, “Port-based network access control,” June 2001. [ 3 ] C. Rigney, S. Willens, A. Rubens, W. Simpson, “Remote Authentication Dial-In User Service (RADIUS),” RFC 2865, IETF, June 2000. [4] P. Calhoun, J. Loughney, E. Guttman, G. Zon, J. Arkko, “Diameter Base Protocol”, RFC 3588, IETF, September 2003. [ 5 ] IEEE Draft 802.1li, “Draft supplement to IEEE Std 802.1 1. part 11: specifications for enhanced security”, work in progress. [6] S. Ratnasamy, P. Francis, M. Handley, R. Karp, S. Shenker, “A Scalable Content-Addressable Network , Proceedings of the 200 1 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. [7] I. Stoica et al., “Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications”, Proceedings of the 2001 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. [ S ] A. Rowstron, P. Druschel, “Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems”, Lecture Notes in Computer Science, 200 1. [9] H.-J. Hof, E.-0. Bla13, T. Fuhrmann and M. Zitterbart, “Design of a Secure Distributed Service Directory for Wireless Sensornetworks”, EWSN 2004, Berlin.

QOS PROVISIONING MECHANISMS FOR IEEE 802.11 WLANS: A PERFORMANCE EVALUATION* JOSE VILLALON Instituto de Investigacidn en Informbtica, Universidad de Castilla La Mancha, Albacete, 02071, Spain

PEDRO CUENCA Instituto de Investigacibn en Informbtica, Universidad de Castilla La Mancha. Albacete, 02071, Spain

LUIS OROZCO-BARBOSA Instituto de Investigacidn en Informcitica, Universidad de Castilla La Mancha, Albacete, 02071, Spain The IEEE 802.1 1 wireless LAN (WLAN) is the most widely used standard nowadays for wireless LAN technology. However, the current standard does not provide QoS support for multimedia communications. Thus, a large number of enhancements to the standard are being proposed. This paper introduces some of the most relevant schemes: the Priority-Based Distributed Mechanisms. We also present the upcoming IEEE 802.1 l e (EDCA) standard being introduced. The IEEE 802.11e standard is a proposal defining the mechanisms for wireless LANs aiming to provide QoS support to multimedia communications. In this paper, we carry out a comparative study of Priority-Based Distributed Mechanisms and the 802.1 l e (EDCA) upcoming standard, when supporting different services, such as voice, video, best-effort and background traffic.

1. Introduction WLANs are gaining popularity at an unprecedented rate, at home, at work, and in public hot spot locations mainly due to their low cost, their ease of deployment and, above all, by allowing the end users to freely move around withm the area they cover. Another influential factor is the appearance in 1997 of the standard IEEE 802.11, with its subsequent revision in 1999 [l], and its subsequent amendments that nowadays enable transmission speeds of up to 54 Mbps, allowing the use of multimedia applications. ~

~~

This work was supported by the Ministry of Science and Technology of Spain under CICYT project TIC2003-08154-CO6-02 and the Council of Science and Technology of Castilla-La Mancha under project PBC-03-001.

205

206

The basic access function in IEEE 802.11 is the Distributed Coordination Function (DCF) where before transmitting, a station, the source station, must determine the state of the channel. If during an interval of time, called Distributed InterFrame Space (DIFS), the channel is sensed free, the station can initiate its transmission. If the channel is sensed busy, once the transmission in progress is finished and to avoid the collision with other stations in the same situation, a backoff algorithm is initiated. This algorithm consists in choosing an interval of time (the backoff time) at random during which the station delays the transmission of its frames. Once having transmitted the source station, it will wait to get back a reply from the destination station. If after waiting for a time interval, denominated Short InterFrame Space (SIFS

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