Communications and Networking in China: 1st International Business Conference, Chinacombiz 2008, Hangzhou China, August 2008, Revised Selected Papers (Communications ... in Computer and Information Science)

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Communications in Computer and Information Science

26

Patrick Bond (Ed.)

Communications and Networking in China 1st International Business Conference, ChinacomBiz 2008 Hangzhou, China, August 2008 Revised Selected Papers

13

Volume Editor Patrick Bond ICST, Begijnhoflaan 93a, 9000 Gent, Belgium E-mail: [email protected]

Library of Congress Control Number: 2009920523 CR Subject Classification (1998): C.2, J.1, B.4 ISSN ISBN-10 ISBN-13

1865-0929 3-642-00204-8 Springer Berlin Heidelberg New York 978-3-642-00204-5 Springer Berlin Heidelberg New York

The copyright of the content of this book is governed by the Belgian Copyright Act (30 Jun 1994). With the exceptions stated explicitly by the Act, no part of the content of the book is allowed to be translated, modified, copied, stored in a database or electronic retrieval system or made public without the prior written consent of ICST. © ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009 Printed in Germany springer.com Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12607983 06/3180 543210

Preface

ChinacomBiz 2008 was the first international business-to-business (B2B) conference held in collaboration with the Chinacom scientific event in Hangzhou, China, on August 28. It was specifically tailored to produce more effective dialogue between the research, technology and business communities on technical developments in China and around the world. The event had industry support via well-know industry names such as the WIFI Alliance, Springer, CREATE-NET and Arris. The main focus of the event was on transport networks and infrastructures, video distribution systems and methods and the associated software systems that tie them all together. A total of 24 authors submitted their papers and a final 11 registrations were accepted. It was an excellent start to the B2B program and feedback was positive from all attendees. The presentations this year originated from the USA, Denmark, Germany, Brazil and of course China, providing the audience with a wide variety of topics and perspectives in all the three categories mentioned above. The presentations from the event are available for download from the website itself (see http://www.chinacombiz.org/). Next year’s event is already being planned and it will again be collocated with the Chinacom scientific event. We look forward to having another great interaction with science, business and technology from a China perspective. August 2008

Patrick Bond

Table of Contents

Undetectable Manipulation of CRC Checksums for Communication and Data Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frank Schiller, Tina Mattes, Uwe Weber, and Rainer Mattes

1

Survey of PHY and LINK Layer Functions of Cognitive Radio Networks for Opportunistic Spectrum Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liang Hu, Villy B. Iversen, and Lars Dittmann

10

Optimizing TCP Performance over UMTS with Split TCP Proxy . . . . . . Liang Hu and Lars Dittmann

25

Use of Photonic Networks in Digital Cinema Postproduction Dailies Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michal Krsek, Paul Hearty, Laurin Herr, and Jurgen Schulze

36

Visualizing TCP/IP Network Flows to Control Congestion and Bottlenecks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nanjun Li and Werner Zorn

40

ZBMF: Zone Based Message Ferrying for Disruption Tolerant Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bahadir K. Polat, Moazzam Khan, Nikhil Tuli, and Pooja Kailay

48

Improving Query Mechanisms for Unstructured Peer-to-Peer Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guangwei Fang and Xiao Zheng

60

Layer 3 Initialization Procedures Recommendation in Next Generation Heterogeneous Mobile Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sebasti˜ ao Boanerges Ribeiro Junior and Paulo Roberto de Lira Gondim

68

Broadcast Data Scheduling in Wireless Environment . . . . . . . . . . . . . . . . . Xianjun Sun, Chuang Lin, Weidong Liu, and Yanping Xiao

76

Analysis on Imai-Shin’s LR-AKE Protocol for Wireless Network Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingjie Wang, Wei Luo, and Changxiang Shen

84

Surveys on the Intrusion Tolerance System . . . . . . . . . . . . . . . . . . . . . . . . . . Kuo Zhao, Nurbol, Fei Ren, Jianfeng Chu, and Liang Hu

90

Study on the DS/CDMA-CSMA Multi-user Communication Systems . . . Yun-xiao Zu, Xiao Wu, and Yu-qin Lv

98

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

105

Undetectable Manipulation of CRC Checksums for Communication and Data Storage Frank Schiller1, Tina Mattes1, Uwe Weber2, and Rainer Mattes2 1

Technische Universität München, Institute of Information Technology in Mechanical Engineering, Garching near Munich, Germany {mattes,schiller}@itm.tum.de 2 Siemens AG, Industry Sector, Nuremberg, Germany

Abstract. The Cyclic Redundancy Check (CRC) is an efficient and widespread coding method to detect random errors in industrial and business data transmission. Because of its simple implementation and low residual error probability, it is also applied to data stored in digital memory. If no error occurred then the checksum (FCS, Frame Check Sequence) is consistent to the original data. Additionally, the actual value of the FCS is often used as a criterion for e.g. the correct version of the software in digital memories or to detect its manipulation. Although it is well known that data protected by CRC can be manipulated undetectably, CRC is applied for the detection of manipulations but not random errors. The paper demonstrates and proves in detail how easily data, like business software or video streams, can be manipulated such that the manipulation is not detectable even for CRC of high order. Keywords: Cyclic Redundancy Check, checksum, manipulation, data transmission, data storage.

1 Introduction The Cyclic Redundancy Check (CRC) is an efficient and widespread coding method to detect random errors in industrial and business data transmission [1, 2, 3]. CRC can be easily implemented and results in a very low residual error probability in relation to the number of redundant bits or length of the checksum, respectively [4]. For instance, by means of 32 redundant bits, a residual error probability of 2-32 is achievable. Because of its simple implementation and low residual error probability, it is also applied to data stored in digital memory. Using CRC, the integrity of data is ensured by a checksum (FCS, Frame Check Sequence) that is the result of a polynomial modulo operation [5, 6, 7]. If no error occurred then the FCS is consistent to the original data. This consistency is typically the evaluation criterion in data transmission. For data storage, the actual value of the FCS is used in addition to the consistency check by practitioners in industry as a criterion for e.g. the correct version of the software in digital memories or in order to detect its manipulation. It is assumed that each change of the data will lead to another consistent FCS. Therefore, it appears to be sufficient to check the value of the FCS. This naïve approach does not consider P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 1–9, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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F. Schiller et al.

that CRC is a mean for detection of random but not intelligent errors. It is well known that data protected by CRC can be manipulated undetectably (cf. e.g. [8]). The paper demonstrates and proves in detail how data can be manipulated actually such that a manipulation is undetectable in data transmission and storage. It emphasizes that CRC must not be applied to data if intelligent sources of errors, i.e. humans supported by intelligent tools in adversarial attacks, are to be faced. In such cases, additional checking of data and other coding methods of cryptography are necessary. The paper is structured as follows. The general functionality of the CRC is explained in Section 2. Section 3 describes some necessary aspects of implementation of CRC. Since errors in form of random faults and manipulations are to be investigated later, Section 4 introduces the modeling of errors. In Section 5, the general kind of undetectable manipulation is developed first regarding manipulation and consistency check, before a simple way of manipulation of data stored in digital memory will be presented in Section 6. There, in addition to the consistency check, the value of the FCS is compared to the original one. The approach is illustrated by means of an example in Section 7. The paper finishes with some conclusions.

2 Functionality of CRC Since CRC has been developed for the detection of errors in data transmission or communication, respectively, the principles of CRC are explained in connection with communication. There CRC is applied as follows: A polynomial modulo operation with a binary polynomial, the so-called generator polynomial g(x) of degree r, is used to calculate the FCS of the given binary net data ND in the following way: enclosed in parentheses and set on the right margin.

fcs ( x ) = ( nd ( x ) ⋅ x r ) mod g ( x ) .

(1)

In (1), nd(x) and fcs(x) denote the polynomial counterpart of ND and FCS that build the telegram T = [ND, FCS]. Note that FCS consists of r bits. T is sent to the receiver and is possibly falsified to T’ during the transmission. In the receiver, check (2) proceeds:

t ' ( x) mod g ( x) = ((nd ' ( x) ⋅ x r ) + fcs' ( x)) mod g ( x) = 0 ?

(2)

If (2) is not true, T has been falsified. If (2) holds, T is regarded to be transmitted correctly and ND’ is used in the intended way in the application. For instance, generator polynomial g(x) = x3+x+1 and net data ND = [1110011] are given. ND leads to the binary polynomial nd(x) = 1·x6+1·x5+1·x4+0·x3+0·x2+1·x1+1·x0 = x6+x5+x4+x+1. The degree of g(x) is r = 3. According to (1), the polynomial counterpart of FCS is calculated by fcs(x) = ((x6+x5+x4+x+1) ·x3) mod (x3+x+1)=x. Thus, FCS = [010] and the telegram T that is sent to the receiver is T = [ND, FCS] = [1110011010]. Obviously, (2) holds, if T’ = T. But if for instance T’ = [1110001011] then t’(x) mod g(x) = (x9+x8+x7+x3+x+1) mod (x3+x+1) = x2+x+1 ≠ 0. Hence, the falsification is detected.

Undetectable Manipulation of CRC Checksums for Communication and Data Storage

3

The determination of the FCS in the sender and the consistency check in the receiver are realized e.g. by a linear feedback shift register (LFSR, see e.g. [4, 7]) or a corresponding table method and will be explained in the following section.

3 Implementation of CRC Realizations of CRC have been developed for hardware and software, cf. e.g. [4, 9]. A standard way to implement the polynomial modulo operation (1) is the application of a linear feedback shift register (LFSR, see Fig. 1). The number of bits of the register is equal to the degree r of g(x). The gi denote the coefficients of g(x). The overall bit pattern of z = [zr-1 … z0] represents the state of the register. The input u stands for the bits shifted stepwise into the register. For u(x) = nd(x)·xr, the final state corresponds to the solution of (1). That means, that ND is bitwise shifted into the register followed by r zeros. For u(x) = t’(x), the final state corresponds to the solution of (2).

z r-1 ⊗ gr =1

⊕ ⊗ gr-1

zr-2

⊕ ⊗ gr-2

z r-3

...

z1

logical XOR logical AND

...

⊕ ⊗ g1

z0

⊕

u

⊗ g0 = 1

Fig. 1. Implementation of CRC by a Linear Feedback Shift Register (LFSR)

A much more sophisticated LFSR with additional feedback is sometimes applied where the result of the modulo operation of the product of nd(x) and xr is available at each step k, cf. [4]. Furthermore, the same functionality can be achieved by means of a specific table approach with a much lower processing time. For the explanation of the overall approach, only the implementation of Fig. 1 is needed. It is formulated in MATLAB code in Listing 1. The length of the input pattern is l. At each step k, the next state of the register z(k+1) is calculated based on the present state z(k) and the present input u(k). Obviously, whenever bit zr-1 is at value 1, i.e. z(k) ≥ 2r-1, the feedback of the LFSR becomes active (cf. Fig. 1). This behavior is handled by a distinction of case in Listing 1. Listing 1. Simple MATLAB implementation of CRC

z(1)=0; % exemplary initial state for k=1:l if z(k)>=2^(r-1) z(k+1)=bitxor(2*z(k)+u(k),g); else z(k+1)=2*z(k)+u(k); end; end; Since it is needed in Section 5, a special kind of inversion of CRC is shown in Listing 2. The final state z(l+1) is given, and at each step k, the present state of the register z(k) is calculated based on the given corresponding state z(k+1) and the present input u(k). The activation of the feedback at step k is detected based on the difference between u(k) and the most right hand bit of z(k+1).

4

F. Schiller et al. Listing 2. MATLAB implementation of inverted CRC

z(l+1)=733298177; % exemplary final state for k=l:-1:1 if mod(z(k+1),2)~=u(k) z(k)=floor(bitxor(z(k+1),g)/2); else z(k)=floor(z(k+1)/2); end; end;

4 Modeling of Errors Obviously, CRC can not detect all errors. If T’ is equal to T’ = [1100101010] in the example of Section 2, then (2) holds for t’(x), hence the falsification is not detectable. Errors can be modeled by superimposed error patterns F. These patterns have the same length like T. A bit of F is allocated by value 0, if the corresponding bit in T is correct, and it is allocated by value 1, if the corresponding bit in T is falsified. Consequently, T is superimposed by F such that T’ = T+F holds (cf. Fig. 2). Note that ‘+’ stands for exclusive-or in the space of binary polynomials and binary words. mod g ( x)

Error:

m

r

nd (x)

fcs(x)

=0

fcs' ( x)

= 0?

f (x)

+ nd ' ( x)

Fig. 2. Modeling of errors by superimposition

An error is undetectable by CRC if and only if f(x), the polynomial counterpart of F, is divisible by the generator polynomial g(x), since:

t ' ( x) mod g ( x) = (t ( x) + f ( x)) mod g ( x) = t ( x) mod g ( x) + f ( x) mod g ( x) . = f ( x) mod g ( x)

(3)

holds. Therefore, t’(x) mod g(x) is equal to zero if and only if f(x) mod g(x) is equal to zero. This feature (3) is applied in the following to get manipulations undetectably.

5 Manipulation and Consistency Check In general, each error pattern F whose polynomial expression f(x) is a multiple of g(x) cannot be detected. If f(x) is a result of a manipulation and not divisible by g(x), an additional manipulation fadd(x) has to take place for undetectability such that:

t ' ( x) mod g ( x) = (t ( x) + f ( x) + f add ( x)) mod g ( x) = ( f ( x) + f add ( x)) mod g ( x) =0

(4)


5

holds. Equation (4) is equivalent to

f ( x) mod g ( x) = f add ( x) mod g ( x) . The most practicable form of the additional error pattern Fadd is a frame of r bits that is shifted (cf. Fig. 3). Obviously, a length of r bits is sufficient for Fadd since

f ( x) mod g ( x) = ( f add ( x) ⋅ x l ) mod g ( x) = (( f add ( x) mod g ( x)) ⋅ x l ) mod g ( x) holds and fadd(x) mod g(x) is of length r. Therefore, fadd can also be determined by sampling its 2r alternatives. This is not considered here because of complexity. The shift parameter l should be chosen such that there is no practical impact on the original manipulated data by F. Otherwise, the result of the main data manipulation would be changed by Fadd again. For instance, ND” = ND’ holds for l = 0. mod g ( x)

Main data manipulation:

+

m

r

nd (x)

fcs(x)

f (x) nd ' ( x)

Manipulation for consistency:

+

=0

fadd (x) nd " ( x)

fcs' ( x) l fcs" ( x)

= 0!

Fig. 3. Additional manipulation for consistency

After the main manipulation of nd(x) by means of f(x) resulting in nd’(x), the necessary fadd(x) for undetectability can be calculated as follows (cf. (2)):

(nd " ( x) ⋅ x r + fcs" ( x)) mod g ( x) = 0 (nd ' ( x) ⋅ x r + fcs' ( x) + f add ( x) ⋅ x l ) mod g ( x) = 0 (nd ' ( x) ⋅ x r + fcs' ( x)) mod g ( x) = ( f add ( x) ⋅ x l ) mod g ( x)

(5)

That means, that the result of the modulo operation of the data after the main manipulation has to be calculated first before fadd can be determined: Algorithm 1: Given: data after main manipulation nd ' ( x) ⋅ x r + fcs ' ( x) generator polynomial g(x) shift parameter l Find: additional error pattern fadd(x) such that (5) holds Solution: 1. result1 ( x) = (nd ' ( x) ⋅ x r + fcs' ( x)) mod g ( x) 2. ( f add ( x) ⋅ x l ) mod g = result1 ( x)

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The first part of the solution is solved by means of a typical implementation of the modulo operation like e.g. in Fig. 1 or Listing 1. The second part is solved by means of the inverted CRC in Listing 2. Starting with state result1(x) and considering an input u of l zeros, fadd(x) is determined (see Listing 3). Listing 3. Determination of Fadd for consistency (in MATLAB)

z(l+1)=result_1; for k=l:-1:1 if mod(z(k+1),2)==1 z(k)=bitxor(z(k+1),g)/2; else z(k)=z(k+1)/2; end; end; That means, a data frame like a telegram consisting of ND” and FCS” is consistent. Any manipulation F can be covered by means of an adequate Fadd.

6 Manipulation and Consistent Identical Checksum In addition to the consistency between net data nd”(x) and checksum fcs”(x), the value of fcs”(x) has to be equal to the original one fcs(x), see Fig. 4. mod g ( x)


+

m

r

nd (x)

fcs(x)

=0

fcs(x)

= 0!

f nd (x) nd ' ( x)

Manipulation for consistency and identical FCS:

+

f add (x) nd " ( x)

l

Fig. 4. Additional manipulation for consistency and identical checksum

Therefore, after the main manipulation of nd(x) by means of f(x) resulting in nd’(x), the necessary fadd(x) for undetectability is calculated as follows (cf. (5)):

(nd " ( x) ⋅ x r + fcs( x)) mod g ( x) = 0 (nd ' ( x) ⋅ x r + f add ( x) ⋅ x l + fcs( x)) mod g ( x) = 0 (nd ' ( x) ⋅ x r + fcs( x)) mod g ( x) = ( f add ( x) ⋅ x l ) mod g ( x)

(6)

That means, that a modulo operation of the net data after the main manipulation ND’ attached by the original FCS has to be executed before fadd can be determined:


7

Algorithm 2: Given: net data after first manipulation nd’(x) original checksum fcs(x) generator polynomial g(x) shift parameter l Find: additional error pattern fadd(x) such that (6) holds Solution: 1. result2 ( x) = ( nd ' ( x) ⋅ x r + fcs( x)) mod g ( x) 2. ( f add ( x) ⋅ x l ) mod g = result2 ( x)

Again, the first part of the solution is solved by means of a typical implementation of the modulo operation like e.g. in Fig. 1 or Listing 1. The second part is solved by means of the inverted CRC like in Listing 3, but starting with state result2(x). That means, a data frame like a telegram consisting of ND” and FCS” is consistent and FCS” is equal to the original one that is expected and checked. Any manipulation occurred before the introduction of Fadd is not detectable.

7 Example Consider the part of a program in Instruction List (cf. [10]) in Table 1 (first column) running e.g. on a fail-safe industrial programmable controller (PLC). The program is supposed to be critical and therefore a FCS of the program calculated by CRC is attached. For maintenance reasons, the program has to be modified but the FCS should be the same as before since its value is checked by independent authorities. The program contains the instruction LD 0xFFFFFFFF that is meaningless for the program execution, but the value of the constant (in hexadecimal notion) will be changed finally in order to get the manipulations undetectably. The actual basis, that means ND, for the determination of the FCS is supposed to be the machine code (see Table 1, second column). A fictitious processor is assumed. The FCS is calculated with generator polynomial 0x104C11DB7 that it is applied e.g. in Ethernet, FDDI, ZIP and PNG. Table 1. Original program

original program LD x LD y -D T z LD 0xFFFFFFFF LD x PUSH +D T y

fictitious machine hex code representation C442078 4C442079 2B44 54207A 4C44203078FFFFFFFF 4C442078 50555348 2B44 542079 FCS: 471CE694

The original program is now manipulated by changing the operation subtraction in the third row into addition, see Table 2. Since the FCS has not been changed during the manipulation of the program is detectable by a consistency check according to (2). If the old FCS would be substituted

8

F. Schiller et al.

by the new consistent FCS according to (1), the manipulation is still detectable by comparison of the new FCS to the expected original one that is usually mentioned in the documentation. Therefore, not the FCS but another part of the program has to be substituted. Here, the constant 0xFFFFFFFF is used in the example. The first manipulation (main data manipulation) should not affect the constant to be modified later for undetectability, see Fig. 5. Otherwise, the result of the main manipulation would be changed, again. Therefore, a bit pattern of zeros of length r is introduced at the appropriate position. mod g ( x)


m

r

nd (x)

fcs(x)

fnd 1 (x)

+

0L0

=0

fnd 2 ( x)

nd ' ( x) Manipulation for consistency and identical FCS:

+

fadd (x) nd " ( x)

l fcs" ( x) = 0! = fcs(x)!

Fig. 5. Manipulation schema

Now, Algorithm 2 (see Section 6) is applied: Given: nd’(x) (see Table 2, second column) original checksum fcs(x) is 0x471CE694 generator polynomial g(x) is 0x104C11DB7 shift parameter l=136 Find: additional error pattern fadd(x) such that (6) holds Solution: 1. result 2 ( x) is 0x2BB53E01 2. f add (x) is 0x8EB047EC That means that the constant 0xFFFFFFFF has to be superimposed with 0x8EB047EC in the program. Finally, the new value of the constant is therefore 0x714FB813, see Table 2. Table 2. Manipulated program (undetectable manipulation)

manipulated program ... +D T z LD 0x714FB813 ...

fictitious machine hex code representation ... 2C44 54207A 4C44203078714FB813 ... FCS: 471CE694


9

The change of the value of the constant does not have any influence on the execution of the program at runtime but prevents the detection of the manipulation. The consistency as well as the identical FCS is ensured.

8 Conclusions CRC cannot be applied to errors caused by intelligent sources. It can never substitute sophisticated methods and algorithms of security [11, 12]. In data communication, the only restriction is the time necessary to execute the undetectable modification. In the near future, telegrams can be manipulated on-line without any chance of detection afterwards in the receiver. Even in the case of the application of CRC to ensure data stored in digital memories, where in addition to the consistency between the net data ND and FCS the actual value is checked by independent authorities, CRC is not at all sufficient. Here the overall software has to be at least compared to a master copy or appropriate cryptographic codes and algorithms should be applied. Deciders in business and industry have to be aware of such issues in order to be able to make the correct decision for the protection of data.

References 1. Peterson, W., Weldon, E.J.: Error Correcting Codes. MIT Press, Cambridge (1996) 2. MacWilliams, F.J., Sloane, N.J.: Theory of Error-Correcting Codes. North-Holland, Amsterdam (1991) 3. Blahut, R.E.: Algebraic Codes for Data Transmission. Cambridge University Press, Cambridge (2003) 4. Schiller, F., Mattes, T.: An Efficient Method to Evaluate CRC-Polynomials for Safety Critical Industrial Communication. Journ. of Appl. Computer Science 14, 57–80 (2006) 5. Koopman, P., Chakravarty, T.: Cyclic Redundancy Code (CRC) Polynomial Selection for Embedded Networks. In: DSN 2004, Florence, Italy, pp. 145–154 (2004) 6. Castagnoli, G.: On the Minimum Distance of Long Cyclic Codes and Cyclic RedundancyCheck Codes, ETH Zurich, Diss. No. 8979 (1989) 7. Schiller, F., Mattes, T.: Analysis of CRC-Polynomials for Safety-Critical Communication by Deterministic and Stochastic Automata, SAFEPROCESS, Beijing, China, pp. 1003– 1008 (2006) 8. Wikipedia (2008), http://en.wikipedia.org/wiki/Cyclic_redundancy_check 9. Williams, R.N.: A Painless Guide to CRC Error Detection Algorithms. Rocksoft Pty Ltd., Hazelwood Park (1993) 10. International Electrotechnical Commission: IEC 61131-3, edn. 2.0 (2003) 11. Menezes, J., van Oorschot, P.C., Vanstone, S.A.: Handbook of Applied Cryptography. CRC Press, Boca Raton (2001) 12. Bellare, M., Rogaway, P.: Introduction to Modern Cryptography. Univ. of California (2005)

Survey of PHY and LINK Layer Functions of Cognitive Radio Networks for Opportunistic Spectrum Sharing Liang Hu, Villy B. Iversen, and Lars Dittmann Department of Communications, Optics & Materials Technical University of Denmark, Lyngby, Denmark {lh,vbi,ld}@com.dtu.dk

Abstract. Cognitive Radio recently arises as the enabling technology for opportunistic spectrum sharing, has drawn significant attentions from the wide range of research communities, including signal processing, machine learning, networking and algorithms. We survey the research challenges of designing cognitive radio networks for opportunistic spectrum sharing, with the aim of sparking new research interests in this field. We follow a layered approach where PHY and LINK layer functions are classified respectively and associated open research questions are proposed. In our work, firstly various proposed cognitive radio network architectures are reviewed associated with open research questions for each of those network architectures. Secondly, we survey the cognitive radio physical layer functions and propose open research questions at physical layer. Thirdly, we focus on link layer functions of cognitive radio networks and propose the open research questions. Keywords: Cognitive radio, opportunistic spectrum sharing, spectrum sensing, spectrum analysis, spectrum selection, spectrum coordination.

1 Introduction A recent report published by the federal communication commission (FCC) in US has shown a surprising finding, which highlights a different cause of the shortage of frequency resource: “In many bands, spectrum access is a more significant problem than physical scarcity of spectrum, in large part due to legacy command-and-control regulation that limits the ability of potential spectrum users to obtain such access”. According to FCC, 1) a large amount of licensed spectrum is used sporadically where the temporal and geographical variations in the utilization of the licensed spectrum range from 15% to 85%; 2) It is the complicated and old regulations that prevent us having open and flexible access to abundant spectrum. It becomes obvious that the bottleneck of spectrum scarcity is not the physical spectrum scarcity but spectrum allocation and regulation scarcity i.e. the imbalance of spectrum utilization between licensed spectrum and unlicensed one and the inefficient spectrum allocation scheme and regulations. Apparently, in order to increase the efficiency of natural spectrum resource utilization, more flexible spectrum management techniques and regulations are required. Cognitive Radio is one of enabling technologies to achieve such goal. In the following sections, we firstly define the terminologies of cognitive radio networks in section II. In section III, we review various cognitive radio network P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 10–24, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

Survey of PHY and LINK Layer Functions of Cognitive Radio Networks

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architectures proposed for opportunistic spectrum sharing. In section IV, we present the heterogeneous cognitive radio network architecture and corresponding network functions in Protocol stack. The Physical layer and Link layer research challenges are investigated in section V and VI respectively. Finally, the conclusion is drawn in section VII.

2 Definitions in the Context of Cognitive Radio Network In the following, we define the terminologies in the context of cognitive radio systems which will be employed throughout this paper. Cognitive Radio It is a radio that can change its transmitter parameters based on the interaction with the environment in which is operates. It has two capabilities: 1) environment aware capability--capture information from its radio environment e.g. by either spectrum sensing or Spectrum Usage Policy updates. 2) Re-configurability: certain operation parameters, such as transmission power, modulation scheme, and carrier frequency and so on, can be dynamically changed in real-time according to the current radio environment. Cognitive Radio is a way to break the spectrum access constraints in traditional spectrum regulations e.g. imbalance between spectrum allocation and spectrum usage, in order to improve spectrum utilizations supporting increased emerging wireless applications while minimize the interference and provide highly reliable ubiquitous wireless communications. Primary System (PS) Primary System is defined as an entity that legally owns some frequency band and has primary access to those frequency bands. There are several examples of Primary Systems: Mobile Cellular system, TV broadcast system, and Emergency Service system. The frequency bands for Primary Systems are allocated by spectrum authorities according to certain spectrum regulations and also called licensed spectrums. Spectrum Opportunities (SO) Spectrum Opportunities is also called spectrum hole or spectrum gap. It is defined as a band of frequencies assigned to a Primary System, but, at a particular time and specific geographic location, the band is not being utilized (therefore can be rent to or borrowed to other systems). Secondary System (SS) Secondary System is an entity that wants to acquire unused spectrum of license owners (Primary System) for its own communication. When SS coexist with PS, by detecting new available spectrum in the environment, SS can use a frequency band as long as the corresponding PS is not using it. After acquiring the licensed spectrum, it must continuously monitor the re-appearance of PS system and minimize its interference to the PS communications while coordinate the spectrum access among all SSs.

3 Overview of Physical and Link Layer Functions in Cognitive Radio Networks To evolve future cognitive radio-based heterogeneous wireless networks, from network level point of view, there are new technical challenges in network design

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L. Hu, V.B. Iversen, and L. Dittmann

compared to existing wireless networks. Basically, the challenges can be categorized into two aspects: protection of PS network functionalities from SS network interference and support of QoS in SS networks. • Protection of PS network from SS network interference For supporting QoS of PSs, it is essential to protect primary network functionalities from secondary network interference. Whenever secondary networks opportunistically share licensed spectrum with primary networks, the communications between PSs must not be interfered significantly by SSs. This is because PSs are the owners of the spectrum and ideally their network services should be guaranteed as if there were no secondary networks. Primary systems are already deployed and cannot be modified technically in most cases any more – thus, if spectrum is shared the secondary systems have to provide all required functionality. Hence, secondary networks, rather than Primary networks, should have cognitive radio capabilities to protect primary network functionalities, which have to address the following open issues: 1.

2.

3.

The SSs should reliable identify “active” PSs during their opportunistic operation on some licensed bandwidth and before they switch to another licensed bandwidth. While SSs are opportunistically using a licensed bandwidth, the SS should continuously periodically monitor that channel to detect whether PSs are reclaiming the channel. If SSs cannot detect “active” PSs in time, the collision between PSs signal and SS signal will unfortunately happen thus the PSs communication might be harmed. Besides, before SS switch to a new spectrum opportunity, it should also reliable identify whether there is already “active” PSs communication on that spectrum opportunity to avoid harming ongoing PS communications. After reliable identifying “active” PSs, group of SSs (the secondary network) should exchange their information of “active” PS identification to achieve group consensus regarding PS activities. This information exchange can help more reliable and efficient identifying and protecting “active” PSs globally by cooperation of various SSs. Upon information exchange, each SS may have a shared global knowledge of “active” PSs. Moreover, achieving consensus is an important requirement to protect primary systems. It is no help to detect them reliably but being unable to achieve consensus about the sensing results in the secondary network. Upon identifying that PSs are reclaiming their spectrum, SS should vacate the spectrum of PSs as soon as possible and switch to some new spectrum opportunity for its ongoing communications.

• QoS support in SS networks The support of QoS in SS networks is another aspect in designing CR-based wireless networks. Supporting of QoS in existing wireless networks is already not easy due to the characteristics of wireless channels while supporting QoS in CR-based SS networks becomes even more challenging. SS network services may always be additionally interrupted when PSs reclaim spectrum, as SSs have to give radio resource access priority to PSs according the spectrum regulation. From time to time, the QoS of SSs might degrade severely if PSs reclaim the spectrum too frequently or even get stopped when SS cannot identify new spectrum opportunities for some


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period. However, supporting some basic (maybe stochastically justified) QoS in SSs communication is essential for the popularity and wide deployment of CR-based wireless networks. To cope with those technical challenges, new network functionalities are needed to be developed, which are introduced in the following section. We strongly believe that new network functionalities should be implemented in SS networks. As CR based wireless networks are built on top of existing wireless network infrastructures, PSs should remain unchanged while adding the required additional CR capabilities to the SSs. Thus, all newly introduced complexity is put in the SS networks and hidden from PS networks. Ideally, the network services in PS networks are not disrupted by SS network in the sense that it looks as if there were no SSs. In Figure 1, the protocol stack for CR networks is presented with different CR functions mentioned above. • •

Physical Layer: Spectrum Sensing, Data reconfigurable transmission based on Software Defined Radio (SDR). Link Layer functions: Spectrum Analysis, Spectrum Selection (including spectrum adjustment), and Spectrum Coordination.

Fig. 1. Physical and Link Layer functions of cognitive radio networks

We provide an overview of different new network functionalities in this section with respect to two networks coexistence scenarios: SS networks coexisting with PS networks and SS networks coexisting with other SS networks. More descriptions about these various functions are described in sections later. • SS networks coexisting with PS networks There exists temporally unused spectrum either temporally or geographically in licensed spectrum used by primary network. Thus SS network can exploit those unused spectrum from the PS network through its cognitive radio capabilities. There are several functionalities that SS networks should have. Firstly, SS within one cell should be able to sense and detect spectrum opportunities precisely from PS networks. This is defined as “Spectrum Sensing”. Secondly, it should also be able to

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estimate the current state of the wireless channel and predict its capacity. This is defined as “Spectrum Analysis”. Then SS should be able to select a set of spectrum bands appropriate according to spectrum rules or spectrum policy while reconfiguring the physical layer RF to switch to the newly selected frequencies. This process is called “Spectrum Selection”. Thirdly, as primary users in general are not aware of SSs, SS networks should be able to vacate the spectrum opportunities immediately and move to a new available spectrum band as soon as PS reclaims them. Thus the interference to PS can be minimized. Besides, in order to support a certain QoS of SS payload communications, the SS should be able to identify and select new spectrum opportunities as soon as possible, in order to mitigate the interference from PS. This is called “Spectrum adjustment or re-selection” which maintains the ongoing wireless connection of SS while protecting primary system. The mutual interference between SS networks and PS networks depends very much on a) the activity pattern of PSs and users (either deterministic or random); b) the spectrum opportunity detection and vacation scheme of SSs. Finally, the Spectrum Coordination function is needed to coordination the secondary usage of the spectrum resource and transmitter-receiver handshake in the context of heterogeneous spectrum opportunities. • SS networks coexisting with other SS networks There are two types of coexistence among SS networks. The first one is the coexistence of SS networks in unlicensed spectrum, for example in the ISM band. The second one is secondary networks coexisting in “virtually unlicensed spectrum” which is temporally or spatially unused licensed bands. Since the two networks share the same spectrum for their own operations, they may interfere with each other without proper coordination schemes, thus reducing the spectrum efficiency and QoS support at SS networks. In addition, in contrast to coexistence among PS and SS networks, every SS network now has the equal right to access the unlicensed or virtually unlicensed spectrum and do not have to vacate the spectrum resource when other networks reclaim it. Instead, different SS networks may compete for the shared spectrum resource where in an extreme case one network may even behave quite “egoistically” in order to obtain as much spectrum as possible. Thus, coexistence of SS networks requires new sophisticated spectrum coordination function and local spectrum usage policy compared with above case. This new coordination function and local spectrum usage policy should a) improve the efficiency of spectrum usage; b) minimize the mutual interference among different SS networks and support QoS; c) provide spectrum access fairness and mitigate “egoistic” misbehavior.¨

4 Review of Physical Layer Functions Cognitive radio communication is strictly conditional on the reliable detection of unoccupied spectrum. One distinguished function of cognitive radio physical layer is Spectrum Sensing: to sense the environment over all available degrees of freedom (time, frequency, and space) in order to identify frequency bands currently available for transmission. Secondly, after identifying the spectrum opportunity, physical layer should have flexible and adaptive Data Transmission schemes that provide best spectrum utilization and capacity while avoiding interference to any PS. The


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capabilities and implementation limits of physical layer has a strong impact on the upper layer performance in the sense that it provides the realistic physical models to upper protocol design. In this section, we focus on function of the spectrum sensing. We firslty review the design challenges in RF front-end design for reliable spectrum sensing. Then we present various spectrum detection methods: non-cooperative transmitter signal based detection, cooperative transmitter based detection and Interference based receiver detection. To the end, the open research problems of spectrum sensing are outlined.

Fig. 2. Cognitive Radio Transceiver

4.1 Wideband RF Front-End The architecture of cognitive radio transceiver is showed in figure 2 where RF front-end and Signal detection are two most important blocks for spectrum sensing. Figure 2 shows an architecture of wideband RF front-end capable of simultaneous sensing over several GHz wide spectrum. In this architecture, a wideband signal is received through the RF front-end, sampled by the high speed analog-to-digital (A/D) converter, and measurements are performed for the detection of the licensed user signal. 4.2 Spectrum Detection Methods After reliable reception and sampling of a wideband signal, advanced digital signal processing techniques can be used to improve a) the RF front-end sensitivity by processing gain and b) primary user knowledge of the signal characteristics. The most efficient way of detect spectrum opportunities is to detect the primary users that are receiving data within the communication range of a secondary user. However, in reality, cognitive radio aware station can hardly have a direct measurement of a channel between a primary receiver and a transmitter. Instead, most recent research focuses on detecting the primary transmitters by the local observation of secondary users. In general, spectrum opportunity detection can be classified into either non-cooperative or cooperative transmitter detection and interference based receiver detection. 4.2.1 Non-cooperative Transmitter Signal Based Detection There are three types of transmitter detection techniques: a match filter detector, an energy detector, and a cyclostationary feature detector. In order to evaluate the

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performance of those detectors, several aspects are discussed [15]: processing gain for a given probability of detection, sensitivity to unknown noise and interference, and implementation complexity. 1) The match filter detector is the optimal detector in stationary Gaussian noise when the signal of primary system is known to the receiver. It can maximize the signal-tonoise ratio (SNR) among all other detectors. The implementation complexity for match filter is rather high compare with other detector in the sense that it needs a separate matched filter based receiver for every primary system. 2) When receiver cannot get sufficient information about primary signal, the optimal detector is energy detector which only integrates squared samples of received signal. Energy detector does not work well for detecting spread spectrum signals (direct sequence and frequency hopping signals). However, the implementation of energy detector is relatively simple which makes it a candidate for primary signal detection. 3) Cyclostationary feature detectors can extract distinct features of modulated signals such as sine wave carrier, symbol rate, and modulation type. These features are detected by analyzing a spectral correlation function that is a two dimensional transform, in contrast to power spectrum management being one dimensional transform. The main advantage of cyclostationary feature detector is that it can discriminate the noise energy from modulated signal energy, thus it has more robustness against unknown noise variance which may result in dismiss detection. 4.2.2 Cooperative Transmitter Signal Based Detection Cooperative transmitter detection refers to spectrum sensing methods where information from multiple SSs is incorporated for primary user detection. The cooperative transmitter detection can be implemented in either distributed manner as in [28], or centralized manner as in [13]. In centralized method, there is a master node in secondary network that collect all sensing information from the SSs and detect the spectrum opportunities collaboratively. In contrast, in distributed method, there is no master node among SSs but instead local sensing information is exchanged among every SSs. For this, a signaling or control channel is needed to exchange the data between different CR nodes. The investigation of the existence of such a channel is also presented in [28]. There is a clear trade-off that the sensing performance is improved at the cost of overhead. In [35], the author study a cooperative sensing method based on energy detector that can mitigate the multi-path fading and shadowing effects. This cooperative scheme improves the detection probability in a heavily shadowed environment. In [33], cooperative sensing is introduced as a mean to reduce the sensitivity requirements on an individual Cognitive Radio. 4.2.3 Interference Based Receiver Detection Traditionally, regulatory bodies minimize interference in a transmitter-centric way through coordination of bands of operation, radiated power, out-of-band emissions, and location of individual transmitters and so on. However, interference actually takes place at the receivers. Therefore, recently FCC introduced a new model for measuring interference and refers to it as interference temperature, showed in figure 3. In contrast to current transmitter-centric approach, this model tries to regulate the interference at the receivers where the interference really occurs. Under this new


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model, a maximum noise level would be set for an entire band and new systems could be placed in service if it is anticipated that the interference temperature limit would not be exceeded. In other words, the interference temperature model accounts for the cumulative radio frequency energy from transmissions and set a maximum cap on their aggregate level.

Fig. 3. Interference Temperature Model

In [34], a interference based receiver detection is studied, where the local oscillator (LO) leakage power emitted by the RF front-end of the primary receiver is exploited for the detection of primary receivers. In order to detect the LO leakage power, lowcost sensor nodes cab be mounted close to the primary receivers. The sensor nodes detect the leakage LO power to determine the channel used by the primary receiver and this information is used by the SSs to determine the spectrum opportunities. 4.3 Open Research Issues in Spectrum Sensing • Receiver based primary signal detection The effective Interference Temperature measurement is a significant challenge for receiver-based detection models. Under the collocated sensing architecture, even though a SS is naturally aware of its transmission power and its precise location, it still cannot practically, effectively measure or estimate the interference temperatures at nearby primary receivers. Primary receivers are passive devices, thus a SS cannot identify their locations and cannot estimate the effects of their transmission on all possible receivers. In this case the Interference Temperature model alone does not effectively protect the licenses. Sensor network based architecture may facilitate the interference temperature based receiver detection by deploying a separate network for spectrum sensing and local interference temperature estimation at primary receiver. However, the cost and feasibility of deploying a separate sensor network is still under studied. • Cooperative Sensing Cooperative Sensing can increase the probability of reliable spectrum opportunity detection while reducing the sensitivity requirement of individual SS. The cooperative sensing in general helps the mitigation of the misdetection spectrum opportunities in fading and shadowing environment but on condition that cooperating users more or less face independent fading. To more precisely study the performance gain by cooperative

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sensing, a comprehensive model relating different parameters such as detection probability, number and spatial distribution of spectrum sensors and more importantly propagation characteristics is yet to be developed. Secondly, energy based cooperative sensing is susceptible to uncertainty in noise power, cyclic feature based cooperative detection is the subject for future research to solve this problem. Last but not least, the un-trusted SSs fail in un-modeled way impose an upper bound on the achievable performance gain of cooperative sensing. Therefore, how to enhance the cooperation among SSs and misbehaving SS detection && punishment are the potential research areas in cooperative sensing.

5 Review of Link Layer Functions 5.1 Spectrum Opportunity Analysis In SS networks, the available spectrum opportunities may show different spatial and temporal characteristics. It is very important for SSs to understand the characterization of different spectrum bands so that the best available channel can be selected according to the user requirement. In order to describe the dynamic nature of SS network, each spectrum opportunity should be classified not only by the time-variant radio environment but also by primary user activities and spectrum band information such as operating frequency and bandwidth. Thus we need to define the parameters which indicate the quality of spectrum opportunity such as interference level, wireless link errors, path loss, link layer delay, and spectrum holding time and so on. Interference Level Different spectrum bandwidths may have different interference characteristics, e.g. some unlicensed spectrum is more crowded than licensed one. On the primary receiver side, one can detect the amount of interference as the indicator of maximum channel capacity according Shannon Capacity. Depending on the interference level of the spectrum band, secondary user can decide its transmission power on this band or even decide whether it should change to another channel with less interference. Spectrum Holding Time Holding time refers to the expected duration that the SS can occupy a licensed band before get interrupted by primary users re-appearance. This parameter depends on the primary traffic patterns. If the primary users use a specific band very frequently, the spectrum holding time of this band would be too small to be used by secondary users. Obviously, the longer the channel holding time, the less interruptions secondary users experience thus the better quality of the spectrum opportunities would be. Since frequent spectrum handover can decrease the holding time, previous statically patterns of handover should be considered while designing SS networks with large expected holding time [18]. 5.2 Spectrum Opportunity Selection Once all the available spectrum bands are characterized, appropriate operating spectrum band should be selected for the current transmission considering the QoS


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requirements of end users, spectrum usage policies set by the regulatory body, and the spectrum characteristics. Based on the user requirement, the data rate, acceptable error rate, delay bound, the transmission mode, and the bandwidth of the transmission can be determined. Then, according to spectrum selection rules and policies, the set of appropriate spectrum bands can be chosen. Haitao [20] proposed five spectrum selection rules that tradeoff fairness and utilization with communication cost and algorithms complexity, though the assumption that all channels have similar throughput does not always hold. In particular, conflicts-free assignment based rules let secondary users reserve channels which are preferable when the spectrum is finely partitioned, and when users have constant traffic, e.g. video streaming. Contention-based rules require fine granularity in time to efficiently utilize spectrum, providing improved utilization for coarsely partitioned spectrum and busty data traffic. E.Knightly[21] proposed an opportunistic frequency channel skipping protocol for searching better quality channel, where the channel decision is based on SNR. In [22], in order to consider the activities of primary users, the spectrum selection has considered the number of handover happened in a certain band. The above spectrum selection methods take a reactive sense-and-avoid approach to impulsively reconfigure spectrum usage without knowledge of future dynamics. In [18], a proactive spectrum selection method is proposed to examine the performance of cognitive radio system in the TV-broadcast scenario. SSs utilize past observations of spectrum band holding time to build predictive models on spectrum availability and select spectrum opportunities to maximize utilization and minimize disruptions of primary users. Finally, the spectrum selection needs to incorporate spectrum adjustment or reselection function. The spectrum adjustment takes place when the quality of current spectrum band degrades due to user mobility or the primary user re-appears. Daniel [23] studied a reliable link maintenance model for cognitive radio network which is essentially a spectrum re-selection model. The spectrum selection structure is indicated in figure 4:

Fig. 4. Spectrum Selection Structure

The spectrum status is read in, and checked against the spectrum rules or spectrum policy. If the user is satisfied, no change is necessary and the selector terminates. Otherwise, the rules are used to determine the appropriate spectrum bands, and the spectrum selection is done accordingly.

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5.3 Spectrum Usage Coordination 5.3.1 Inter-network Spectrum Coordination Inter-network spectrum coordination refers to spectrum sharing among multiple SS networks being deployed in overlapping locations and shared spectrum as show in the figure 5.

Fig. 5. Spectrum sharing and coordination

Traditionally, one of the focuses in inter-network spectrum coordination is the coexistence of WLAN and Bluetooth networks in ISM band, where the spectrum coordination is done by a spectrum etiquette protocol. This spectrum etiquette protocol facilitates spectrum coordination between WLAN and Bluetooth by announcing the radio and service parameters on common spectrum coordination channel (CSCC) based on a low rate 802.11b radio. Besides, another focus has been in the area of spectrum usage coordination of different access points in WLAN. In the context of cognitive radio, the inter-network spectrum coordination can be classified into two categories: 1) Decentralized Spectrum Coordination 2) Centralized Spectrum Coordination In the first category, different SS networks use different radio access methods coexisting in the same unlicensed band. Nodes within SS networks of different wireless interfaces cannot directly exchange control information with each other, thus they can only mitigate the interference by local observation. One of the interesting scenarios is the coexistence of IEEE 802.11b and IEEE 802.16a. IEEE 802.16a can provide wireless connectivity to homes and offices while short-range IEEE 802.11b WLAN offers complementary wireless local area network services within a home or offices. In [24], a reactive interference avoidance algorithm has been proposed to deal with the coexistence problem of IEEE 802.11b and 802.16a network where interference mitigation is done by controlling transmit frequency, transmit power and spectrum time occupancy. With a higher level protocol complexity, X.Jing proposed [25] a proactive algorithm to improve spectrum coordination between radio nodes using a common spectrum coordination channel at the edge of the shared frequency band or even at licensed band. The centralized inter-network spectrum coordination is common in many practical environments such as home and offices. It has a central spectrum broker as we defined above that can process detailed information about wireless network, which


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allows highly efficient network configuration and better enforcement of a complex set of policies. In [26], a dynamic spectrum access protocol (DSAP) is proposed as one example of centralized inter-network spectrum coordination. In this work, a central entity acts as the spectrum broker to lease the spectrum to SSs. The spectrum lease acts in a way analogous to how DHCP lease IP address to host in a network.Another example can be found in [27], where a distributed QoS based dynamic channel reservation (D-QDCR) scheme is used. 5.3.2 Intra-network Spectrum Coordination In Intra-network spectrum coordination, the focus is how SSs can try to access the spectrum opportunities in licensed band without causing interference to the primary users. Typically, it refers to scenario where one SS network coexists with primary network. Intra-network Spectrum Coordination function not only coordinates spectrum usage within the SS network, but also should always protect primary network traffic and minimize the interference from SS to PS. Intra-network spectrum coordination can be classified into two types. 1) Cooperative coordination In cooperative coordination, SSs act somewhat selfless, occasionally sacrificing local performance to improve the system utility by explicitly exchanging control information with its neighbors. In particularly, each SS exchanges spectrum opportunities information with neighboring users so that SS within vicinity have a common view of spectrum availability and each secondary user also knows the spectrum opportunities of its neighbors. Thus each secondary user selects a spectrum band not only based on its own utility but also need to consider the usage of its neighbors. Jun [28] proposed a cooperative distributed coordination scheme in a scenario where a secondary wireless ad-hoc network coexist with a primary network. It aims to alleviate the saturation problem of the common control channel. 2) Non-cooperative coordination By frequent coordination information exchange, the cooperative spectrum coordination may heavily stress the communication resources of constrained networks such as sensor and mobile ad-hoc networks. The non-cooperative coordination can significantly reduce the coordination and control traffic by enabling SS to act independently based on local observations of the interference pattern and neighboring nodes. Haitao [30] proposes a device-centric spectrum management scheme which eliminates the need of common control channel for spectrum sharing. P.Papadimitratos [31] propose another noncooperative spectrum coordination MAC “AS-MAC” for an ad-hoc overlay network (SS network) coexisting with GSM networks (PS network) to exploit the unused spectrum left by them. 5.4 Open Research Problems in Link Layer The Common Control Channel is both needed in Inter-network Spectrum Sharing and Intra-network Spectrum Sharing to provide many Cognitive Radio functions such as interference mitigation [17], distributed cooperative sensing, transmitter-receiver handshake, communication with central entity [18], and so on. There are problems with traditional CCC with respects to scalability, device cost and security. In the

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context of work [17], there are several drawbacks of CCC for coexisting WiMAX and WLAN: First, it requires a static assignment of licensed spectrum before deployment, increasing complexity and cost. Second, the licensed channel’s fixed bandwidth limits scalability in terms of device density, traffic and spectrum ranges. Finally, a simple jamming attack of the fixed control channel would disrupt the entire network. Secondly, a set of dynamic CCCs can be constructed by selecting them from dynamic available data channel lists shared among a cluster of neighbouring SSs as indicated in [20]. By organizing users into groups, coordination and control traffic are distributed onto multiple CCC, which is more scalable and robust to jamming attack. However, the drawback is the group and discovery formulation might result in a large overhead and decrease the SSs throughput when group updates are triggered too often due to primary traffic dynamics. The third approach is to eliminate the need for the Common Control Channel as in [22] where SSs act independently based on the local interference observation and spectrum selection rules instead of collaborating with other neighbouring SSs or [24] where a receiver-oriented method can be implemented for transmitter-receiver handshake.

6 Conclusion Traditional fixed spectrum allocation has been proved to be inefficient in terms of spectrum utilization which potentially leads to spectrum scarcity. The emerging cognitive radio technology forms the foundations of opportunistic spectrum sharing to improve the spectrum utilization and efficiency. We survey the research challenges in physical and link layer of cognitive radio networks. In particular, at the physical layer, the most challenging problems lie in spectrum sensing: the design of wideband sensing RF-Front-End and reliable spectrum detection algorithms, as well as flexible data transmission schemes over non-continuous, highly dynamic, heterogamous spectrum opportunities. In the link layer, the main research challenges lie in: designing comprehensive framework of spectrum analysis and selection, designing MAC protocols for spectrum coordination/sharing among secondary systems and primary systems. The issues covered in this review address broad aspects of research challenges in designing cognitive radio networks for opportunistic spectrum sharing. We hope this survey could shed a light on various open research questions in this field, bring the research community an overview picture of cognitive radio network research, and mostly importantly spark new research interests/directions.

References [1] Webb, W., Marks, P.: Radio spectrum pricing. IEEE Review (March 1996) [2] Zander, J.: On the cost structure of future wideband wireless access. In: IEEE 47th Vehicular Technology Conference, vol. 3, pp. 1773–1776 (May 1997) [3] Mixed signals on the airwaves: The allocation of spectrum is neither efficient nor effective. Financial Times (December 28, 2002) [4] Why 3G may still come up short. Business Week Online (March 22, 2004) [5] Petrin, A., et al.: Measurement and analysis of urban spectrum usage. In: 6th International Symposium on Advanced Radio Technologies (ISART), pp. 45–49 (March 2004)


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[6] Engelman, R., et al.: Fedral communications commission, spectrum policy task force, report of the spectrum efficiency working group. Technical report (November 2002) [7] Peha, J.M.: Approaches to spectrum sharing. IEEE Communication Magazine (February 2005) [8] Grossglauser, M., Tse, D.: Mobility increase capacity of ad hoc wireless networks. IEEE/ACM Transactions on networking (August 2002) [9] Weiss, T.A., Jondral, F.K.: Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency. IEEE Radio Communication Magazine (March 2004) [10] Brodersen, R.W., et al.: Corvus: a cognitive radio approach for usage of virtual unlicensed spectrum, Berkeley Wireless Research Center (BWRC) White paper (2004) [11] Cabric, D., et al.: A Cognitive Radio approach for usage of virtual unlicensed spectrum. In: Proc 14th IST Mobile and Wireless Communication (June 2005) (submit) [12] Buddhikot, M.M., et al.: DIMSUMNet: new directions in wireless networking using coordinated dynamic spectrum access. In: Proc. IEEE WoWMoM 2005, pp. 78–85 (June 2005) [13] Cordeiro, C., et al.: IEEE 802.22: the first worldwide wireless standard based on cognitive radios. In: Proc. IEEE DySPAN 2005 (November 2005) [14] Zekavat, S.A., et al.: User-Central Wireless System: Ultimate Dynamic Channel Allocation. In: Proc. IEEE DySPAN 2005 (November 2005) [15] Cabric, D., Brodersen, R.W.: Physical Layer Design Issues Unique to Cognitive Radio Systems. In: IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications (September 2005) [16] Wild, B., Ramchandran, K.: Detecting Primary Receivers for Cognitive Radio Applications. In: Proc.IEEE DySPAN 2005, pp. 144–150 (November 2005) [17] Tang, H.: A Unified Approach to Wireless System Design, PhD thesis, University of California at Berkeley (2002) [18] Acharaya, P., Singh, S., Zheng, H.: Reliable Open Spectrum Communication Through Proactive Spectrum Access. In: IEEE TAPAS 2006, Boston, US (August 2006) [19] Tang, H.: Some physical layer issues of wide-band cognitive radio system. In: Proc. IEEE DySPAN 2005, pp. 151–159 (November 2005) [20] Zheng, H.: Device-centric spectrum management. In: Proc. IEEE DySPAN 2005, pp. 56– 65 (November 2005) [21] Sadeghhi, B., et al.: Opportunistic media access for multirate ad hoc netoworks. In: Proc. ACM/IEEE MOBICOM 2002, Atlanta, CA (2002) [22] Krishnamurthy, S., et al.: Control Channel based MAC layer configuration, routing and situation awareness for cognitive radio networks. In: Proc. First IEEE Workshop on Wireless Mesh Networks (WiMesh) (September 2005) [23] Willkomm, D.: Reliable Link Maintenance in Cognitive Radio Systems. In: Proc. IEEE DySPAN 2005 (November 2005) [24] Jing, X., et al.: Reactive Cognitive Radio Algorithms for Co-Existence between IEEE 802.11b and 802.16a Networks. In: Proc. IEEE Globecom (2005) [25] Jing, X., et al.: Spectrum Co-existence of IEEE 802.11b and 802.16a Networks using the CSCC Etiquette Protocol. In: Proc. IEEE DySPAN 2005(November 2005) [26] Brik, V., et al.: DSAP: A protocol for Coordinated Spectrum Access. In: Proc. IEEE DySPAN 2005 (November 2005) [27] Marias, G.: Spectrum Scheduling and brokering based QoS demands of competing WISPs. In: Proc. IEEE DySPAN 2005 (November 2005) [28] Zhao, J., et al.: Distributed coordination in dynamic spectrum allocation networks. In: Proc. IEEE DySPAN 2005 (November 2005)

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Optimizing TCP Performance over UMTS with Split TCP Proxy Liang Hu and Lars Dittmann Department of Communications, Optics & Materials Technical University of Denmark, Lyngby, Denmark {lhua,ladit}@fotonik.dtu.dk

Abstract. The TCP performance over UMTS network is challenged by the large delay bandwidth product. Large delay bandwidth product is mainly caused by the latency from the link layer ARQ retransmissions and diversity technique at physical layer which are used to cope with radio transmission errors. To cope with large delay bandwidth product, we propose a novel concept of split TCP proxy which is placed at GGSN between UMTS network and Internet. The split proxy divides the bandwidth delay product into two parts, resulting in two TCP connections with smaller bandwidth delay products which can be pipelined and thus operating at higher speeds. Simulation results show, the split TCP proxy can significantly improve the TCP performance in terms of RLC throughput under high bit rate DCH channel scenario (e.g.256 kbps). On the other hand, it only brings small performance improvement under low bit rate DCH scenario (e.g.64 kbps). Besides, the split TCP proxy brings more performance gain for downloading large files than downloading small ones. To the end, for the configuration of the split proxy, an aggressive initial TCP congestion window size (e.g. 10 MSS) at proxy is particularly useful for radio links with high data rates DCH channels with large delay bandwidth product. Keywords: Delay Bandwidth Product, Split TCP proxy, UMTS, TCP congestion control.

1 Introduction Ubiquitous Internet access is regarded as a key success factor for third generation mobile communication system. WCDMA/UMTS [1, 2] shows this trend by providing efficient support for packet-switched data services with data rates up to 384 kb/s for wide area coverage and maximum 2M bits/s for hot spot areas. The mobile internet access over UMTS is expected to bring one or two order of magnitude higher data rates than the previous 2G cellular networks. However, it is important that the combination of Internet application and underlying transport layer can make good use of the underlying large network capacity provided by WCDMA air interface. One of the main problems of UMTS network is TCP throughput degradation in the large delay bandwidth products (up to around 20k bytes). A number of studies can be found in the literature [3, 4, and 7] have shown TCP performs poorly over wireless links. However, those studies primarily either focuses on optimize TCP to cope with radio transmission errors [4] or the long propagation delay in satellite system [7]. In P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 25–35, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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contrast, we target at the large delay bandwidth product problem in UMTS access network. The large delay bandwidth product is mainly caused by either the variable end-to-end delay due to link layer ARQ retransmissions or the enhanced physical layer transmission bandwidth (which can be up to 2M kbps in UMTS and 10M kbps in High Speed Packet Access (HSPA)). The contributions of this work are two-folds: firstly, the TCP performance over UMTS dedicated transport channels (DCH) is studied under various delay bandwidth product scenarios. Unlike [5], our simulator incorporates the impact of packet loss rate of Internet. Secondly, a novel TCP split proxy concept is proposed to cope with the poor TCP performance under large delay bandwidth product scenarios. The split proxy is implemented at GGSN. With split proxy, a TCP connection from an Internet server is terminated in the proxy located at GGSN, and a second TCP connection is used towards the mobile client. The split proxy divides the bandwidth delay product into two parts, resulting in two TCP connections with smaller bandwidth delay products which can be pipelined and thus operating at higher speeds. The rest of the paper is organized as follows. In section 2, we introduce the UMTS network architecture. In section 3, we present the concept of split TCP proxy. In section 4, we describe the simulation settings for evaluating TCP performance with and without split proxy. The simulation results and analysis are described in section 5. We draw final conclusions in section 6.

2 Overview of UMTS Network 2.1 UMTS Network Architecture Figure 1 shows the WCDMA network architecture in packet switch operation [6]. The network functionality is divided into three groups: User Equipment (UE), UMTS Terrestrial Radio Access Network (UTRAN) and Core Network (CN). UTRAN consists of Node B and Radio Network Controller (RNC). The CN comprises two basic nodes: Serving GPRS Support Node (SGSN) and Gateway GPRS support Node (GGSN). It acts as the backbone for the radio network and the connectivity toward external networks. GGSN provides inter-working with external packet-switched networks such as public internet or corporate intranets via the GI interface. SGSN is

Fig. 1. WCDMA network architecture in PS domain

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Fig. 2. WCDMA protocol architecture-User-Plane. RLC provides reliable data transfer over error prone radio link by ARQ retransmission.

connected to RNC via the IuPS interface. SGSN is connected to RNC via the luPS interface. UE is connected to UTRAN over the WCDMA radio interface Uu. 2.2 UMTS Network Protocol Architecture Figure 2 depicts the WCDMA protocol architecture for the transmission of user data plane data which is generated by TCP or UDP based applications. The applications as well as the TCP/IP protocol suite are located at the end-nodes: UE and host. Basically, WCDMA provides two types of channels: dedicated channels (DCH) exclusively used by one user and common channels shared between users. In this work, only DCH channels are considered. Four different protocols are involved in the transmission over the air interface: • • • •

Packet Data Convergence Protocol (PDCP) Radio Link Control protocol (RLC) Medium Access Control (MAC) Physical layer (PHY)

The PDCP provides header compression functionality which improves spectra efficiency for transmitting IP packets over the radio interface. The RLC provides link layer ARQ functionality which is typically required for dealing with packet loss due to radio transmission errors. RLC can operate in three different modes: The acknowledged mode, unacknowledged mode and transparent mode. Due to the TCP performance is sensitive to packet loss due to transmission errors, it is nature to user RLC acknowledge mode for TCP traffics. The acknowledged mode provides reliable data transfer over the error-prone radio interface via ARQ protocol. In the unacknowledged mode, the data transfer over the radio interface is not error free but with no additional delay due to retransmission. The functionality of transparent mode is similar to unacknowledged mode but no protocol information is attached to the PDU. RLC protocol in WCDMA provides the means to optimize the protocol for delay or throughput, or a trade-off between them. Basically, the protocol uses a selective repeat request strategy, where the acknowledgements are triggered by either poll flags sent by the data sender or the detection of erroneous blocks at the receiver, or based on periodic events.

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The Medium Access Control (MAC) layer provides a set of logical channels to RLC. Besides, the MAC layer is responsible for controlling the amount of data sent according to negotiated minimum and maximum data rates during the radio bearer setup. The instantaneous data rate is determined for every transmission time interval (TTI). The MAC layer requests the amount of data buffered at the RLC layer, which is ready for transmission. Based on available radio resources and the amount of data to transmit, the MAC layer submits the appropriate number of RLC blocks to the PHY layer. The Physical layer (PHY) contains besides all radio frequency functionalities, the signal processing including Rake receiver, channel estimation, power control FEC, interleaving, and rate match.

3 Split TCP Proxy for UMTS Performance Enhancing Proxies are introduced when the performance suffers due to characteristics of a link or sub-networks [9]. An important subclass of PEPs are split connections proxies. This means a TCP connection from one end system is terminated at the proxy and another connection originate there. With TCP proxy, local acknowledgement in the proxy about the state of a TCP connection can be used to enhance the performance by shortcutting the transmission of ACKs or retransmissions. In the context of UMTS, the proxy is only used for shortcutting the transmission of ACKs, as RLC takes care of the retransmissions. The placement of TCP proxy in UMTS network is indicated in figure 3. In UMTS, a TCP connection from an Internet server is terminated in the proxy located in the CN, and a second TCP connection is used towards the mobile client. To cope with the large delay bandwidth of UMTS network, the split proxy divides the bandwidth delay product into two parts, resulting in two TCPs with smaller bandwidth delay products and thus operating at higher speeds. Unlike previous work [10] [11], the purpose of introducing a TCP proxy is to increase the throughput during TCP slow start, rather than the fast packet loss recovery due to radio transmission errors , as the link layer of UMTS are carefully designed for radio transmission errors. Figure 4 shows the difference in the information flow for the two cases with and without Split Proxy supporting local ACKs. While in case a, without Split Proxy the server has to wait for the response from the client resulting in a poor slow start performance, the Split Proxy speed up the data transfer in case b. The Split Proxy effectively implements pipelining for the two TCP connections. On the connections toward the client the PEP sends the first segment, and in parallel requests already new data from the server. Secondly, to further enhance of UMTS radio link utilization, the TCP connection from proxy to client can be configured with an initial window size large than the two segment proposed by [12], or the three segment proposed by [13]. This approach is motivated by the observation: the RTT for the TCP connection traversing the Internet is typically smaller than the RTT for the connection over UMTS network. This means that during a conventional slow start, data arrive faster at the proxy than they can be sent toward the client when both two split TCP connections use the classic initial window size. To balance the speed of data transfer, a more aggressive TCP initial window over the radio bearer can enable the proxy forward more segments without


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Fig. 3. The Schematic of Split TCP Proxy in the end-to-end TCP path. The split TCP proxy is placed in between UMTS core network and Internet.

Fig. 4. Schematic TCP transfer with and without Proxy. In the case of using split TCP proxy, by using the local ACK, two TCP connections can be pipelined for transmissions.

waiting for ACKs from the client. Thus, during TCP slow start, the radio link can be more efficiently utilized since the TCP sending window can more quickly reach the maximum capacity of the radio link.

4 Simulation Settings We firstly evaluate the end-to-end TCP performance over WCDMA DCH channels without split proxy. Secondly, we compare the TCP performance with split proxy and without it. The performance evaluation is based on OPNET simulator [8]. This model assumes that an application transfers files on request from a server in the internet to a mobile client connect to UMTS network. A TCP Reno is used as the transport protocol, which is configured for a mobile environment according to the recommendations in [14]. All radio protocols have been implemented in details as described earlier. The Internet and Core Network have been simplified by add a constant delay to transmit IP packets which is 100 ms for one way. In addition, independent packet losses are considered in the internet. For the radio link, it is assumed a dedicated physical channel is used. The power control is assumed to work perfectly, resulting in a constant block error rate on the radio link. Block errors are assumed to be independently distributed. The RLC layer is configured in acknowledged mode and PDU delivery is in sequence. The RLC transmitting and receiving window is both 1024 PDUs while the SDU from upper layer is discard after MaxDAT times RLC layer retransmissions. Polling is not supported in OPENT model. The status report timer is 100 ms.

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The TCP receiver advertised window (awnd) is 32768 Byte. The slow start threshold (ssthresh) is set to the size of awnd. The TCP retransmission timeout (RTO) is set to minimum 1 second and maximum 60 second. Fast retransmit is triggered after sender receives three duplicated ACKs from receiver. The simulation parameters are given in the table below: Table 1. Simulation Parameters

Fig. 5. Simulation Setup

As indicated in figure 5, the simulation setup is based on the system architecture discussed in the previous section. The network traffic is HTTP and FTP traffic, where the user behaviors of requesting files using HTTP and FTP are modeled such that the


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inter-request time follows exponential distribution with average value 30 seconds. The file size is in between 50, 100, or 200 Kbytes.

5 Simulation Results 5.1 TCP Performance over DCH without Split Proxy A. Impact of TCP slow start From the efficiency point of view, the impact of the TCP slow start after the TCP connection establishment depends on the overall amount of requested data. For small files (e.g. WWW pages), the document transmission is affected by the poor radio link utilization during the TCP slow start, with the subsequent throughput degradation. On the other hand, for larger files (e.g. ftp downloads) the effect is expected to be minor. In the following, we aim to study the influence of slow start to the RLC throughput, with various file sizes and DCH bit rates. RLC throughput VS TCP initial counter

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As shown in the graph, in general, for all DCH channel bit rates (64, 128,256 kbps), small file size has a lower RLC throughput than large file size. This is due to the fact that: The RLC throughput suffers in very TCP slow start phase; Assuming continuous downloading files, downloading small files involves more TCP slow start phases compare to downloading large files. Thus downloading small files has more degradation of RLC throughput. In particularly, for a higher bit rate DCH channel, small file size degrades the RLC throughput significantly. For low speed DCH channel, the throughput of RCL decreases very little when the file size evolves from large size to small size. As it is indicated in the figure 6, for the DCH 256 kbps, the RLC throughput decrease almost 113 kbps when the file size evolves from 200 Kbytes to 50 Kbytes. In contrast, for DCH 64 kbps, the RLC throughput decreases only 20 kbps when the file size becomes 50 Kbytes. The reason is that: a high data rate DCH channel indicates a high delay

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bandwidth product; With a large delay bandwidth product, the RLC throughput of small file decreases more dramatically due to low utilization of network capacity during TCP slow start phase; On the other hand, in a low delay bandwidth product environment such as a low bit rate DCH case, there are few difference between good and bad utilization of network capacity during TCP slow start because the network capacity is anyway very small. B. Impact of TCP initial congestion window (Other simulation parameters: file size= 100k bytes, Internet loss ratio=0, RLC MaxDAT=10) As it is shown in figure 7, a large TCP initial congestion window can improve the TCP performance for the slow start phase. Because a larger TCP initial window can enable the sender to fill up the “TCP pipe” much more quickly than a small value in the slow start phase. Thus the utilization of radio link is increased via using a large slow start initial counter. This is especially the case, when the delay bandwidth product is large. As shown in the figure 7, for DCH 256 kbps, the RLC throughput increases more than 30% when the initial counter increases from 1 MSS (Maximum Segment Size) to 4 MSS. In contrast, for DCH 64 kbps, the RLC throughput has a small increase less than 20% when the initial counter is set to 4 MSS. Imapct of Internet Loss Ratio

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C. Impact of Internet loss rate The impact of Internet packet loss ratio is studied in this section. We assume the following simulation setting: 128 kbps and 256 at radio link, Internet loss rate (1%, 5%, and 10%); Inter-request time Exponential with mean 30s, file size 200k bytes. The conclusion we get from the simulation are: 1) for high bandwidth radio link, the impact of Internet loss ratio is significant to the RLC throughput; As shown in the figure 8, the RLC throughput drops around 36% when the Internet loss ratio increases from 1% to 10%. 2) For low bandwidth radio link, the impact of Internet loss ratio to the RLC throughput is very small. As shown in the figure 8, for DCH 128 kbps, the


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throughput degradation is only 23% when the internet loss ratio evolves from 1% to 10%. The reason is that a high bandwidth radio link indicates a large Bandwidth Delay Product (BDP); RLC throughput is more vulnerable to Internet packet loss rate when the delay bandwidth product is large, compare to the case of small delay bandwidth product. D. RLC interaction with TCP protocol UMTS radio access network introduces the RLC layer to recover the block erasures caused by the link level errors of the radio channel. With a sufficiently number of RLC retransmissions, the SDU error ratio provided to Transport layer can be negligible. Figure 9 plots the RLC throughput as function of RLC parameter MaxDAT for DCH data rate (64,128,256 kbps) respectively. The results clearly show that the more reliable the RLC, the higher RCL throughput can be achieved. In order to test the reliability of RLC, the MaxDAT is set to 3, 5 and 10. It is shown that the more reliable the RLC, the better RLC throughput can be achieved. In order to reach a high degree of reliability, different MaxDAT is needed for different BLER targets. As indicated in figure 9, for BLER 10%, when MaxDAT is set to 5, the RLC throughput is maximized while BLER 40% requires a MaxDAT larger than 10 to achieve max RLC reliability. 5.2 Comparison of the Performance with Split TCP Proxy and without Split TCP Proxy From figure 10, the average RLC throughput is presented for different sizes of files being transmitted. It is clear that, for every small file sizes, very little performance gain can be achieved with a proxy. For a larger file size, a proxy gives significant performance gain. The major benefit of a proxy at rates of 64 and 128 kb/s is that it reduces the time spent in slow start. Since the proportion of slow start as part of the total transmission time decreases as the file sizes increases, the gain of TCP proxy increases. Besides, for a data rate of 256 kb/s, the gain increases further for larger file sizes. In this case, the link is underutilized not only during slow start, but also during fast retransmit and fast recovery after packet losses. For large files, fast retransmit and fast recovery becomes important. With proxy, the RLC throughput can significantly increase during fast retransmit and fast recovery after packet losses. To sum up, a proxy increase TCP performance for small data rates DCH mainly during the slow start, while for high data rates DCH both slow start phase and fast retransmit phase. Figure 11 compares, for different radio bearer rates, the average RLC throughput with and without proxy. For the proxy, different initial windows have been investigated. For a TCP configuration according to table 1, a proxy provides small gain for link rates of 64 and 128 kbps. Also, a large initial window of 10 segments in the proxy only marginally improves the results for these data rates. However, for the data rate of 256 kbps, a proxy with an initial window of 3 and 10 segments achieves the gain of 33% and 66% respectively. This shows that a aggressive initial TCP congestion window at proxy is particularly useful for radio links with high data rates whose delay bandwidth product is large.

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6 Conclusion We firstly study the TCP performance without split proxy over UMTS DCH channel under various delay bandwidth product. The simulation results show that: TCP performance in terms of RLC throughput is more vulnerable to the impact of TCP slow start and Internet packet loss under high bite rate DCH channels (thus large delay bandwidth scenarios). A large TCP initial congestion window can improve TCP performance degradation due to TCP slow start, especially under high DCH bite rates cases (thus large delay bandwidth product). For a given Block Error Rate (BER) caused by the radio transmission errors, a proper configuration of RLC MaxDAT can minimize the SDU error ratio provided to upper transport layer. To cope with large delay bandwidth product, we propose a split TCP proxy placed in between UMTS access network and Internet. By shortcutting the transmission of ACKs and pipelining for two split TCP connections, it can significantly improve the TCP performance under large delay bandwidth product scenarios. Simulation results show that: a split proxy increases TCP performance only during the slow start under low data rates DCH, while both slow start phase and fast retransmit phase under for high data rates DCH. Thus it brings more performance gain under high data rats DCH channels (thus high delay bandwidth product scenarios). Besides, the performance gain offered by proxy is larger for downloading large files than downloading small files. Finally, for the configuration of the split proxy, an aggressive initial TCP congestion window size (e.g. 10 MSS) at proxy is particularly useful for radio links with high data rates whose delay bandwidth product is large.

References [1] Kaaranen, H., et al.: UMTS Networks. Architechture, Mobility and Services. John-Wiley & Sons, New York [2] 3GPP, http://www.3gpp.org


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[3] Caceres, R.,, L.: Improving the Performance of Reliable Transport Protocols in Mobile Computing Environments. IEEE Journal on Selected Areas in Communications 13(5) (1995) [4] Balakrishnan, H., et al.: Improving Reliable Transport and Handoff Performance in Cellular Wireless Networks. ACM Wireless Networks 1(4) (1995) [5] Ameigeiras, P.J., Mogensen, P.: Imapct of TCP flow control on the radio resource managemnt of WCDMA Networks. In: VTC 2002 (2002) [6] 3GPP, Network Architechture, TS 23.002 (March 2000) [7] Luglio, M., et al.: On-board satellite ‘split TCP’ proxy. IEEE Journal on Selected Areas in Communications 22(2) (2004) [8] http://www.opent.com [9] RFC 3135, http://rfc-ref.org/RFC-TEXTS/3135/kw-proxy.html [10] Balakrishnan, H., et al.: Improving TCP/IP Performance over Wireless Networks. In: 1st ACM Conf.Mobile Commun. and Networking, Berkeley, CA (November 1995) [11] Bakre, A., Badrinath, B.R.: I-TCP: indirect TCP for Mobile hosts. In: 15th Int’l Conf. Distributed Communication Systems (May 1995) [12] Henderson, T.R., Katz, R.H.: Transport Protocols for Internet-Compatible Satelite Networs [13] Allman, M.: Increasing TCP’s initial Window, RFC 2414 (September 1998) [14] Inamura, H., et al.: TCP over 2.5 G and 3 G Wireless Networks, RFC 3481 (February 2003)

Use of Photonic Networks in Digital Cinema Postproduction Dailies Workflow Michal Krsek1, Paul Hearty2, Laurin Herr3, and Jurgen Schulze4 1

CESNET, z.s.p.o., Zikova 4, Praha 6, 160 00, The Czech Republic [email protected] 2 Rogers Communications Centre, Ryerson University, Toronto, Canada, M5B 2K3 [email protected] 3 CineGRID Inc., 5756 Ayala Avenue, Oakland, California 94609, USA [email protected] 4 California Institute for Telecommunications and Information Technology University of California at San Diego, 9500 Gilman Drive, MC 0436 La Jolla, California 92093-0436, USA [email protected]

Abstract. We describe a recent proof of concept demonstration of international, grid-based collaboration in Post Production for Digital Cinema. In the demonstration, very high resolution (4,096 x 2,160 Pixels x 24 Frame per second) Digital Cinema content captured in the Czech Republic was rendered using processing resources in the United States. Editing and color correction were carried out in real time by an editor/colorist in Canada under the artistic direction of a cinematographer in the Czech Republic. Final conforming, product assembly, and exhibition were carried out in the Czech Republic. The creative workflow demonstrated was made possible by a 10 GE fiber networking infrastructure and by grid-like resource management. Keywords: CineGrid, digital cinema, Post Production, high-speed photonic networks, networked collaboration.

1 Introduction CineGrid is a non-profit membership organization whose mission is to build an interdisciplinary community focused on the research, development and demonstration of networked collaborative tools, enabling the production, use and exchange of very high-quality digital media over high-speed photonic networks. One such application is the Digital Cinema. With resolutions of up to 4096 pixels horizontally x 2160 vertically at 24 frames per second, and with each pixel requiring up to 16 bits per Red, Green and Blue color channel, Digital Cinema provides not only exceptionally high quality imagery for exhibition, but also exceptional challenges in Production and Post Production. For example, when 4K x 2K image data is captured and output in “raw”, rather than in Red-Green-Blue format, the processing resources required to render the data from the sensor in a time-frame that is viable for Production are both substantial and rare. And, finally, the technical and P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 36–39, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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creative resources necessary to edit and color-correct digital cinema content are rare and geographically distributed, particularly given the realities of on-location shooting. CineGrid members believe that the existence of broadband photonic networks and of grid-based tools for the discovery, acquisition, provisioning, and management of creative and processing resources can facilitate Production and Post Production in Digital Cinema. They further believe that the network management, distributed resource management, and other tools adapted or developed to support such applications will be applicable to other high-demand applications, such as scientific visualization and collaboration. The importance of high-speed networks in enabling the Digital Cinema applications is evident when one considers the basic parameters of the medium.

2 Demonstration Objective and Concept The underlying objective of the demonstration was to show that Production and Post Production in Digital Cinema can be facilitated by using high-speed photonic networks and by grid-like management of distributed resources. The demonstration was conceived in two parts: • Capture in a 4K format at one location with rendering at another, in this case Prague and San Diego, respectively; • Edit and color correct with the editor/colorist at one location and the cinematographer at another, in this case Toronto and Prague respectively.

3 Approach, Strategy and Basic Technology For the first part of the demonstration, we captured 4K x 2K digital cinema imagery in Prague using a Dalsa Origin 16 camera and used processing-cluster resources at the California Institute of Telecommunications and Information Technology (Calit2) at the University of California San Diego to render the raw Bayer images1 to RGB. The

Fig. 1. Link and signal structure used for editing/color-correction demonstration 1

In the Bayer format used, the image is captured against a target 4096 x 2160 x 24 sampling structure, but the approximately 8.8 megapixel spatial sampling lattice is divided into 4-pixel blocks in which Green is sampled at twice the frequency of Red and Blue.

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key factor was to allow transfer of the “raw” 4K x 2K data from Prague to San Diego and of the rendered RGB data back to Prague within a viable time window for fullscale Production and Post Production. For the second part of the demonstration, we established a near-real-time workflow between the cinematographer in Prague and an editor/colorist in Toronto. The key factor at this stage was to establish a transcontinental interconnect that would support the delivery of workable image data from Prague to Toronto, a two-way telepresence between the cinematographer and the colorist, and bi-directional transfer of control data between the colorist’s control surface in Toronto and the cinematographer’s base processing system in Prague. The link and signal structure were as shown in Figure 1. 3.1 Network Connections The demonstration described here was presented at the 2007 meeting of the Global Lambda Integrated Facility2 in Prague. The network connections were established ad hoc and were as shown in Figure 2.

Fig. 2. Diagram of the Network used for the demonstration

4 Challenges and Results Several challenges had to be solved while preparing this demonstration. 2

GLIF is an international virtual organization that promotes standards for fiber optic (lambda) networking, involving dedicated, high-capacity circuits based on optical wavelengths, which terminate at exchange points known as GOLEs (GLIF Open Lightpath Exchanges).

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First, tuning up all the network equipment as well as processing nodes to work with very long high-speed circuits was necessary (e.g. jumbo frames had to be set on all the routers and switches and tune-up TCP window size on processing nodes to get maximum throughput). Second, we needed to render (de-Bayer) raw image data using parallel processing. Processing of one second of image data took about 23 seconds on one machine, so parallel processors were used to get nearly real-time performance. Time distribution of the de-Bayering is shown in Table 1. Table 1. Time Distribution of de-Bayering from Prague at San Diego cluster

Step Transfer Prague -> San Diego Processing on cluster Transfer San Diego -> Prague

Duration per one second of footage 4.4 seconds 9.6 seconds 9.1 seconds

The processing devices used for this demonstration were Linux based personal computers or servers that had well defined interfaces HD-SDI (SMPTE 292M) and 1000Base-T (IEEE 802.3ab). We tweaked those interfaces for best performance. Third, it was necessary to set up and maintain telepresence between Toronto and Prague. This two-way connection had to support the exchange of HDTV video as we as audio, with minimum latency and jitter. And, finally, we needed to tweak console/cluster communication for the color correction system used, a Baselight FOUR3. In its design configuration, this system is intended to support of separations of up to 50 meters between the control surface and the base cluster. In our case, we had a separation of approximately 7,500 km, resulting in lengthier communication delays.

5 Conclusions We demonstrated the feasibility of using of high-speed photonic networks for realtime Post Production of Digital Cinema source material. Both the colorist in Toronto and the cinematographer in Prague provided positive feedback – insertion of the long-distance photonic network between them had no effect on the collaborative creative activity. The high speed, low latency and zero jitter on the network, combined with the high resolution telepresence, allowed collaboration as if in the same room.

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BaseLight FOUR is a cluster-based color correction device developed by UK based vendor FilmLight.

Visualizing TCP/IP Network Flows to Control Congestion and Bottlenecks Nanjun Li1 and Werner Zorn2 1

Institute for Digital Communications, University of Edinburgh, EH9 3JL, United Kingdom [email protected] 2 Hasso Plattner Institute, University of Potsdam, 14480, Germany [email protected]

Abstract. Internet technological trends indicate that the link transmission speed has been significantly improved in the last decade, while the network nodes (routers and switches) have become the common bottleneck of Internet. Based on this insight, this paper applies the newly developed Visualized IP-based Network Simulator (VINS) to study TCP/IP networks formed on fast speed links. VINS design complies with the related RFCs and BSD operating system in three ways: IP networking; socket hosting and interfacing; service on the node. Two sample scenarios are presented regarding IP networking and TCP end-to-end congestion control for bulk data delivery such as video files, and handling packet overflow and delay on intermediate nodes. Keywords: TCP/IP, Performance Evaluation.

1 Introduction Several recent studies indicate that the bottleneck of today’s Internet has been shifted to the node, such as routers and switches, since the link speed has been significantly improved and even exceeded the traffic demands. However the existing network simulators still consider congestion issue over the links. This paper applies the newly developed Visualized IP-Network Simulator (VINS) into TCP/IP network performance evaluation where overflow and delay are frequent on the nodes. In Section 2 we briefly review the TCP/IP stack in BSD operating system [1], related IETF RFCs (i.e. [2][3][4]) and some existing network simulators. Section 3 introduces the design of VINS, which is a C++ Win32 application. We summarize VINS contribution as: IP networking, socket hosting/interfacing, and node store/relay services. The first example is presented in Section 4, illustrating the capability of VINS to simulate IP network with complex topology. The second scenario is presented in Section 5, as a study on TCP end-to-end congestion control and avoidance when packets can be delayed and lost on an intermediate node. By varying parameterized background traffic, we compare the performance of four TCP mainstream variants – Tahoe, Reno, NewReno and SACK, and find new insights using our evaluation method. We conclude in Section 6 and brief plan in future. P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 40–47, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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2 TCP/IP Networks and Existing Simulators To network unknown number of nodes, addressing/subnetting schemes are designed as Classful [2] and CIDR [3], which grant free and easy network maintenance and extension. The network topology is the result of all nodes’ routing tables (layer-3) and link connectivity as infrastructure (layer-2). In Internet, IP and routing table misconfiguration by mistakes (i.e., BGP mistakes) or malicious attacks is commonly observed [4][5], and may result in TCP disruption because of falsification of reachability. Besides the intermediate nodes, host nodes provide access points to users as well platforms for socket-based applications. These sockets choose TCP [6][7] and UDP [8] as transport modules. Each individual node in an IP network is an autonomous system working in asynchronous mode to handle unpredictable network events, such as timeout, packet arrival, departure and user request (Fig. 1). In BSD, IP interrupt queue is monolithic with default capacity to 50, and normally scheduled as first-in-first-out (FIFO). This design prevents parallelism causing packet flow disorder, which is found to be critical to TCP performance [10]. In the link layer, a node may have multiple interfaces and associated interface-send-queues for output. IP interrupt routine serves packets in two ways: 1) hosting, by triggering the transport routines like TCP, if the packet has reached its destination; 2) forwarding, by looking-up the routing table for the longest-prefix-matching neighbor node and relaying (Fig. 1).

Fig. 1. A BSD node has a monolithic FIFO queue at the IP layer

S. Floyd [11] and J. Stone [12] claim that in the wired networks, the majority of packet losses are caused by traffic congestion, while the losses due to link corruption instead of congestion are rare. Other studies (i.e. [13]) find that the node has become the main bottleneck of today’s Internet since the link transmission speed has been significantly improved and even exceeding traffic demands. We infer that congestion commonly occurs when the IP interrupt queue is overflowed despite of the router may

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still have large buffer. We also studied three existing simulators: NS-2 [14], OPNET [15] and OMNeT++ [16], which provide much needed tools and are widely used in network research. However their simulated networks might have some reality concerns while comparing with the implementation in real world: 1) IP module has been missing. 2) It might be expected if simulators can provide store-relay function and service time on the nodes. 3) Socket is not included in simulation, which is the interface bridging the user byte-streams and discrete protocol unit in the networks. In BSD, the indicators in the TCP control block (tcpcb), such as snd_nxt, rcv_nxt, snd_una etc, are pointing to the bytes in the so_snd and so_rcv buffers of a socket, with which TCP can guarantee data’s reliability and pursue high performance by properly adjusting the congestion window (cwnd := snd_nxt – snd_una).

3 Innovative Features of VINS As for network simulator classification (S.Y. Wang et al [17]), VINS is a “traditional” one for object-oriented and discrete-event features, an application layer software. Each node in VINS has a 32-bit IP address and forwarding prefix, which indicates its domain and potential size. Once the FIFO queue is overflow, new arriving packets will be dropped. The packet time-to-live (TTL) is initialized on the source host as 64. Router decrement packet’s TTL when relays it, or discards it if any of following events occurs: 1) TTL expiry. 2) Destination (of packet) is unreachable. 3) Routing table update / redirect. 4) Other ICMP errors stated in RFC792 and [18]. Service time for packet is simulated with a stochastic value taking certain probability distribution functions (PDF). VINS currently provides four choices: Deterministic, Gaussian, Uniform and Exponential. Generating random numbers takes unpredictable time, i.e., to create two Gaussian numbers we need the Box-Muller Transformation [19] contains uncertain times of loop, square and logarithm. We split off a process to generate the normalized random numbers for all nodes need to do is a linear transformation. VINS link is parameterized as latency and loss ratio per million packets. Packet is delayed and dropped in the given values, though in the wired networks such as Ethernet, the physical delay is not significant and link corruption (i.e. CRC corruption) is very rare.

4 Scenario 1: IP-Networking The first sample scenario (Fig. 2) demonstrates VINS capability in simulating multidomain networks with diverse transport modules. There are 14 applications running on different ports of the host nodes, exchanging protocol data units with UDP and TCP, including Tahoe, Reno, NewReno and SACK [20][21]. Two of them are set mute mode to receive data only: A:1312 (Tahoe) and N:880 (UDP), while the others are in talk mode and sending data to their peers. Mute UDP applications do not send any packet into the network, while mute TCP applications can acknowledge the received data with pure ACK packets.


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Fig. 2. Scenario 1 demonstrates VINS simulation of various transport modules, including UDP and TCP variances over a complex IP network

Table 1 is excerpted from the system report, showing the routes of end-to-end flows may be asymmetric on a bi-directional connection (as light-grayed): packets from B:80 to H:8080 goes through path B->O->E->V->R->G->H; the counterpart flow through path H->G->R->J->O->B. The routes depend on the IP configuration and connectivity of the network. Table 1. Routes and Protocols of Streams in Scenario 1 Src. M:917 H:799 B:80 H:8080 M:289 I:909 A:1312 P:303 S:2771 H:137 A:1001 N:880 X:365 Y:1224

Route M->L->J->R->G->H H->G->R->J->L->M B->O->E->V->R->G->H H->G->R->J->O->B M->L->J->R->F->I I->F->R->J->L->M A->D->E->J->L->P P->L->J->E->D->A S->K->U->W->R->G->H H->G->R->V->U->K->S A->D->E->V->R->F->N N->F->R->V->E->D->A X->T->R->V->E->D->Y Y->D->E->V->R->T->X

Dest. H:799 M:917 H:8080 B:80 I:909 M:289 P:303 A:1312 H:137 S:2771 N:880 A:1001 Y:1224 X:365

Reachability True True True True True True True True True True True True True True

Proto TAHOE NEWRENO NEWRENO RENO NEWRENO RENO TAHOE RENO SACK SACK UDP UDP SACK SACK

Mode TALK TALK TALK TALK TALK TALK MUTE TALK TALK TALK TALK MUTE TALK TALK

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This sample also exhibits that UDP flows can be flooding and slow down other flows’ performance, especially to TCP. Application from A:1001 to N:880 is an UDP application, VINS reports this flow possesses 30.7% packets that are relayed in the network. This is because the UDP protocol does not control the rate of injecting packets. UDP flood attack is commonly seen as flooding a victim router and forcing it to send many ICMP [9] messages, eventually quench the clients. This issue has also been described and addressed in [22].

5 Scenario 2: TCP Performance Evaluation TCP congestion control is performed on the two end-nodes over a connection. A commonly used evaluation scheme for TCP is to delay and drop packets on the links by varying the link latency and packet drop ratio within [0, 1]. However, this scheme does not properly reproduce the congestion issue in real world since packet losses are not independent. Second, the packet loss ratio can be reduced by TCP congestion avoidance, thus imposing constant loss ratio is not fair evaluate a TCP variance. Further, packet delay and loss are related issues, which might be expected to consider together. Our scheme is to vary parameterized background traffic across an intermediate node and investigate: statistics on the intermediate node; throughput on endpoint; impact of background traffic and TCP control variables (Fig. 3).

Fig. 3. TCP connection between A1:11 and B1:1011: all nodes have same mean packet service time 40 ms in exponential distribution. Background traffic is sent from X to Y.

The background traffic is send from X1, with interval t exponentially distributed and mean departure rate λ (packets/sec). The probability of interval t helds (1):

P (λ , t ) = λ ⋅ e

− λ ⋅t

(1)


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The probability of A packets arrival per second at R1 takes (2):

P ( A, λ ) =

λA ⋅ e −λ A!

(2)

We interest the utilization of R1, which is calculated with function (3): U i = Di ⋅ X i =

Di Bi

(3)

where X i is the mean service time to the packet; B i is the service rate, the reciprocal of X i , indicating the maximum departure rate of node i.

Fig. 4. Utilization of R1 illustrates the capability of TCP in exploring free bandwidth

Fig. 5. Tuning the departure rate from X, the arrival rate and departure rates of R1

We found Tahoe performs the best when background traffic is under 40 pkts/sec, but goes down as background traffic increasing. SACK exhibits its strength in a congestive environment. A series of experimental tests (R. Bruyero et al [23]) claim this ratio ranges between 1.16 and 1.29, which backs up our simulation results (as

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Fig. 6. Comparison among Tahoe, Reno, NewReno and SACK, for special interest we give the ratio of SACK on Reno

Fig. 7. Diagram of throughput on delay shows Tahoe is always under others

Fig. 6). Plotting throughput on given loss ratio and delay (i.e. Fig. 7), Tahoe always under-performs, however as we explained, not a fair test.

6 Conclusion To match the trend of fast link speed in simulation, we designed VINS as a tool for network design and research. We also devise a fairer method for TCP performance evaluation rather than imposing constant loss ratio and delay on the links, and find that Tahoe, which is deemed to be too gentle, performs the best over other three variances if network is not congestive. With increasing the background traffic rate, SACK achieves the best performance.


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VINS is being developed to accommodate wireless networks where contention and congestion may coexist, and nodes are demand to route for destination without IP configuration (Ad hoc networks). TCP’s traditional congestion avoidance needs improvements to deal with the new issues such as differentiating contention and congestion, and handling frequent route change etc.

References 1. Wright, G.W., Stevens, W.R.: TCP/IP Illustrated, vol. II. Addison Wesley, Reading (1994) 2. Postel, J.: Internet Protocol, RFC 791 3. Fuller, V.: Classless Inter-Domain Routing, CIDR: an Address Assignment and Aggregation Strategy, RFC 1519 4. Hengartner, U., Moon, S., Mortier, R., Diot, C.: Detection and analysis of routing loops in packet traces. In: 2nd ACM SIGCOMM Workshop on Internet measurement (2002) 5. Mahajan, R., Wetherall, D., Anderson, T.: Understanding BGP Misconfiguration. In: Proceedings of SIGCOMM, Pittsburgh, PA (2002) 6. Postel, J.: Transmission Control Protocol, RFC 793 7. Jacobson, V.: TCP Extensions for High Performance, RFC 1323 8. Postel, J.: User Datagram Protocol, RFC 768 9. Postel, J.: Internet Control Message Protocol, RFC 792 10. Bennett, J.C., Partridge, C., Shectman, N.: Packet Reordering is Not Pathological Network Behavior. IEEE/ACM Transaction on Networking 7 (1999) 11. Floyd, S.: A Report on Some Recent Developments in TCP Congestion Control. IEEE Communications Magazine (2001) 12. Stone, J., Partridge, C.: When the CRC and TCP Checksum Disagree. In: SIGCOMM Symposium on Communications Architectures and Protocols (2000) 13. Molinero-Fernández, P.: Circuit Switching in the Internet, Ph.D. Thesis, Stanford University (2003) 14. Fall, K., Varadhan, K.: The NS Manual (2006) 15. OPNET Technologies, Inc., http://opnet.com/ 16. Bless, R., Doll, M.: Integration of the FreeBSD TCP/IP-Stack into the Discrete Event simulator OMNET++. In: Proceedings of the 2004 Winter Simulation Conference (2004) 17. Wang, S.Y., Chou, C.L., Lin, C.C.: The Design and Implementation of the NCTUns Network Simulation Engine. Simulation Modeling Practice and Theory 15, 57–81 (2007) 18. Jacobson, V.: Congestion Avoidance and Control. ACM SIGCOMM (1988) 19. Box, G.E.P., Muller, M.E.: A Note on the Generation of Random Normal Deviates. Annals Math. Stat. 29, 610–611 (1958) 20. Mathis, M., Mahdavi, J., Floyd, S., et al.: TCP Selective Acknowledgment Options, RFC 2018 21. Floyd, S., Mathis, M.: An Extension to the Selective Acknowledgement (SACK) Option for TCP, RFC 2883 22. CERT® Advisory CA-1996-01 UDP Port Denial-of-Service Attack (1996) 23. Bruyeron, R., Hemon, B., Zhang, L.: Experimentations with TCP selective acknowledgment. ACM SIGCOMM Computer Communication Review 28(2), 54–77 (1998)

ZBMF: Zone Based Message Ferrying for Disruption Tolerant Networks Bahadir K. Polat, Moazzam Khan, Nikhil Tuli, and Pooja Kailay College of Computing Georgia Institute of Technology {baha,moazzam,nikhiltuli,poojakailay}@gatech.edu

Abstract. We introduce Zone Based Message Ferrying (ZBMF), a hybrid protocol for Disruption Tolerant Networks (DTN) which combines the concept of message ferrying with the well known DTN routing protocols. ZBMF, transforms the disrupted network space into a connected network topology by dividing it into overlapping ferry routing zones. ZBMF gives DTN message routing a sense of direction, thus providing both contained dissemination of copies of messages and faster delivery of messages in the network. ZBMF does not dictate a strict movement pattern to message ferries, which can be very useful in the real world. Rescue helicopters, school buses, tanks or soldiers in the battle field, all can be designated as message ferries and carry messages without changing their activity and movement schedules. Due to its low overhead, ZBMF can be used to cover large areas of disrupted communication such as cities and large disaster areas. Finally, we implement our ZBMF on the Opportunistic Network Environment (ONE) Simulator and evaluate it as performing better in terms of overhead and latency than other well known protocols for Disruption Tolerant Networks while maintaining equally high delivery ratios. Keywords: Disruption Tolerant Networks, Message Ferrying, Space Path, Time Path.

1 Introduction Disruption tolerant networks (DTN) are special networks in the wireless communication concept, in which there exist no space path between the nodes throughout most of the network duration. In disruption tolerant networking, the nodes communicate when they meet each other. The communication duration in these meeting times is limited. Regular routing protocols for wireless networks such as AODV, DSR, DSDV, and ZRP all require that there exists a space path between the source and the destination [1]. In these protocols, the messages between source and destination are divided into packets and routed through known routes that exist for a significant amount of time. Since, in DTNs, there is no stable path between the source and destination, these routing protocols suffer from low delivery success ratios and high routing overheads. In DTN, these protocols will spend impractical amounts of time and control messages to P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 48–59, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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discover a path between the source and destination, which does not exist most of the time in DTNs. So these protocols may never discover a path between the source and destination. Even in circumstances when such a path is found, the chances of disruption are high, which initiates the discovery process again. As a result, these protocols will spend the majority of the network time and energy in this discovery-disruption cycle, creating high overheads and will have extremely low delivery success ratios, with high delays. From the argument above, we conclude that DTNs need a different approach for routing. The main scheme for routing in DTN requires that the nodes transfer complete messages instead of dividing them into packets. The routing protocols in DTN perform message based transfers between nodes. The main paradigm in DTN routing is “store and carry forward”. In this scheme the nodes after receiving messages destined to other nodes store these messages and carry them for a certain amount of time. When the nodes meet with each other they exchange messages obeying certain rules specified in the particular routing protocol. This way messages are carried physically in the DTN and reach their destinations. There are many protocols such as Epidemic [4], Prophet [5], MaxProp [6], Spray and Wait [7] that specify certain rules for carrying and forwarding messages among the nodes in the DTN. We will talk about certain characteristics of these routing protocols while we explain our protocol. Message Ferrying is a particular concept in DTN [2]. The DTN routing protocols mentioned above depend on the fact that the communicating nodes are mobile enough to meet with each other to exchange messages. What happens in the case when the nodes have limited radio range, very low or no mobility and are positioned far (out of radio range) from each other so that they have almost no chance of meeting with each other. In message ferrying this problem is solved by designating mobile nodes, ferries, which travel the area and physically carry messages among these nodes.

2 Zone Based Message Ferrying Zone based message ferrying (ZBMF) can be seen as a hybrid routing protocol that uses certain concepts from DTN routing and message ferrying. ZBMF uses zone information to route messages in the network. ZBMF makes the following assumptions about the mobility model. The disrupted ordinary nodes, in the network, are grouped together into message ferrying zones based on geographical position. The message ferries designated to a certain zone based on either their local movement pattern or on a particular specification stay inside their local zones most of the time, but are free to move to carry out their job inside their local zones. (Refer to Section 4 for details on plausible solutions for the initial setup of zones). Under the aforementioned mobility pattern the routing domain is divided into overlapping zones. The overlap areas between the zones constitute links among the zones. This way, the zones form a connected network. Figure 1 illustrates the mapping of the disrupted network space into a connected network of zones. ZBMF uses message ferrying inside the zones. When a message reaches to the zone of its destination it is carried physically to its destination by a message ferry local to that zone. ZBMF forwards the messages through zone routes. The protocol uses the shortest zone route to forward the messages to the destination. The major difference between ZBMF and other DTN routing protocols is that

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ZBMF incorporates a sense of direction into the routing process. A simple example can show us this difference. Let us say that a node S has a message destined to a node D (see figure 1 for visual representation). Node S only infects the ferries in the zones that belong to the shortest zone path to the destination D. Since the zones are connected and we pick the shortest zone path we are assured that the message will get to its destination D with very small delay, and high delivery success ratio. We also obtain minimum overhead, since we are not infecting any nodes from other zones that are not in the zone path. This way, our protocol can cover big areas and provide high success ratios, small delays and minimum overhead. 2.1 Zone Based Message Ferrying, the Protocol We would like to present the entities and concepts in ZBMF. In ZBMF, the nodes are identified either as ferries or ordinary nodes. Message Ferry: A message ferry is a mobile wireless node that physically carries/forwards the messages of the ordinary nodes. Ordinary Node: An ordinary node is a wireless node with limited radio range and very low or no mobility. Ordinary nodes are the sources and destinations of the data messages in the network. Ordinary Nodes use the ferries to carry their messages for them. In zone based message ferrying the communication domain is partitioned into overlapping zones. From this concept emerge two types of communication, intrazone communication and inter-zone communication. Intra-zone routing uses the principles of message ferrying, where the ferries of the zone carry and forward the messages of the ordinary nodes in their zones. In inter-zone routing the ferries from different zones meet with each other and selectively forward messages destined for other zones than their own. The address of any node in the network consists of the zone id and the network address of the node. The ordinary nodes maintain the addresses of all the ordinary nodes in the network. The ferries maintain a routing table in which they store next hop zones only. Since the addresses already contain the zone ids, ferries do not need to maintain long routing tables for each ordinary node in the network. These concepts and entities are presented in figure 1 to give us an easy to understand visual example. The protocol consists of two subsections, intra-zone routing and inter-zone routing. Intra-zone routing: Inside the zones, the protocol uses message ferrying principles. The ordinary nodes (ON) maintain a record of all the addresses of the other ONs in the network. The address of any node in the network consists of the zone id and the network address of the node. When the ON encounters a ferry, it first extracts the zone id of the ferry from the ferry’s address. If the ferry belongs to the same zone as the ON does, it then checks to see if the ferry already has the message by checking the message id list sent to it by the ferry. If the ferry does not have the message, the ON forwards the message to the ferry. Otherwise it does not forward the message. When a ferry sees another ON it forwards its message list to the ON. After the ON performs the checks mentioned above, it sends the message to the ferry or not. The ferry also checks its message list to see if it has any messages destined to that ON, if yes, the ferry forwards the messages to the ON. When the ferries contact each other they exchange message lists. The format of the messages is illustrated by Figure 2. The ferries first extract each others zone id’s and check to see if they belong to the same zone. If the ferries belong to the same zone they do not forward each other the messages that are destined to ONs in their own zones, they


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deliver those messages directly. The messages that have a different destination zone are exchanged between the ferries of the same zone, if the ferries do not have these messages already. Although this exchange adds some overhead due to infection, it also speeds up the forwarding of messages through the intermediate zones on the zone path. In case the ferries belong to different zones then they follow the protocol for inter-zone routing. Inter-zone routing: Inter-zone routing is concerned with the proper forwarding of the messages between zones. In the inter-zone routing the contacting elements of the network are the ferries. When two ferries contact each other, they exchange message lists. The ferries extract each other’s zone IDs.

Fig. 1. Zone based Message Ferrying Source Address Destination Address Zone_ID + Network Address Zone_ID + Network Address Flags Msg. Address of the Ferry Msg_List_Exch. 0/1 ID Zone_ID + Network Address Zone_Advertisement 0/1 Message Body

TTL

Fig. 2. Message Format

Destination Zone

Next Hop Zone

Zone 1 Zone 2

Zone 3 Zone 4 Fig. 3. Routing Table of Ferries

They first check to see if they have any messages destined to that zone. The routing table maintained by each ferry (shown in Figure 3) contains the mapping of the destination zone with the next hop zone according to the shortest path between the source and destination zone. If they do, then they check the message list sent by

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B.K. Polat et al. Input: Message Extract: Zone_ID Check: If Message = Msg_List If (Msg_List) If Zone_ID = Own_Zone_ID Check: Msg_List if message_m is not in the Msg_List && message_m_destination != own_zone (ensures that a ferry delivers a message destined to its zone itself) Forward: message, if the other ferry is not sending you a message else message_m = forward_queue->next_message; Loop: until forward_queue = empty or connection is lost. Else Zone_ID != Own_Zone_ID Check: Routing table for the next hop zones for messages in the forward queue which are destined to zones other than own_zone if have a message_m destined for the other ferry with Zone_ID Check: Msg_List if message_m is not in the Msg_List Forward: message _m, if the other ferry is not sending a message else message_m = forward_queue->next_message Loop: until forward_queue = empty or connection is lost. else have no message destined for the other ferry with Zone_ID Else (!Msg_List) Store: received message in the forward queue

Fig. 4. Algorithm for message forwarding between ferries

the other ferry to see if that ferry already has the message. If the other ferry does have the message already no further step is taken. If the other ferry does not have the message, the ferries forward their messages. If the ferries belong to the same zone they follow the protocol for intra-zone routing mentioned above. The algorithm for message exchange between ferries is presented in Figure 4.

3 Evaluation The evaluation is done in a simulation environment with a special Delay Tolerant Network simulator called Opportunistic Network Environment (ONE)[3], developed by researchers at the Helsinki Institute of Technology. As the simulation environment – the placement of the zones and the membership of individual zones (i.e. the ordinary nodes and the ferries) needed to be consistent for comparison with other protocols, we develop multiple standard network topologies. This enables us to record repeatable and fair measurements during the evaluation of the ZBMF protocol. 3.1 The Simulation Environment We compared ZBMF routing protocol to four traditional routing schemes, enumerated as follows: i.

Epidemic Routing: The basic routing protocol that simply floods a message that is generated by an ordinary node. The ferries transmit the message


ii.

iii.

iv.

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without any sense of direction or path, to all the ferries/ordinary nodes that fall in its movement path. Expectedly, the protocol has a disadvantage of extremely high message overhead but it makes up for it with its high delivery ratio [4]. PROPHET Routing: The PROPHET scheme is a novel probabilistic routing scheme whose algorithm relies on calculation of delivery predictability to forward messages to the reliable node. The probability is used to decide if one node is reliable to forward message to. The routing has lesser overhead than epidemic scheme but the actual values of probabilities are crucial for success of the scheme [5]. MaxProp Routing: MaxProp is based on prioritizing both the schedule of packets transmitted to other peers and the schedule of packets to be dropped. These priorities are based on the path likelihoods to peers according to historical data and also on several complementary mechanisms, including acknowledgments, a head-start for new packets, and lists of previous intermediaries [6]. Spray and Wait Routing: A scheme that “sprays” a fixed number of copies into the network, and then “waits” till one of these nodes meets the destination. This scheme expectedly needs to be tuned for each topology because the number of “sprayings” have to be sufficient enough for giving the scheme a reasonable chance to deliver the message packet [7].

3.2 Simulation Parameters The simulation environment as explained above involves setting up network topologies, each of whose zones are connected to each other by overlapping zone boundaries. The ordinary nodes in the network have a transmit range of 50m and the message ferries have a transmit range of 100m. The messages distributed across the network are consistent for all the routing protocols and in all topologies. The message TTLs (time to live) are set at 2000mSecs and the simulations are run for 20,000mSecs. There are two ferry speeds for each simulation to measure the effects of ferry speed to the simulation results. 3.3 Simulation Metrics • • •

Delivery Success Percentage: The percentage of the messages delivered successfully to the intended destination station, among the total message events generated. Overhead Ratio: The ratio of the number of the overhead messages generated while transmitting messages among nodes/ferries to the total number of generated messages. Median Latency: The time delay between the generation of a message event and the delivery of the message is called the latency of the message event. The median latency for the simulation is the median of all the latency values generated across the simulation.

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Fig. 5. Delivery Success Ratio for Slow Ferry Speed

3.4 Simulation Results The delivery success ratio graph Fig. 5 indicates that the success ratio for ZBMF is almost equally as high as Epidemic routing, PROPHET routing and MaxProp Routing schemes because the ZBMF Routing scheme uses message ferrying zones that are connected through overlapping nodes and boundaries. The topology 4 consists of a straight line of zones and thus the PROPHET scheme does not do well here as it relies on probabilities to route messages through zones, which drop to low values as the network extends in one direction. Spray and Wait routing scheme gives a success percentage of nearly 25 % because the number of sprayings have a drastic effect on the delivery success. In Spray and Wait, the number of copies that a message can have, is a fixed number. If this number is too small, the messages have a lower chance of delivery. On the other hand if the number of allowed copies is too big, then the schemes suffer from high overhead. The graph Fig. 6 for the delivery success ratio when the speed of the ferries is set to fast is almost identical to the previous graph. This is because the message TTL throughout the simulations is set to a generous value (i.e. 2000 mSecs) as compared to the total time of simulation. This ensures that as long as the packets are not lost or are not forwarded to zones with no path to the destination zone, the packets have a reasonable chance of getting to the destination node. The fast ferry results in only a small improvement of the success ratio for the five routing protocols. The graph Fig. 7 shows the most beneficial aspect of the ZBMF Routing protocol. We see that the Epidemic Routing scheme has a mean overhead of 65, which is in line with expectations because Epidemic Routing simply floods the message across all nodes and ferries. Prophet and MaxProp are probabilistic based routing schemes and thus they have a significant decrease in the overhead as compared to Epidemic Scheme because they do not flood the packet across all nodes but transmit packets based on probability values. Spray and Wait protocol has a low overhead because the algorithm restricts the number of packets that can be replicated which decreases the number of successfully delivered messages. The ferries transmit copies of packets to nearby ferries and try to deliver the last replicated packet themselves; but since very few of the ferries can travel to the transmission range of the destination, the ferries fail to deliver the last packet to the destinations most of the time. As a result, the Spray and Wait protocol’s low overhead has to be taken in correct perspective – the restriction in number of packets translates into lower number


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of overhead messages and hence lower overhead but also into significant loss of delivery success. The overhead in the fast ferry case is slightly more for epidemic routing, and that is because the ferries transmit more packets per simulation and hence the overhead rises. The Spray and Wait and the probabilistic protocols remain unaffected by ferry speed, as expected, due to their methodology of selective transmissions as opposed to the flooding approach taken by Epidemic Routing.

Fig. 6. Delivery Success Ratio for Fast Ferry Speed

Fig. 7. Overhead Ratio for Slow Ferry Speed

The mean latency graph of Figure 9 plots the median latency values of successful packets for all topologies and routing protocols. Our ZBMF Protocol has the second lowest median latency for all the topologies. This can be attributed to the sense of direction and shortest zone path approach that our protocol implements which result in faster delivery of packets, hence decreasing the median latency. It is interesting to note that the Spray and Wait protocol has the minimum median latency values among all the protocols. This can be explained on the basis of the restriction on the number of packets allowed for replication. The replicated packets are either delivered to the destination zone by the ferries or they are dropped because more replications cannot be permitted. Thus, the only successful packets are the ones lying in close proximity of the originating node and hence, the lower latency. The median latency graph for

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the fast ferry case is essentially unchanged except for the SpraynWait protocol where, due to the limited number of the successful packets, the ferry speed change results in significant savings in time taken.

Fig. 8. Overhead Ratio for Fast Ferry Speed latency

Fig. 9. Median Latency for Slow Ferry Speed

Fig. 10. Median Latency for Slow Ferry Speed


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4 Zone Setup for ZBMF ZBMF uses zone information to route messages in the network. Successful creation of overlapped zones through which messages propagate from one zone to another is crucial for ZBMF. For this purpose we have devised scheme that sets up the zones in the network space for ZBMF. The detailed implementation of this scheme is left as future work. The scheme for setting up the zones in the network for ZBMF introduces only a one-time delay and overhead at the beginning. Once the set up is completed, ZBMF performs routing and no additional overhead is introduced. Our scheme also requires that we know the geographic coordinates of the static ordinary nodes in the network. The position information of the static ordinary nodes is accumulated at a central node which receives feedback from the mobile ferries about the frequencies with which they detect the static ordinary nodes. This information is used to divide the network space into zones and designate ferries to each zone based on their frequency of detecting other nodes. All the nodes flood the network with the Mac addresses of all the nodes they detect. After a certain settling period “P”, whose length depends on the size of the network all the nodes learn the Mac Address of all the other nodes in the network. The static ordinary node with the smallest Mac Address becomes the central node “CN”. Since CN has the smallest Mac Address it is automatically recognized by all the nodes in the network as the central node. Once the CN is configured, the ferries send the geographical coordinates of the static ordinary nodes they detect to the CN. Once the coordinates of the static ordinary nodes are known, the CN makes an initial setup of zones and groups the static ordinary nodes into zones. The initial set up of the zones is dependent solely on the coordinates of the static ordinary nodes. The CN gathers detection frequency messages from the ferries. These messages contain the Mac addresses of the static ordinary nodes ferries detect and the ferries' frequency of detecting them. The CN then gathers this information and makes the necessary modifications to the zone boundaries if necessary so that no single zone is left without an assigned ferry. After this set up is complete each static ordinary node and ferry is assigned to a zone. This assignment is based on the fact that if a ferry visits a specified percentage (node visit percentage “np%”) of the nodes in a zone with a specified frequency (node visit frequency threshold “nv%”), then the ferry is assigned to that zone. And finally this information is flooded to the network

Ferry ID

Mac Address Ferry n

of

Ordinary Node Detected

Frequency detection

Mac Address of Ord. Node 1

24

...

...

Mac Address of Ord. Node n

17

of

Fig. 11. Data collected by Central Node for each ferry for the predetermined duration T

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for all nodes. At the end of this procedure, all the nodes and the ferries in the network get forwarded the zone configuration of the network and extract their respective zones and zone routing tables.

5 Conclusion We proposed a new routing protocol for Disruption Tolerant Networks. Zone based message ferrying protocol (ZBMF) combines the concept of message ferrying with traditional DTN routing protocols. ZBMF routes messages through shortest paths of ferry zones. The major motivation behind ZBMF is to effectively route messages in one direction through the shortest paths possible. We then implemented and evaluated the performance of ZBMF. Our evaluations demonstrate the potential of the ZBMF Protocol, especially its ability to use routing tables which leads to decrease in the overhead and latency while still maintaining a high rate of delivery success. ZBMF allows mobile nodes, designated as message ferries, to operate freely in their zones while carrying messages among disrupted nodes. This way no special configuration or strict schedule is needed for message ferries. After the initial zone setup overhead, ZBMF routes messages with very low overhead through shortest paths. These features enable ZBMF protocol to be applied to large networks with low costs and render high performance.

6 Future Work The current work is done under the assumption of connected zones in the topology. But to partition the challenged network into zones is a nontrivial problem, as discussed in previous sections. The zone partitioning is important because we can construct the routing tables only on the basis of the relative placement of the zones. One such method could be electing a central node which would collect information from the individual nodes and then partition them into zones. There are multiple issues that need to be taken care of in this scenario as discussed in the section for Zone Setup. We also found that the performance of the ZBMF Protocol cannot be compared in the true sense with the SpraynWait Protocol without modifying the latter’s parameters, like number of allowed replications, according to the actual topology in each case. We also need to understand the possible effect of the changes in the environment variables that were kept consistent till now, like the transmit ranges of the ferries and zones, the message TTL, scalability problems in case of large number of zones. In the future, we would like to perform further analysis of the effects of these factors also. Acknowledgments. We acknowledge the supervision provided to us by Dr. Mostafa Ammar during the course of this project. His valued advice enabled us to achieve valuable results and a new scheme for DTNs.

References 1. Fall, K.: A Delay-Tolerant Network Architecture for Challenged Internets, Applications, Technologies, Architectures, and Protocols for Computer Communication. In: Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, Karlsruhe, Germany, pp. 27–34 (2003)


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2. Zhao, W., Ammar, M., Zegura, E.: A Message Ferrying Approach for Data Delivery in Sparse Mobile Ad Hoc Networks International Symposium on Mobile Ad Hoc Networking and Computing. In: Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing, Roppongi Hills, Tokyo, Japan, pp. 187–198 (2004) 3. Opportunistic Network Environment (ONE), Delay-tolerant Networking at TKK Netlab Helsinki Institute of Technology, http://www.netlab.hut.fi/~jo/dtn/index.html#one 4. Vahdat, A., Becker, D.: Epidemic routing for partially connected ad hoc networks. Duke University (2000) 5. Lindgren, A., Doria, A., Schelen, O.: Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review (2003) 6. Burgess, J., Gallagher, B., Jensen, D., Levine, B.N.: MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks. In: Proc. IEEE INFOCOM (April 2006) 7. Spyropoulos, T., Psounis, K., Raghavendra, C.S.: Spray and wait: An Efficient Routing Scheme for Intermittently connected mobile networks. In: Proceedings of the 2005 ACM SIGCOMM workshop on Applications, Technologies, Architectures, and Protocols for Computer Communication Delay-tolerant networking (2005)

Improving Query Mechanisms for Unstructured Peer-to-Peer Networks Guangwei Fang1 and Xiao Zheng2 1

College of Mathematics and Computer Science, Yichun University, 336000 Yichun, China [email protected] 2 School of Computer Science, Anhui University of Technology, 243002 Maanshan, China [email protected]

Abstract. For unstructured peer-to-peer networks, one of the methods for improving query performance is informed routing. This paper introduces an ant-like query routing method. This method views query messages as artificial ants, and utilizes pheromone as routing hints which direct transferring query messages to nodes owning more resources. This paper presents generation and update rule of pheromone, routing policy for artificial ants as well. In addition, entering and quitting of nodes is considered. Our method supports high scalability and is suitable for large-scale P2P networks. By adjusting the number of artificial ants dynamically, a better tradeoff between cost and quality could be achieved. Keywords: P2P networks, query routing, ant colony system.

1

Introduction

Peer-to-peer(P2P) networks have been extensively studied in recent years. One of the key open challenges is the organization and resources query mechanism in P2P systems. Although with the advantage of faster query, structured P2P networks are extremely sensitive to nodes entering and leaving the system. However for unstructured P2P networks there is no limit on data placement, and extra process on peers entering and leaving, e.g. resources migration, and position recalculation of resources store, it have availability, and suits dynamic computing situation. In unstructured P2P networks, topology and query routing are the key factors of influencing query performance, in which topology impact average search delay, whereas the latter impact recall and cost. The query routing technique can be categorized to blinded search and informed search. Blinded search is adopted by earlier unstructured P2P networks, e.g. Gnutella, the largest open P2P network in operation, uses a breadth-first search (BFS) traversal with depth limit to forward query messages. Henceforth some improved algorithms appear. But these algorithms have less efficiency, which produce more load and useless query messages. This paper introduces a pheromone based search technique inspired by the ant colony algorithm. Different from the methods of building an index over the P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 60–67, 2009. c ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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data of all neighbors or limiting the message flooding scope to improve search performance, our technique puts different amount of pheromone on different nodes in terms of success ratio of visiting resources, which makes the nodes owning more resources receive more query.

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Related Work

Search approaches of unstructured P2P networks can be categorized to blinded search and informed search. Gnutella uses blinded flooding-based query algorithm to forward messages. Although with the advantages of simple implementation mechanism, quick response speed, and suiting dynamic joining and leaving of nodes, the algorithm is not extremely scalable. Each individual query generates a large amount of traffic so that large systems quickly become overwhelmed by the queryinduced load. Reference [2] improved Gnutellas algorithm and presented a Random Walk algorithm. In Random Walk, each node forwards k query messages, called walkers, randomly to its k neighbors. Random Walk can adjust the number of walkers in order to achieve a trade-off between search success ratio and the number of messages. This algorithm has become a most popular message forward method. For example, Freenet uses this algorithm with k=1[9]. Each node forwards the query to a single neighbor, and waits for a definite response from the neighbor before forwarding the query to another neighbor (if the query was not satisfied), or forwarding results back to the query source (if the query was satisfied). Reference [4] studies the behavior of Random Walk in a power-law random network. They found when selecting those nodes with more degrees, better search performance will be got. However its scalability lowers because almost every query message will be sent to these nodes, which will burden almost all query messages in the whole network. These improvements of messages forward methods only make the number of message under control, and can not increase search efficiency. Because every node has the same probability of being queried, the capability of successful search lies on the density of resources replica and the number of traversed nodes. Beverly Yang etc. suggested a Directed BFS technique and a Local indices technique which are different from the blinded search mentioned above [1]. The Directed BFS technique queries a restricted set of nodes intelligently selected to maximize the probability that the query will be answered. If we allow nodes to answer queries on behalf of other nodes, then we can still reduce the number of nodes that process a query without decreasing the number of results. In the Local Indices technique, each node n maintains an index over the data of all nodes within r hops of itself, where r is a system-wide variable known as the radius of the index. When a node receives a Query message, it can process the query on behalf of every node within r hops. In this way, the data of many nodes can be searched by processing the query at few nodes, thereby maintaining satisfaction and number of results while keeping costs low. When r is small, the amount of metadata a node must index is also quite small. As a result, Local Indices

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with small r should be easily accepted by a loosely controlled system such as Gnutella. Our work aims to search technique in decentralized and unstructured P2P networks[2]. The presented algorithm uses accumulated pheromone, which can be seen as experience, in previous searching to direct latest query routing. Each node need not maintain the index over the data of its neighbors, which lowers the cost of maintenance.

3

Problem Descriptions

An unstructured P2P network is defined as a quaternion G(V,E,R,C ). V is a set of nodes denoting all peers in the network, i.e. V = {vi |i = 1, ..., n}. E is a set of edges which denotes all links in the network, i.e. E = {eij (vi , vj )|vi , vj ∈ V }. R = ri |i = 1, ..., m is a resources set denoting all shared resources in G. C is a resource function C : V → P(R) that maps each node v ∈ V to a subset of R, where P (R) is a power set of R. The problem is that given such G and r ∈ R, how to solve V subject to V = {v|r ∈ C(v) v ∈ V }. In a real decentralized and unstructured P2P network, nobody knows the global distribution of shared resources, and there is not any index of whole resources, still not any position information of resources. Accordingly, the algorithm must traverse nodes in terms of a certain policy, i.e. form a source node, along edges to reach some nodes, and query the wanted resource r at having reached nodes. When the number of elements in set V is very large, a tradeoff must be made between recall and cost, e.g. response time, load. A well-performed algorithm should strike a best recall in a desired response time, and simultaneously make use of fewer query messages, to lower the load in the network as possible.

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Algorithm Design

The principle of Ant Colony System(ACS) is using pheromone remaining in paths to indirectly exchange the path information among ants, whereas the pheromone is deposited by former ants passing by the path, which represents some experience knowledge. Several researchers have attempted to use the notion of ant pheromones for network-routing mechanisms [3] and service selection technique [6]. Our work also follows these thoughts. For each query request, n query messages are generated. A query message is corresponding to an artificial ant. An artificial ant selects a neighbor as its next hop in terms of a routing rule, and deposits pheromone on its passing path. When finding the wanted resource on a certain node, it will update the pheromone around the node. Moreover, the pheromone remaining on the path will volatilize in period. Every artificial ant has a life-span. When its life value is zero, the message denoted by an artificial ant will be abandoned. The objective of artificial ants is to find target resources as many as possible under certain restricted conditions.


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Message Routing

In order to transfer the message after it reaching a node, every node should maintain a message routing table. Simulating the behaviors of ants, our algorithm uses pheromone to direct the routing of query messages. Accordingly, the message routing table is a pheromone table composed of pheromone on the paths to neighbors. The item of a pheromone table is composed of a neighbor node and the pheromone on the relative path, i.e. a pair (n, p). n denotes a neighbor node, p denotes pheromone amount, and (n, p) denotes that the amount of pheromone on the path from the local node to neighbor n is p. The amount of pheromone represents the success ratio of previous searching at the corresponding node. More pheromone denotes higher success ratio. Therefore transferring query messages to the node having more pheromone may get a higher searching ratio. The probability of artificial ant k selecting neighbor j at node i is defined as ⎧ ph(i, j) ⎪ ⎨ , j ∈ J(i) − T abu(k) Σ (1) pk (i, j) = u∈J(i)−T abu(k) ph(i, u) ⎪ ⎩ 0, j ∈ J(i) − T abu(k) where J (i) is a set of neighbors of node i, Tabu(k ) represents the nodes ant k having passed, and ph(i, j ) represents the pheromone amount on the path from i to j. 4.2

Pheromone Generation and Updating

Pheromone plays an important role in ant routing which directs ant selecting path. So how to generate and update pheromone is a key to influence algorithm performance. In ACS, local update and global update rule is applied to update the pheromone on the selected path. Our pheromone update policy based on the principle of ACS and makes some improvement to satisfy search requirements. In the following cases, pheromone will be generated and updated. 1) As behaviors of real ants, query messages will deposit pheromone on the path passed by. Its new generated pheromone will add to the pheromone remained on the path. Let phi (j) denote the pheromone amount on the path from local node i to neighbor j, phi (j) = α · phi (j) + (1 − α)Δp1

(2)

where α ∈ (0, 1) and Δp1 is a constant. 2) When having found a target resource at node n, the artificial ant would diffuse pheromone to all neighbors. Receiving a message of pheromone update, a neighbor would update pheromone in the corresponding item of its pheromone table. (3) phm (n) = β · phm (n) + (1 − β)Δp2 , m ∈ J(n) where β ∈ (0, 1) and Δp2 is a constant. m belongs to a set of neighbors of nodes n.

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3) For each item of the pheromone table, an update will be done in period. phi (n) = ρ · phi (n),

with ρ ∈ (0, 1)

(4)

Thus if a neighbor is not always visited, its pheromone amount will be closer to zero. Such node may lack resources. 4) If a new node enters the P2P network, it will send update messages to all its neighbors. In this situation, update neighbors pheromone table by formula (3). 5) If a new resource is stored in a node, it will send update messages to all its neighbors. In this situation, update neighbors pheromone table by formula (3). 4.3

Life-Span Control

Every artificial ant has life-span, which could control the number of messages transmitting in the network and insure there are no more messages transferred ceaselessly after a query is over. Our algorithm also uses TTL to control messages life-span. When an artificial ant is created, its TTL will be set to an initial value. Hereafter at each passing node, the artificial ants TTL will be updated. If not found target resources, the ant will decrease TTL. If found, nothing done so that the ant will visit more nodes. If all neighbors are in the tabu table, the ants TTL will be set to zero. So the ant will not be transferred and be killed. When ant k at node i, the updating rule of its TTL is: ⎧ ⎪ ⎨ T T L(k) − 1, r(k) ∈ C(i) T T L(k), r(k) ∈ C(i) T T L(k) = (5) ⎪ ⎩ 0, J(i) ⊆ T abu(k) where r (k ) represents the target resource of ant k wanting to find. C (i) is the set of resources at node i. Based on above discussion, pheromone table update algorithm and message routing algorithm are described as follows. Algorithm 1. Pheromone table updating algorithm. This algorithm runs after receiving a pheromone update message. In the following function, the parameter node1 is a local node, and node2 is a neighbor. UpdatePheromone(Node node1,node2,float Ph) { node1.PheromoneTable[node2]=blta* node1.PheromoneTable[node2]+(1-blta)*Ph } Algorithm 2. Message routing algorithm. boolean HandleMessage (Message Query, Node node) { //if the node ever received this message, delete it. if SeenMessage(Query,node) return false; //if there is not a target resource, the TTL minus 1, otherwise diffuse pheromone to neighbors. if (NoFound)


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Query.TTL--; else for any nodei in node.neighbour UpdatePheromone(node,nodei,ph); //if all neighbor have been visited, set TTL to zero. if (all node.neighbour in Query.Tabu) Query.TTL=0; //if TTL equals to zero, delete the message. if(Query.TTL==0) return false; //Select a neighbor by (1), and forward the message to it. nextnode=SelectNextNode(node.neighbour); ForwardMessage(Query, nextnode); //Update the pheromone on the path to the next hop by (2). Update(node,nextnode); return true; }

5

Simulations and Performance Analysis

NeuroGrid simulator[7] is a Java based P2P network simulator which emphasizes particularly on simulation and performance test for unstructured P2P network. The simulator is initially used to evaluate a search algorithm called NeuroGrid[8]. Due to open source software, it also can be modified to simulate user designed search algorithm. This paper uses NeuroGrid simulator to simulate our algorithm and evaluate its performance. The parameters in our experiment are showed in table 1. The experiment selects randomly a node as a start node of query. Each experiment consists of at least 10000 trials and the average values are calculated as experiment results subsequently. This paper implements two experiments. Experiment 1 calculates our algorithms search efficiency and the total number of query messages under the variety number of ants. Search efficiency is the ratio of the number of success search to the total. The number of messages could impact network load and computing resource in node. Under the precondition Table 1. Comparison of four algorithms Configuration

Value

No. of No. of No. of No. of No. of No. of Initial

1000 1000 100 4 4 2 7

nodes documents keys neighbors at each node documents at each node keys at each document TTL

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No.of messages

600 500 400 300 200 100

50 40 30 20 10 0

0 5

10

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25

5

30

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No. of ants

Fig. 1. Relation between the number of ants and algorithm performance

Table 2. Comparison of four algorithms Metrics

Our algorithm NeuroGrid Gnutella Random Walk

Average path length 5.3 No. of messages 320

4.6 154

6.3 5400

9.2 2650

of ensuring search efficiency, decreasing the number of messages is an effective method of improving search performance. Fig.1 shows that with the number of ants increasing, search efficiency will properly increase, but the number of messages will increase simultaneously. Experiment 2 compares our algorithm with other classical algorithms, e.g. NeuroGrid, Gnutella and Random Walk. Two metrics are calculated that are average path length and the number of messages. As showed in Table 2, the performance of our algorithm is little less than NeuroGrid. However, each NeuroGrid node maintains a knowledge base that stores associations between keywords and other NeuroGrid nodes. The maintenance of NeuroGrid nodes is higher than the node in our algorithm.

6

Conclusion and Future Work

This paper presents an ant-like query message routing mechanism for resource searching in unstructured P2P networks. The method views the query message as an artificial ant, and target resources as the food ants wanting to search. The algorithm utilizes pheromone as heuristic information that direct an ant to select the next hop. By adjust the number of ants, a better tradeoff between search efficiency and cost could be achieved. Moreover, the maintenance of a pheromone table is small, which hardly increase the burden of the resident node. The future works are improving our algorithm and farther performance analysis, which include the update policy of pheromone, the relation between the number of ants and the size of a network, and the adaptation while node entering and leaving frequently.


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Acknowledgments. This work is supported by Natural Science Research Foundation of Anhui Higher Education, China under Grants No. KJ2007B308ZC.

References 1. Yang, B., Garcia-Molina, H.: Improving Search in Peer-to-Peer Networks. In: Proc. of the 22nd International Conf. on Distributed Computing Systems (ICDCS 2002), pp. 5–14. IEEE Press, New York (2002) 2. Lv, Q., Cao, P., Cohen, E., Li, K., Shenker, S.: Search and replication in unstructured peer-to peer-networks. In: Proc. of the Intl. Conf. on Measurements and Modeling of Computer Systems, pp. 84–95 (2002) 3. Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE transactions on evolutionary computation 1, 53–66 (1997) 4. Adamic, L.A., Lukose, R.M., Puniyani, A.R., Huberman, B.A.: Search in power law networks. Phys. Rev. E 64, 46135–46143 (2001) 5. Caro, G.D., Dorigo, M.: AntNet: Distributed stigmergetic control for communications networks. J. of Artificial Intelligence Research 9, 317–365 (1998) 6. Zheng, X.: Ant Colony Intelligence Based Solution for Grid Services Selection. In: Proc. of the 7th World Congress on Intelligent Control and Automation, pp. 2512– 2517. IEEE Press, New York (2008) 7. Joseph, S.: An Extendible Open Source P2P Simulator. P2P Journal, 1–15 (2003) 8. Joseph, S., Hoshiai, T.: Decentralized Meta-Data Strategies: Effective Peer-to-Peer Search. IEICE Trans. on Communications 6, 1740–1753 (2003) 9. Freenet website, http://freenet.sourceforge.net

Layer 3 Initialization Procedures Recommendation in Next Generation Heterogeneous Mobile Networks Sebastião Boanerges Ribeiro Junior and Paulo Roberto de Lira Gondim Departamento de Engenharia Elétrica, Universidade de Brasília, Brazil [email protected], [email protected]

Abstract. The wireless service evolution in conjunction with widespread of WLAN and multi-mode handsets combining heterogeneous radio technologies leads to the possibility of ubiquitous service access thru the offer of vertical handovers techniques. This combined with the multimedia real time service offering unveils the need of new layer 3 initialization procedures in order to keep the end-user quality and experience. This paper presents an evaluation of the network connection procedures against the requirements of the new scenario and proposes changes to the standards. Keywords: Heterogeneous wireless networks, vertical handover, multimedia real time service, Wi-Fi association and authentication, layer 3 initialization.

1 Introduction The technological evolution being seen in the last years lead us to the offer of distinct high speed packet data access networks suitable for real time multimedia services and to the availability of multi-mode handsets in the manner to allow the ubiquitous connection on those access networks. In conjunction with the broad acceptation of Session Initiation Protocol (SIP) [1] based service and web based services, this evolution is addressing the end user wish for seamless connection regardless the location and network being used. The end user need of seamless connection means that the systems, which can be triggered by the network or by the handset, automatically choose the best access network based on the better balance of predefined requirements like cost, speed and quality of service (QoS). Nowadays, the most comprehensive packet data access network regarding cost and data rate are those based on IEEE 802.11 (Wi-Fi) [2]. Seamless wireless multimedia communication data service that wants to get all the advantages of the surrounding access networks must be able to execute simple, fast and effective handover procedure between different accesses networks technologies, this kind of handovers are best known on the literature as vertical handover [3]. In the case of WWAN (e.g. GSM and UMTS networks) to WLAN (e.g. IEEE 802.11) handover the traditional mechanisms based on quality connection monitoring in the physical layer cannot be applied due to the WLAN coverage is include in the WWAN coverage in most of the cases [4]. As the handset is able to scan the available WLAN networks even when the WWAN signal quality is acceptable, the handover P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 68–75, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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decision to WLAN is handset based. Emerging standards like IEEE 802.21 Media Independent Handover are introducing network mechanisms to facilitate the vertical handover decision based on information services supplied from the network to the handsets [5]. Fast handover is crucial for multimedia real time services mainly when in the direction WWAN (e.g. WCDMA or GSM networks) to WLAN (e.g. IEEE 802.11g/WiFi), due to the better data rate and cost per bit offered in the existing Wi-Fi networks [4]. The better end user experience in this case could be gotten as a measurement of the overall handover delay to WLAN, as this means more data rate availability and less cost using the service. In this paper, the overall handover delay is considered as the time used by the system with all the process related to the switch between the radio bearer access networks until the complete availability of the new wireless network to the service being requested by the end user. However the process of handover can cause disruption of the service being requested due to the technology and limitations on the handset resources. The pace for the broad acceptation of seamless vertical handover services is tied with the market offer of low cost multi-mode handsets even if that means resource constrains. As example, a handset can switch off the previous radio interface before initiate attachment procedure to the new access network due to lack of resource to keep both radio interface active or as a strategy to save battery life. The different characteristics between the heterogeneous access technologies can also cause delays due to the change in the handset network interface configuration. An important process that account for the overall handover delay and for the disruption of service is not related with the radio link itself, but with the network layer (ISO/OSI layer 3) initialization procedures. These procedures are required by the handset network interface to setup the Internet Protocol (IP) parameters for the service connection to the new access network. The procedures include the network parameters retrieving from specific servers or from auto configuration methods and the succeeding address duplication detection method. For each version of IP (aka IPv4 and IPv6) commercially available there are different layer 3 initialization procedures and protocols defined for these procedures. Measurements performed by the authors’ unveiled delay up to 1.5 seconds during the layer 3 initialization procedures after Wi-Fi association in a handset with Windows Mobile Operational System (OS). This delay value does not guarantee the Quality of Service (QoS) during multimedia real time data services offering, including video streaming and video communication service. Literature recommendation stands that interruption of the service should not be greater of 40 ms for voice service quality during handover procedures [6]. The layer 3 initialization procedures were evaluated and the standards addressing duplication detection methods found as the main offender in the delay measurements. The standards used for address duplication detection are described in this paper and suggestions for improvements aiming the target delay are proposed. In this paper we describe the main general procedures that will take place during the handset vertical handover to Wi-Fi networks before the service being requested to be ready to re-connect to the operator’s data network. These procedures are the authentication and association to the Wi-Fi network and the layer 3 initialization and connection procedures in IPv4 and IPv6 networks. The results of the author’s

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measurements of the layer 3 connection procedures are also described and we conclude with suggestions for standards improvement to the scenario under analysis.

2 IEEE 802.11i Authentication and Handover Delay The original IEEE 802.11 specification defines two subtypes of authentication services: Open System and Shared Key [2]. Open System authentication can be defined as a null authentication algorithm. Shared Key authentication is based on the exchange of a shared secret key and can be used with the WEP (Wired Equivalent Privacy) privacy mechanism. Due to security weakness these security mechanisms were superseded by the IEEE 802.11i [7], published as an amendment to the IEEE 802.11 standard. The 802.11i architecture define the use of IEEE 802.1X [8] standard for authentication and a new mechanism for confidentiality and data integrity based on RSN (Robust Security Network) together with the security protocols TKIP e CCMP [7]. Due to backward compatibility with the previous state machine the IEEE 802.11i defines that an open system authentication shall be requested before the association procedure to the selected Access Point (AP). With IEEE 802.11i, after a successful association the mobile station supplicant initiates the IEEE 802.1X authentication and the key management, in a different way, the original IEEE 802.11 standard allows the exchange of data frames just after the association state. The IEEE 802.1X requires the use of some EAP method for the authentication procedure. Heterogeneous WWAN and WLAN services based on dual mode handsets look up to use EAP-SIM [9] or EAP-AKA [10] methods in view of the fact that they are based on the pre-existing WWAN (GSM/WCDMA) credentials stored in the SIM/USIM Card. The use of WWAN pre-existing credentials allow seamless authentication of the handset in any supported WLANs, but may introduce some extra delay due to the authentication request being sent to the home WWAN. After the successful IEEE 802.1X authentication, a method of security association is established for the purpose of data confidentiality. The IEEE 802.11i defines also key management procedures and protocols to provide fresh keys for the security association called 4-Way Handshake and Group Key Handshake. The Fig. 1 illustrates the IEEE 802.11 operations described. After a successful key management procedure the handset can start the network connection, based on layer 3 procedures. As the handset IEEE 802.11 MAC management entity had issued the primitive confirming the association success and had changed the IEEE 802.11 state variable [2] (State 3: Authenticated, Associated) before the IEEE 802.1X procedures [7] the handset could also had started the disconnection from the previous access network. This means that any delay due to the network connection procedures represents an interruption on the end-user’s service. Depending of the IP version supported, different procedures and different delay figures are applied, as described in the sequence.


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Fig. 1. IEEE 802.11 and 802.11i initial operations

3 Connection Procedures on IPV4 Networks Most of the data networks deployed today is still based on the old IPv4 standards due to the lack of ubiquitous end user clients/handset supporting to new IPv6 standards. Another reason for the current scenario is that some of address space problems aimed to be solved by IPv6 networks have been temporary postponed as result of the work done in areas as NAT (Network Address Translation) and address allocation, as the IETF Classless Inter-Domain Routing (CIDR) standard [11]. IPv4 dynamic connection procedures rely on DHCP [12] protocol. DHCP standard defines that some method should be used by the client station to check that the IP address granted is not in use by any other station or host. The industry chosen method is based on the ARP (Address Resolution Protocol) protocol. The ARP protocol is an IETF standard [13] defined to find a layer 2 address (e.g. Ethernet MAC address) based on the layer 3 information (e.g. IP address). For address duplication detection purposes a special ARP packet is send to the network with the same sender and target

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address, this packet is called Gratuitous ARP, and it is intended to not be replied. Most of the handsets models and mobile computer OS deployed nowadays apply three sequential Gratuitous ARP packets as its address duplication algorithm. The issue with this approach is the timer value used. For each new Gratuitous ARP packet sent the timer expiration time is multiplied by a factor (usually 2). As the success criteria (i.e. no duplicate IP address informed) means no reply to the Gratuitous ARP request, the address duplication detection means unnecessary delays due to the sequence of timer expired requests. Commercial handset with Windows Mobile OS implementations can show delays as long as 1.5 seconds as result of the sequential Gratuitous ARP timer expiration, which means an unacceptable delay in the case of seamless handover of multimedia communication applications.

Fig. 2. IPv4 Layer 3 Initialization Procedure

The authors did performed measurements on different handsets and operational systems of the delay to execute the network connection procedures. In the worst case, which was verified with Windows Mobile 5 OS, the handset sent out three Gratuitous ARP request, the first immediately after the DHCP procedures (DHCP ACK), the second after 0.5 seconds and the third one was delivered one second after the second packet, as show in Fig. 2. This means a delay of 1.5 seconds between the first and the


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third Gratuitous ARP packet just to check whether IP address defined to the interface was duplicated. The best case was seamed in a GF210 handset from UTStarcom with proprietary OS and means transmit of two gratuitous ARP packets with 0.5 seconds delay between them. Even in the best case verified the delay due the ARP expiration timer is not appropriated for the offer of multimedia real time services during the vertical handover procedure. There is no definition in the ARP standard [13] about the complete procedure for the use of gratuitous ARP to address detection. The authors believe that there is no room for modifications on this old protocol to define a new procedure for multimedia real time vertical handover services, and the best approach should be based on the introduction of the new IPv6 protocols suite.

4 Connection Procedures on IPV6 Networks The IPv6 standard definition came up with a complete set of new protocols to deal with the IP address configuration issues. The IPv6 suite defines two different standard methods for network layer configuration: the autoconfiguration [14] defines a stateless IP address configuration procedure based on the subnet prefix and a local identifier [15] and the DHCPv6 [16] defines a stateful protocol and procedure for address and network configuration of host interfaces. All IPv6 configuration methods shall use a duplicate address detection (DAD) algorithm [15], defined in [14], which is based on the use of the new ND (Neighbor Discovery) protocol [17]. In a similar way that ARP protocol implementation, the DAD algorithm defines that the station shall sends packets containing Neighbor Solicitation messages with source address as undefined address and target address as the tentative address (IP address candidate to the interface). The lack of response after a timer expiration delay means that the tentative address can be assigned to the interface. Even on this new protocol the retransmission timer proposed in the original standard (RETRANS_TIMER) is set to 1,000 milliseconds [17], an extremely long time period for multimedia real time communications. In the same standard document is recognized that the proposed timer values can be overridden by specific changes regarding the link layer being used. Considering the proposed scenario of multimedia real time service during vertical handover procedure the authors recommend that the ND protocol timers [17] should be reviewed and updated. This review should include the retransmission timer (RETRANS_TIMER) and the maximum retransmission solicitation delay (MAX_RTR_SOLICITATION_DELAY) values, based on the expected maximum disruption time the specified service should support to keep the end user experience and quality of the service. The MAX_RTR_SOLICITATION_DELAY is defined as the upper limit value for the random time delay selected by the node before send the first message after an interface initialization. The purpose is alleviating possible congestion problem when many interfaces are initialized at the same time, such as after a general failure. This principle could be applicable in the vertical handover scenario proposed due to the possibility of a group of handsets arrive at the same time under the coverage of the same AP or WLAN hotspot. But, the value defined in the original standard (1,000 ms)

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must be updated to attend the proposed scenario and because the AP coverage limits the amount of concurrent handsets associations. For voice services the European Telecommunications Standards Institute (ETSI) recommends a maximum interruption of the service during handover of 40 ms [6]. This figure was recommended for horizontal WWAN handover scenario with switched voice service, but should be considered as indicative of the tight boundaries necessary for multimedia real time communications services. The simple redefinition of the timer expiration time to meet the expected maximum disruption time could not be feasible for all the deployed Wi-Fi hotspots, due to the packet travel time in hotspot subnet. The adoption of smaller value for retransmission timer should lead to a re-engineering of the hotspots network to guarantee that the ND solicitations messages will be processed by all the nodes before the expiration timer.

5 Conclusion The broad availability of different high speed wireless data access technologies covering the same region, together with the evolution of multi-mode handsets and the end user demands of multimedia real time service has lead to the development of novel seamless vertical handover methods. This paper scenario is based on vertical handover procedure from WWAN to Wi-Fi access network. The attractiveness of this scenario is due to the higher data rate and lower cost per bit provided by WLANs networks in comparison with commercial WWANs (e.g. GSM and UMTS networks). These new vertical handover scenarios should push the review of handset authentication and layer 3 connection procedures, in the manner to address the new requirements of connection delays and quality of service while the handover is being executed. The main issue is regarding the disruption of service during a multimedia real time communication service, as per example a video conference. As the network layer 3 connection procedures are executed after the Wi-Fi association and authentication, i.e. the link layer handover, any delay on it affect directly the service quality and the end-user experience. We have shown that the duplicate address detection mechanism is the main cause of the network connection delay, as result of its success criteria based on timer expiration request. The IPv4 networks offer no method to avoid disruption of the service during the network connection on the proposed scenario. The methods being utilized account up to 1.5seconds delay and the authors’ recommendation is to upgrade the existing access networks and handset to support IPv6 for the evaluated scenario. With the development of IPv6 protocol suite, novel methods for IP configuration and for duplicate address detection were also developed. Even with these new methods the published standard defines a long timer limit value which is not feasible for multimedia real time service handover delay. The authors’ recommendation for IPv6 Wi-Fi based networks subjected to vertical handover is the definition of new tighter timer values for the ND protocol specific variables. The use of tighter timer limits should also be accompanied with engineering guidelines for the local area networks serving the Wi-Fi hotspots, as extensive hotspots networks can not be able to respond timely the duplicate address detection requests.


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The adoption of the recommendations of this paper can assist and stimulate the development of low tier multi-mode handsets. The capacity to use the same network interfaces regardless the wireless access network, and continuing being able to offer seamless handover service can simplify the handset design and save production cost.

References 1. Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston, A., Peterson, J., Sparks, R., Handley, M., Schooler, E.: SIP: Session Initiation Protocol, RFC 3261, IETF (June 2002) 2. IEEE Computer Society, ANSI/IEEE Std 802.11, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, 1999 edn. (June 2003) 3. Nasser, N., Hasswa, A., Hassanein, H.: Handoffs in fourth generation heterogeneous networks. IEEE Communications Magazine, 96–103 (October 2006) 4. Nie, J., Wen, J.C., Dong, Q., Zhou, Z.: A seamless handoff in IEEE 802.16a and IEEE 802.11n hybrid networks. In: 2005 International Conference on Communications, Circuits and Systems, pp. 383–387 (May 2005) 5. de la Oliva, A., Bernados, C.J., Melia, T., Soto, I., Vidal, A., Banchs, A.: A case study: IEEE 802.21 enabled mobile terminals for optimized WLAN/3G handovers. Mobile Computing and Communications Review 11(2), 29–40 (2007) 6. European Telecommunications Standards Institute, TR 122 925 V3.1.1 (2000-2002) Universal Mobile Telecommunications System (UMTS); Service aspects; Quality of Service and Network Performance (3G TR 22.925 version 3.1.1 Release 1999) (April 1999) 7. IEEE Computer Society, 802.11i, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications – Amendment 6: Medium Access Control (MAC) Security Enhancements (July 2004) 8. IEEE Computer Society, IEEE Std 802.1X-2004, IEEE Standard for Local and Metropolitan Area Networks – Port Based Network Access Control (December 2004) 9. Haverinen, H., Salowey, J.: Extensible Authentication Protocol method for Global System for Mobile Communications (GSM) Subscriber Identity Modules (EAP-SIM), RFC 4186, IETF (January 2006) 10. Arkko, J., Haverinen, H.: Extensible Authentication Protocol method for 3rd generation Authentication and Key Agreement (EAP-AKA), RFC 4187, IETF (January 2006) 11. Rekhter, Y., Li, T.: An architecture for IP address allocation with CIDR, RFC 1518, IETF (September 1993) 12. Droms, R.: Dynamic Host Configuration Protocol, RFC 2131, IETF (March 1997) 13. Plummer, D.C.: An Ethernet Address Resolution Protocol, RFC 826, IETF (1982) 14. Thomson, S., Narten, T.: IPv6 Stateless Address Autoconfiguration, RFC 2462, IETF (1998) 15. Donzé, F.: IPv6 autoconfiguration. The Internet Protocol Journal 7(2), 12–16 (2004) 16. Droms, R., Bound, J., Volz, B., Lemon, T., Perkins, C., Carney, M.: Dynamic Host Configuration Protocol for IPv6 (DHCPv6), RFC 3315, IETF (July 2003) 17. Narten, T., Nordmark, E., Simpson, W.: Neighbor Discovery for IP Version 6 (IPv6), RFC 2461, IETF (December 1998)

Broadcast Data Scheduling in Wireless Environment Xianjun Sun, Chuang Lin, Weidong Liu, and Yanping Xiao Dept. of Computer Science and Technology Tsinghua University, 100084 Beijing, China [email protected], [email protected], [email protected], [email protected]

Abstract. Broadcast data scheduling is widely used in traffic information system, stock price, weather information and news distribution system. This paper proposes a novel approach to disseminate real-time information of these environments, which delivers data through multiple push channels and one pull channel. The simulation results show that our approach achieves better performance than the previous work in practical applications. Keywords: Broadcast scheduling, multi-channel, rate adjustment.

1 Introduction Broadcast data delivery is a good method to disseminate information to large clients in satellite or wireless asymmetric environment, where bandwidth of downlink channel is much higher than that of uplink channel. The main aim of broadcast scheduling is to provide desirable data items for clients with less delay and lower energy consumption. Average expected delay (AED) is an effective metric to evaluate the performance of broadcast scheduling. There are a lot of broadcast scheduling algorithms to minimize this metric, including off-line and on-line. Off-line algorithms assume both the entire set of data items and the demand probability for data items are known in advance. On the contrary, on-line algorithms decide which item to transmit first without a-priori knowledge of data items. In general, both on-line and off-line algorithms adopt approximate approach to get near-optimal value, and the conclusions are related to specific assumptions and parameter setting. Compared to off-line algorithm, the on-line version is suitable for real applications, although it has an inferior to optimization value. Performance of multi-channel broadcast scheduling is measured by multi-channel average expected delay (MCAED), which is related with parameters such as access probabilities, database size and channel bandwidth. Applications, such as meteorological data dissemination, news dissemination, etc., are expected to delivering bulk incremental data items to clients. In this situation, database size is variable, which is considered less by previous work. This motivates us to develop a new on-line scheduling algorithm of multiple push channels and one pull channel (MP+P). Our algorithm focus on multi-channel on-line algorithm, which considers more parameters, such as variable database size, date item length and variable channel bandwidth. The rest of the paper is organized as follows. Section II presents related work on multi-channel broadcast scheduling in recent years. Section III describes the system P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 76–83, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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model and performance metric. Section IV introduces the MP+P scheduling algorithm. Section V presents simulation results, and the last section concludes this paper.

2 Related Work Pervious research on multi-channel scheduling can be divided into two catalogues: 1) designing algorithms of data allocation among multi-channels[1], [2], [3], [4]. 2) investigating into hybrid mechanism of broadcast scheduling in multi-channel environment [5], [6]. The FLAT algorithm adopts a simple but not so efficient allocation algorithm, equally allocating the items to each channel [1]. Peng and Chen proposed VFk algorithm, which skews the amount of data allocated to each channel [2]. Yee and Navathe proposes the DP algorithm, which using dynamic programming to partition the data items on multi-channels and achieved an optimal solution [3]. Although these algorithms share the same objective, they assign different weights to complexity and performance. Navrati Saxena and Cristina Pinotti propose a balanced approach to partition the data items into multi-channels averagely, which adopts a hybrid push-pull scheduling for each single channel (denoted by BH). In each channel, the push and pull sets are served in an interleaved [5]. BaiHua Zhang, Xia Wu, Xing Jin and Dik Lun Lee propose a two-level optimization scheduling algorithm (denoted by TOSA), where highlevel optimization strategy clusters data items into channels according to available bandwidth, and low-level optimization schedules the items on each channel in order to guarantee the average performance [6]. However, both average and proportional data allocation among channels can not guarantee maximum utilization of bandwidth. So we address the issues by a heuristics approach. In our model, we use a rate function to adjust rate among channels during the broadcast. Issues of rate control among multiple channels in wireless network have been focused by several researches recently [7], [8]. In this paper, we combine scheduling method with rate adjustment scheme, which proves to achieve a performance tradeoff between efficiency and simplicity.

3 Preliminary 3.1 System Model Suppose the database contains N data items, denoted by D={d1,d2, ...,dN}. Incremental data items is ΔD = {(n1 , l1 ), (n2 , l 2 ),...(n m , l m ) , where (ni, li) denotes data item set i consists of ni data items with equal length li. Let ΔN denote the number of data items in ΔD . Each item di can be characterized by a demand probability pi. First, we give a model for multi-channel push and one pull scheduling (MP+P) by definition. Definition 1. A broadcast program S for MP+P scheduling is a partition of incremental data into multi-channels, and a placement of data item selected from data set

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by clients’ outstanding requests in another channel. This program determine rate of each channel by a dynamic bandwidth allocation scheme. For

brevity

and

clarity’

S1k → F ( RΔ (ΔD, k ), Rπ ( D), C (k + 1, B)

sake, we formulate the model above by . Where RΔ (ΔD, k ) denotes a partition of data set

ΔD among k channels, Rπ (D) denotes a cycle scheduling of data item selected from D

according to requests’ probability, and C(k+1,B) denotes assigning bandwidth to each channel. Fig. 1 shows a simple data allocation and bandwidth assignment scheme of MP+P scheduling, where k=3, R1=3, R2=3, R3=4, ∑R=10. The broadcast program transmits 3, 3 and 4 unit length data item of channel C1, C2 and C3 respectively by a round-bin mode.

8

6 5

7

9 C3

Incremental 8

C2

3

4

2

1 C1

6

7

9

Cyclical

5

3

R3=4

4

1

2

1

R2=3

R1=3

time

Fig. 1. MP+P Scheduling

3.2 Performance Metric Average Expected Time for pure-push-based schedule can be given by ΔN

AED =

∑ w p Where w denotes expected item delay for items i on S, assuming that, i

i i

i =1

at any instant of time, clients start to listen with the same probability. Performance metric to evaluate pure-pull-based schedule is often using Average N

Access Time, ATT =

∑ i =1

∑

r j ∈R j

w(r j ) p j

.Where w(rj) is the actual delay between the

| Rj |

request r for item j and the transmission time of item i. R and |Rj| denote the set of request for item i and its size respectively. Suppose the number of data items is ΔN, that of data items allocated to channel i is ΔNi, and ∑ΔNi=ΔN. Then average wait time of multiple push-based channels is measured as follows.

MCAED =

1 k 1 ∑ k 1 ΔN i

ΔN

∑ wi pi + ∑ j =1 i =1

N

∑

r j ∈R j

w(r j ) p j

| Rj |

.

(1)


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4 Designing Algorithms of MP+P Scheduling In this section, we discuss algorithms of MP+P scheduling firstly. Then, we present rate adjustment scheme for MP+P scheduling. Finally, we give a theoretical analysis of performance metric’ lower bound. 4.1 MP+P Scheduling MP+P scheduling allocates M data flows to k channels according to the data items’ arrival character, keeping data item from the same flow in a channel. After a simple data item partition, our approach begins to order sequence data items in each channel towards minimizing value of MCAED. MP+P scheduling can be completed by three steps. 1) Data Allocation. Picking up a data flow with maximum Ai and assigning it to a channel with minimum Aj, where Ai is a sum value of product of each data items’ pi and li in chanel i . 2) Permutation. Sorting items in non increasing order of Ai. As algorithm 2 shows, this step moves the scheduling towards the near optimization. 3) Rate adjustment. Assigning total link bandwidth to each channel by formula 3 and determining rate of the broadcast program for each channel. 4.2 Rate Adjustment To the best of our knowledge, there are two kinds of broadcast in the literature, flat broadcast and non-flat broadcast, which adopt average assign and ratio assign of bandwidth respectively. For flat broadcast, the expected wait time for an item on the broadcast is the same for all items regardless of their relative importance to the clients. Alternatively, the server can broadcast different items with different frequency, so that the broadcast can emphasize the most popular items and de-emphasize the less popular ones. Theoretically, non-flat broadcast programs can be considered as a bandwidth allocation problem. We assign bandwidth among channels by the way similar to non-flat broadcast programs, which make bandwidth B proportional to Ai in each channel.

Ai = ∑ d ∈C (li / pi ) .We consider data items in each channel as a queue. When one i

i

or more channel queues transmission finished, the others can acquire available bandwidth through recalculating rate. This step of rate adjustment will be implemented during the broadcast. Note that, although recalculation of rate can be made at any time, we suppose that each data item is transmitted by a constant rate, which implies any channel can only recalculate rate until it finishes current items transmission at a fixed rate. Then, the average wait time for channel i to acquire bandwidth after channel j finishing its task is li/2Ri(t) (i≠j). At the beginning of the broadcast program, we calculate a initial value Ri(0) of each channel’s rate. Let Ra(t) denote available bandwidth after a certain queue finishing transferring. Ri(0) and Ra(t) meet∑Ri(0)=R and Ra(0)=0.

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Algorithm 1. Initialization and data allocation Input:( Ω, k, p) Output: partition M data flows to K channels Incremental data flow Ω={D1,D2,…Dm} Channel queues Γ={Q1,Q2,...Qk} 1: while (Ω≠Ø) do 2: find Max(Ai) in Ω 3: find Min(Aj) in Γ 4: Qj +ÅDi 5: Remove Di from Ω 6: End while In order to help the broadcast program know when and which channel is free in time, we let server maintain a boolean value qi(t) for each channel. if queue i is null, qi(t)=0,else qi(t)=0.

Ra (t ) = ∑ Ri (t ) × qi (t ) .

(2)

According to rule of random allocation, the rate of each channel can be obtained by

Ri (t ) = Ri (t ) + Ra (t ) .

(3)

Algorithm 2. Permutation and rate adjustment Input :(k, B, p) Output: broadcast program S 1: For iÅ1 to K do 2: For jÅ1 to |Di| 3: Ai↓ Ascending sort 4: End for 5: Ri(t)ÅRi(t)+Ra(t) Compute channel rates 6: Ra(t) Å0 7: End for 4.3 Complexity and Lower Bound In this section, we discuss the time complexity and lower bound of MP+P scheduling. Operation in algorithm 1, such as finding a maximum and a minimum on the maximum heap m and k can be done in O(n) time. The time complexity for algorithm 2 to sort, compute boolean value qi(t) and rate of queues in each channel is O(n+nlogn). So the time complexity of MP+P scheduling algorithm is O(n+nlogn). For the rest of this subsection, we will present a lower bound of MCAED for MP+P scheduling. We suppose that the demand probability pi=1, which means each incremental data items should be transferred. Letting θ (N ) denote items selected by outstanding request, we can derive.


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Theorem 1. For all sets of data items’ access probabilities,

MCAEDlower _ bound =

θ (N ) 1 ΔN + k k ΔN i ( l + lj) . ∑∑ i ∑ R 2k i =1 j =1 j =1

(4)

Proof: For multiple push channels, there is wi = li/Ri(t). We replace wi with li/Ri(t) , then formula 3 can be modified as below

MCAED =

=

θ (N ) l 1 k 1 ni 1 j + . ∑ ∑ ∑ k i =1 ni j =1 Ri (t ) j =1 R1 (t )

1 k

k

∑ i =1

ni + 1 + 2 Ri (t )

θ (N )

∑ R (t) j =1

lj

(5)

(6)

1

As analysis of above, the average wait time for channel i to acquire bandwidth of channel j is li/2Ri (t).Thus, when the broadcast program transfer data items of these two queues in turns by a total rate Ri(t)+Rj(t), the following holds lj li + l j li + ≥ Ri (t ) R j (t ) Ri (t ) + R j (t )

.

(7)

Applying (7) to (5), we can obtain the formula 4. Theorem 1 shows that MCAED can be expressed by a function of data items, rate and access probabilities and reach a lower bound value when total bandwidth is used during the broadcast.

5 Simulation Results We make a simulation in wireless environment by a multicast program designed for a meteorology data disseminate system. Table 1 shows parameters in simulations. We assume that the access probabilities of data items follow the Zipf distribution. Length of data items follows average distribution between [1, MaxItemLength]. We compare our approach with BH scheduling under various θ values, various numbers of channels, various Item Length and sizes. From Fig. 2(a), we can see that with the increasing of θ, the improvement of MP+P exceeds BH by 1.8% and 6.2% under unit item length and non-uniform item length with MaxItemLength is 50. Fig. 2(b) shows the performance under different item length distributions with the number of channels K changing from 3 to 10, and 10000 items in the database. Compared with BH, MP+P improves the performance by 11.9% and 34.9% on average respectively. Fig. 2(c) depicts the performance affected by incremental item numbers, which also consider two cases: unit item length and non-uniform item length. Compared with BH, our algorithm improves performance by 17.6% and 22.3%. Fig. 2(d) shows that MP+P improve performance by 23.9% on average with increase of MaxItemLength. Simulations show that our approach achieves better performance under more general environment.

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X. Sun et al. Table 1. Parameter and Value

Parameter Database size Channel numbers Bandwidth Access arrive rate data item length

Denotation

Value

N K B λ

Skew coefficient

θ

10000 3-10 512 10 1,25,50,75,100,125,150,175 0.25,0.5,0.75,1,1.25,1.5

l

(a)

(b)

(c)

(d)

Fig. 2. (a) Performance vs. θ. (b) Performance vs. Channels. (c) Performance vs. Item Number. (d) Performance vs. MaxItemlength.

6 Conclusion Broadcast data delivery is rapidly becoming the method of choice for disseminating information to a massive user population in many new application areas where


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client-to-server communication is limited. The main advantage of broadcast delivery is its scalability: it is independent of the number of users the system is serving. In this paper, we have investigated into strategies broadcast data scheduling. Contributions of this paper are as follows: 1) data broadcasting is considered as a efficient way, in terms of delay and bandwidth, for the distribution of information to a large number of users in wireless communication environment. 2) We present a broadcast scheduling model of multiple push channels and one channel (denoted by MP+P). 3) We design an online scheduling algorithm based on our model, and analyzes the lower bound value of its performance metric MCAED. In future, we plan to extend our work to save power consumption of wireless clients by predicting accurate receival time of data items on broadcast scheduling.

References 1. Huang, J.-L., Chen, M.-S.: Dependent data broadcasting for unordered queries in a multiple channel mobile environment. IEEE Transactions on Knowledge and Data Engineering (TKDE) 16(9), 1143–1156 (2004) 2. Lee, G., Yeh, M.S., Lo, S.C., Chen, A.: A strategy for efficient access of multiple data items in mobile environments. In: Proceedings of 3rd International onference on Mobile Data Management (MDM 2002), Singapore, pp. 71–78 (January 2002) 3. Yee, W.G., Navathe, S.B.: Efficient data access to multi-channel broadcast programs. In: Proceedings of International Conference on Information and Knowledge Management (CIKM 2003), New Orleans, Louisiana, USA, pp. 153–160 (November 2003) 4. Liu, C.-M., Lin, K.-F.: Broadcasting Dependent Data with Minimized Access Latency in a Multi-channel Environment. In: IWCMC 2006, Vancouver, British Columbia, Canada, July 3–6 (2006) 5. Saxena, N., Pinotti, M.C.: On-line Balanced K-Channel Data Allocation with Hybrid Schedule per Channel. In: IEEE Intl. Conf. in Mobile Data Management (MDM) (2005) 6. Zheng, B., Wu, X., Jin, X., Lee, D.L.: TOSA: a near-optimal scheduling algorithm for multi-channel data broadcast. In: Proceedings of the 6th International Conference on Mobile Data Management, Ayia Napa, Cyprus, May 09-13 (2005) 7. Song, Y., Zhang, C., Fang, Y.: Throughput Maximization in Multi-channel Wireless Mesh Access Networks. In: ICNP 2007, Beijing,China, October 16-19 (2007) 8. Choi, J., Na, J., Park, K., Kim, C.-k.: Adaptive Optimization of Rate Adaption Algorithms in Multi-Rate WLANS. In: ICN 2007, Beijing,China, October16-19 (2007)

Analysis on Imai-Shin’s LR-AKE Protocol for Wireless Network Security Yingjie Wang, Wei Luo, and Changxiang Shen Beijing Remote Sensing Institution, Beijing 100192, China [email protected]

Abstract. In 2005 Imai and Shin proposed a leakage-resilient authenticated key exchange protocol(LR-AKE) for wireless network security. For simplicity, the protocol is based on password authentication plus additional secrets to fit wireless environment (e.g., computation constraint). In this paper we show that Imai-Shin’s scheme is vulnerable to both client and server impersonation attacks and needs to be improved to provide strong security for wireless network. Keywords: Authentication, Key exchange, Password, Security analysis.

1 Introduction As wireless technology has become more pervasive along with the popularity of mobile devices, traditional electronic commerce and transactions have come true over wireless networks, which have great potential for improving access to services. Nevertheless there is a lot of concern on wireless communication carrying sensitive information, and two fundamental security issues must be considered: authentication and confidentiality. Both can be achieved through an authenticated key exchange (AKE) protocol at the end of which the two parties share a common session key (e.g., the key is used for confidentiality and/or data integrity). The security problem demands for secure algorithms and protocols that could be able to persuade common users. The level of security is directly related with the trust on the session key used to protect the sensitive data. This way the key exchange algorithms and protocols are of great importance in the security of wireless environments in general. To achieve authenticated key exchange, two methods are usually used: PKI (Public Key Infrastructure) based authentication and password based authentication. When it comes to wireless networks, it must be concerned that there are yet natural constrains of the small size and low resource equipments, whose battery limitation strictly compromises its processing capabilities. So concerning the energy consumption constrains of wireless mobile devices, password based authentication solutions are better suited due to the user friendliness and small simplicity of its algorithms. In 2005 Imai and Shin proposed a leakage-resilient password authenticated key exchange protocol (LR-AKE)[1] for wireless network security, assuming that no TRM(Tamper-Resistant Modules) storage is available on both user and server sides. They claimed the scheme is efficient and secure. The main contribution of this paper is to expose that Imai-Shin’s key exchange scheme for wireless environments is not P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 84–89, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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secure enough to most real world applications. We show that impersonation attacks can be mounted successfully on Imai-Shin’s scheme: both client impersonation attack or/and server impersonation attack. The paper is organized as follows. In Section 2 Imai-Shin’s protocol is reviewed. In section 3 we construct client/server impersonation attacks which can be mounted successfully on Imai-Shin’s protocol. Conclusions are presented in Section 4.

2 Review of Imai-Shin’s Protocol [1] Imai-Shin’s protocol consists of the four phases: an initialization phase, a secrecy amplification phase, a verification phase and a session-key generation phase (see Fig. 1).

Fig. 1. Imai-Shin Leakage-resilient authenticated key exchange (LR-AKE) protocol, where the enclosed values in rectangle represent stored secrets of client and server, respectively

Preliminary. The protocol is defined over a finite cyclic group

G =< g > , where

G = q and G is a prime order subgroup over a finite field F p . Let g and h are two generators of G so that the Discrete Logarithm Problem is hard to solve [2]. Both g and h may be given as system parameters.

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1) Initialization. A client

C registers a secret value generated by his password pw to

S i ( i ≥ 1 , a client may register to one or more servers). The client picks a distinct random polynomial pi (x ) of degree 1 with coefficient

the server

ai1 randomly chosen in ( Z / qZ ) * , pi ( x) = a i 0 + ai1 ⋅ x mod q , and sets

(1)

ai 0 = pw , where pw is the user’s password. After computing pi (1) with

the above polynomial, the client registers securely a value

h pi (1) to server S i as

follows:

S i ← h pi (1) .

(2)

pi ' ( x) instead of pi (x) in its mobile devices, such as mobile phones or PDA devices, and keeps password pw in Then the client just stores a secret polynomial

mind.

pi ' ( x) = pi ( x) − pw = a i1 ⋅ x mod q . Where

(3)

pi ' ( x) is obtained from pi (x) by removing the password.

C wants to share a session key with server S i , he first recovers the polynomial pi (x) by adding his password pw to the

2) Secrecy Amplification. When the client

polynomial

pi ' ( x) stored in device. With pi (x) the client computes h − pi (1) . Also

r1 ← R ( Z / qZ ) * to compute y1 ← g r1 ⋅ h − pi (1) , p (1) r then sends y1 to S i . Server S i also computes y 2 ← g 2 ⋅ h i with a random

he chooses a random number

r2 ← R ( Z / qZ ) * and the secret value h pi (1) registered by the client in the initializing phase. The server transmits y 2 to the client along with the authenticator Ver2 as showed in Fig. 1 below. On both ends, the client’s keying material becomes MK C ← ( y 2 ⋅ h − pi (1) ) r1 , and the server’s keying material becomes

number

MK Si ← ( y1 ⋅ h pi (1) ) r2 . Only if the client uses the right password

h

− pi (1)

to server

pw and the corresponding secret value

S i and S i uses the right secret value h pi (1) , both of them can share

the same keying material

MK C = ( y 2 ⋅ h − pi (1) ) r1 = g r1r2 = ( y1 .h pi (1) ) r2 = MK Si .

(4)


3) Verification. The client and the server computes and

87

Ver1 = MAC ( SID MK C 10)

Ver2 = MAC ( SID MK Si 01) , respectively, using a secure message authenti-

cation code with the keying materials as input, and exchanging

SID = C S i y1 y 2 . Upon

Ver2 and Ver1 , the client verifies Ver2 = ? MAC ( SID MK C 01) ,

(5)

and the server verifies

Ver1 = ? MAC ( SID MK Si 10) .

(6)

If both equations hold, they proceed to the session key generation phase. Otherwise, the session will be closed. 4) Session Key Generation. If both of the above verifications succeed, the two parties generate the session key using the verified keying materials as follows:

SK C ← MAC ( SID MK C 11) ,

(7)

SK S I ← MAC ( SID MK S I 11) .

(8)

3 Cryptanalysis of Imai-Shin’s Protocol Imai-Shin’s protocol requires no tamper-resistant module storage available, and it is claimed secret leakage resilient. In this section, we show that Imai-Shin’s scheme is not as secure as it is claimed, and it is vulnerable to both client and server impersonation attacks. Since Imai-Shin’s protocol does not require a secure server storage, an intruder can easily get access to secrets table on the server S i , that is, the adversary can get the p (1)

value h i . Then the adversary can choose to impersonate the client or/and the server in the run of the protocol. Below we first show how the intruder can impersonate the client C to communicate with the server. 1) Client Impersonation Attack. The adversary chooses a random number

r1 ' ← R ( Z / qZ ) * and with h pi (1) he can compute y1 ' ← g r1 ' ⋅ h − pi (1) , then he sends (C , y1 ' ) to the server S i . Upon receiving y1 ' , S i computes y 2 ' ← g r2 ' ⋅ h pi (1) with a random number r2 ' ← R ( Z / qZ ) * and the secret value h pi (1) stored. In the following steps, the adversary impersonates the client C to exchange messages ( S i , y 2 ' , Ver2 ' ) and Ver1 ' with the server S i like the client does in Fig. 1. Ver2 ' and Ver1 ' are computed as:

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Ver1 ' = MAC ( SID' MK C ' 10) ,

(9)

Ver2 ' = MAC ( SID' MK Si ' 01) ,

(10)

MK C ' ← ( y 2 '⋅h − pi (1) ) r1 ' = ( g r2 ' h pi (1) h − pi (1) ) r1 ' = g r1 'r2 '

(11)

where

is computed by the adversary;

MK Si ' ← ( y1 '⋅h pi (1) ) r2 ' = ( g r1 ' h − pi (1) h pi (1) ) r2 ' = g r1 'r2 '

(12)

is computed on the server side, and

SID' = C S i y1 ' y 2 ' . It can be seen that

(13)

MK C ' = MK Si ' , so the adversary and the server can pass mu-

tual authentication. Thus they can share the same session key at the end of the protocol.

h pi (1) by corrupting the p (1) p (1) r " secret table on the server S i , he can compute y 2 " ← g 2 ⋅ h i by h i and a 2) Server Impersonation Attack. When the adversary gets

r2 " ← R ( Z / qZ ) * for communicating with the client C . Since the p (1) adversary can share the same secret h i with the client C as the server S i does,

random number

similar to analysis in client impersonation attack, it can be easily concluded that the adversary and the client can authenticate each other successfully and share a session key in the end of the protocol. Thus server impersonation attack succeeds.

4 Conclusion In this paper, we have demonstrated that Imai-Shin’s scheme is vulnerable to both server and client impersonation attacks in wireless networks. Thus the scheme is not secure enough for most real world applications in wireless environment. For authenticated key exchange (AKE) in wireless systems, power and computation constraint must be concerned, due to simplicity of the algorithm and user friendliness, password based authentication schemes are usually better suited than those of PKI based. But so far few schemes based on password authentication can achieve strong security. Our future research would be focused on how to achieve strong security as well as simplicity of the algorithm/protocol for authenticated key exchange in wireless environments, one option is to how to make improvements on Imai-Shin’s scheme to get enough security for wireless access.


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References 1. Imai, H., Shin, S.H., Kobara, K.: Authenticated key exchange for wireless security. In: IEEE Wireless Communications and Networking Conference, vol. 2, pp. 1180–1186. IEEE Press, New Orleans (2005) 2. Schneier, B.: Applied cryptography: Protocols, algorithms and source code in C, 2nd edn. John Wiley & Sons Inc., New York (1996)

Surveys on the Intrusion Tolerance System Kuo Zhao*, Nurbol, Fei Ren, Jianfeng Chu, and Liang Hu** Department of Computer Science and Technology, Jilin University Changchun 130012, China Tel.: 86-431-85168277; Fax: 86-431-85166494 {zhaokuo,hul}@jlu.edu.cn

Abstract. In classical dependability, fault tolerance has been the workhorse of many solutions. Classical security related work has on the other hand privileged, with few exceptions, intrusion prevention. Intrusion tolerance (IT) is a new approach that has slowly emerged during the past decade, and gained impressive momentum recently. Instead of trying to prevent every single intrusion, these are allowed, but tolerated: the system triggers mechanisms that prevent the intrusion from generating a system security failure. In this paper, we describe the preliminaries of intrusion tolerance system (ITS) such as the concept and objective of ITS, discuss the main mechanisms and strategies of ITS, and present some example systems. Keywords: Intrusion tolerance system, mechanism, strategies.

1 Introduction According to the report of CSI/FBI, 95% organizations have the firewalls, 61% have the Intrusion Detection Systems, and 90% adaptive access controls, but attacks still could seep into the system. This obviously indicated that another kind of mechanism is needed to implement the precaution against intrusions due to the limitation of current intrusion detection mechanism and intrusion prevention mechanism. The research on IT has been an active field, and the development of commercial products continuously varies besides academic discussions. The IT is a brand-new network security technology which integrates the cryptography and the fault-tolerant technology with the characteristic of keep providing the corresponding network service, and could guarantee the confidentiality and integrity of system’s essential data even if some parts of the system have been already destroyed or controlled by the attacker successfully. The IT is called the third generation safety work - the core of survival technology in some materials. The paper is organized as follows. In Section 2 we describe the preliminaries of intrusion tolerance system. Section 3 discusses the main mechanisms of ITS. Section 4 * This work is supported by National Nature Science Foundation of China under Grant NO. 60873235, Program for New Century Excellent Talents in University of China under Grant No.NCET-06-0300, and Graduate Innovation Program for “985 Project” in Jilin University of China under Grant NO.20080244. ** Corresponding author. P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 90–97, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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provides the main strategies of ITS. Section 5 presents some example systems. Finally, Section 6 concludes the paper.

2 Preliminaries The term “intrusion tolerance” appeared originally in a paper by Fraga and Powell [1]. Later their scheme –Fragmentation-Redundancy-Scattering– was used in the DELTA-4 project to develop an intrusion-tolerant distributed server composed by a set of insecure sites. In afterward several years, many independent research works has carried on under the concept of IT, which were mainly in the protocol aspect, but not until 2000 did this field making an eruption-like progress. In Europe and America, the research has been made on the concept, the mechanism and the architecture of IT in two main projects: OASIS and MAFTLA. A main reason was the appearance of malicious wrong causes the distributional system to have the fundamentality problem. On the other hand, the traditional fault-tolerant is following an architecture, which cannot be completely suitable for the aspect of malicious mistake with intention. 2.1 Intrusion Tolerance System Concept Intrusion Tolerance System (ITS) [2] refers to the system that could provide the service while in the situation of suffering a certain invasion. The ITS not only can resist traditional attack but can also resist attacks that can not detected by prophylaxis methods such as firewall system, authentication and encryption system. The system will take some essential measures to guarantee that the key application or the key service's function works correctly and continuously, these measures including limit the suspected code and data, reconfigure the hardware and software resource and so on. The ITS can be defined as the system that can provide serves continually even if he server is in the condition of being attack with the ability to filter, restore and adapt intrusions. 2.2 Objectives of ITS The main objective of ITS can be divide into four levels: − Firstly: Protective system's safe operation. The ITS should prevent and stop the impediment of further aggressive behavior, warning and tries hard to repair system’s corresponding flaws automatically when the attacks were not yet initiated or the attacks were going on but have not create the destruction. − Then: Guarantee service usability. After attacker successfully attacked the application system, ITS needs to enable the application systems to provide the complete services or the degradation services and tried hard to make the system return to (health) normal condition by reorganization and restoration. − Again: Guarantees on the secret of server data. ITS needs to guarantee that the intruder cannot obtain the plaintext data when trying to grasped the server data access authority through a illegal way. − Last: Guarantees on the authenticity and integrity of server data.

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When the network services were under intrusions, the ITS needs to protect server's application data and the system data from illegal distortion, or can discover the distorted part and recovery the data when necessary. The four levels above are the main objective of ITS. These four levels are the cascade progressive; each latter level provides in-depth protection to the system compared to the preceding level.

3 Intrusion Tolerance Mechanisms The implementation of ITS is aimed for certain safety mechanisms [3] such as secure communication mechanism, intrusion detection mechanisms, intrusion containment mechanism, and error handling mechanism. 3.1 Secure Communication Mechanism In the network environment, secure communication mechanism is necessary for ensuring communication between safe and reliable communication, Preventing and stopping attack like Eavesdropping, camouflage, DOS and so on. The secure communications mechanism of IT usually applied technologies such as encryption, authentication, filtering, and classic fault-tolerant correspondence. During the interaction of IT, there is no design against threshold secret sharing system technology for considering that each correspondence side in a domain environment, threshold secret sharing system technology will increases system's burden and complexity of achieving the system, unable to guarantee system's Realtime. Under the demand of Guaranteeing security, you can use Trusted Timely Computing Base (TTCB) [4] component to implement all parts of the system between the group communications, it’s a feasible plan, and TTCB is a real time safe Wormhole TTCB is a simple component which contains a collection of limited service. Its objective is to support execution IT protocol and the mixed method of system frame. 3.2 Intrusion Detection Mechanism The intrusion detection system monitor and analyze the event which occurs through the computer system or the network, detect and respond to possible attacks, intrusion, security crack exist in the system. Intrusion detection system bind crack analysis and attacks forecast technology for predicting the occurrence of error, finding the reason of attack or security crack. Intrusion detection system can also bind Audit mechanism, record System's behavior and security event, examine and analyze the reason and the security problem. Detection response and tests of system security can predict the error which will happen by analyzing vulnerability, predicting attack and so on, finding the reason of attack or security crack, the union audit record module realizes to the situation has the diary report, and the way of recording the system behavior and the way of security event, going a step further to exam and analyze the reason and the security problem, carries on the linkage through the internal response sub module, calling security configuration strategy in advance to implement the system initiative IT.


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3.3 Intrusion Containment Mechanism Resource redundant and the design multiplicity can be used to increases the difficulty and the cost of attack. We can also use safety septa, structure reconfigure and other method to limit invasion, impediment the further proliferation of invasion. Resources redundancy and design multiplicity such as increases system isomer of the server and increases the separation to realize the isolation of the damaged component, can be used to increase the difficulty and the cost of invasion, limit and impediment the proliferation of invasion. 3.4 Error Handling Mechanism Error handling mechanism prevents disastrous expiration from existence, and it is composed of Error detection mechanism and Error recovery mechanism. 1. Error detection mechanism Error detection mechanism has two parts, Complete examination and Audit Log: Tests through the system safety non-periodically to system's module's security testing, realizes the examination to the system integrity; Analyze the system module's diary schedule audit analysis by the audit record module, carries on through the system log audit analysis to the systematic error examination. Through error detection composed of two way, Confirms oneself whether create Error recovery strategy because of false triggering, avoids occurring false triggering itself, can limit the wrong further dissemination. However, if we trigger the false triggering, we can localization the reason of creating error, provides the report for wrong recovery, guarantee system can restore from the mistake before creating own false triggering this kind of situation. 2. Error recovery mechanism The purpose of error recovery mechanism is to recover from wrong state for intrusion, and maintenance or restores the essential data, the key service even the complete serves, error recovery mechanism contains forward recovery, backward recovery, and error masking. z z z

The system continues to a forward condition, which guarantee of providing the correct service. The system recovers to a true state and run anew. The system use redundancy to shield wrong for providing correct serves. Main safeguard mechanism contain: component redundancy, threshold cryptography, system voting operation, Byzantine negotiation and interactive consistency.

Used heterogeneous operating system through each module and increases between the module to deploy structure these two ways, through system voting operation, Byzantine negotiation and interactive consistency, under permitted condition, Even if the partial modules have the security or the wrong question, the system can support normal service. Error masking is the design mentality of prevent problem from spreading. But forward recovery and backward recovery the system recover to a true state and run anew.

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Different error detection methods vary in the affection on the validity of recovery. Error shielding mechanism should be considered primordially, forward recovery and backward recovery should be unified simultaneously in order to guarantees the integrity of error processing.

4 Intrusion Tolerance Strategies Not surprisingly, intrusion tolerance strategies derive from a confluence of classical fault tolerance and security strategies [5]. Strategies are conditioned by several factors, such as: type of operation, classes of failures (i.e., power of intruder); cost of failure (i.e., limits to the accepted risk); performance; cost; available technology. Technically, besides a few fundamental tradeoffs that should always be made in any design, the grand strategic options for the design of an intrusion-tolerant system develop along a few main lines that we discuss in this section. We describe what we consider to be the main strategic lines that should be considered by the architect of IT systems, in a list that is not exhaustive. Once a strategy is defined, design should progress along the guidelines suggested by the several intrusion-tolerance frameworks just presented. 4.1 Fault Avoidance vs. Fault Tolerance The first issue we consider is oriented to the system construction, whereas the remaining are related with its operational purpose. It concerns the balance between faults avoided (prevented or removed) and faults tolerated. On the one hand, this is concerned with the ‘zero-vulnerabilities’ goal taken in many classical security designs. The Trusted Computing Base paradigm [6], when postulating the existence of a computing nucleus that is impervious to hackers, relies on that assumption. Over the years, it became evident that this was a strategy impossible to follow in generic system design: systems are too complex for the whole design and configuration to be mastered. On the other hand, this balance also concerns attack prevention. Reducing the level of threat improves on the system resilience, by reducing the risk of intrusion. However, for obvious reasons, this is also a very limited solution. As an example, the firewall paranoia of preventing attacks on intranets also leaves many necessary doors (for outside connectivity) closed in its way. Nevertheless, one should avoid falling in the opposite extreme of the spectrum— assume the worst about system components and attack severity— unless the criticality of the operation justifies a ‘minimal assumptions’ attitude. This is because arbitrary failure protocols are normally costly in terms of performance and complexity. The strategic option of using some trusted components— for example in critical parts of the system and its operation— may yield more performant protocols. If taken under a tolerance (rather than prevention) perspective, very high levels of dependability may be achieved. But the condition is that these components be made trustworthy (up to the trust placed on them, as we discussed earlier), that is, that their faulty behavior is indeed limited to a subset of the possible faults. This is achieved by employing techniques in their construction that lead to the prevention and/or removal of the precluded faults, be them vulnerabilities, attacks, intrusions, or other faults (e.g. omission, timing, etc.).


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The recursive (by level of abstraction) and modular (component-based) use of fault tolerance and fault prevention/removal when architecting a system is thus one of the fundamental strategic tradeoffs in solid but effective IT system design. This approach was taken in previous architectural works [7], but has an overwhelming importance in IT, given the nature of faults involved. 4.2 Confidential Operation When the strategic goal is confidentiality, the system should preferably be architected around error masking, resorting to schemes that despite allowing partial unauthorized reads of pieces of data, do not reveal any useful information. Or schemes that by requiring a quorum above a given threshold to allow access to information, withstand levels of intrusion to the access control mechanism that remain below that threshold. Schemes relying on error detection/recovery are also possible. However, given the specificity of confidentiality (once read, read forever...), they will normally imply some form of forward, rather than backward recovery, such as rendering the unduly read data irrelevant in the future. They also require low detection latency, to mitigate the risk of error propagation and eventual system failure 4.3 Perfect Non-stop Operation When no glitch is acceptable, the system must be architected around error masking, as in classical fault tolerance. Given a set of failure assumptions, enough space redundancy must be supplied to achieve the objective. On the other hand, adequate protocols implementing systematic error masking under the desired fault model must be used (e.g. Byzantine-resilient, TTP-based, etc.). However, note that nonstop availability against general denial-of-service attacks is still an ill-mastered goal in open systems. 4.4 Reconfigurable Operation Non-stop operation is expensive and as such many services resort to cheaper redundancy management schemes, based on error recovery instead of error masking. These alternative approaches can be characterized by the existence of a visible glitch. The underlying strategy, which we call reconfigurable operation, is normally addressed at availability- or integrity-oriented services, such as transactional databases, web servers, etc. The strategy is based on intrusion detection. The error symptom triggers a reconfiguration procedure that automatically replaces a failed component by a correct component, or an inadequate or incorrect configuration by an adequate or correct configuration, under the new circumstances (e.g. higher level of threat). For example, if a database replica is attacked and corrupted, it is replaced by a backup. During reconfiguration the service may be temporarily unavailable or suffer some performance degradation, whose duration depends on the recovery mechanisms. While the attack lasts), the service may resort to configurations that degrade QoS in trade for resilience, depending on the policy used (e.g., temporarily disabling a service that contains a vulnerability that cannot be removed, or switching to more resilient but slower protocols).

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4.5 Recoverable Operation Disruption avoidance is not always mandatory, and this may lead to cheaper and simpler systems. Furthermore, in most denial-of-service scenarios in open systems (Internet), it is generically not achievable. Consider that a component crashes under an attack. An intrusion-tolerant design can still be obtained, if a set of preconditions hold for the component: (a) it takes a lower-bounded time Tc to fall; (b) it takes a upper-bounded time Tr to recover; (c) the duration of blackouts is short enough for the application’s needs. Moreover, the crash, which is provoked maliciously, must not give rise to incorrect computations. This may be achieved through several techniques, amongst which we name secure check-pointing and logging. Recoverable exactly-once operation can be achieved with intrusion-tolerant atomic transactions [8]. In distributed settings, these mechanisms may require secure agreement protocols. This strategy concerns applications where at the cost of a noticeable temporary service outage, the least amount of redundancy is used. The strategy also serves longrunning applications, such as data mining or scientific computations, where availability is not as demanding as in interactive applications, but integrity is of primary concern.

5 Some Example Systems Since the term “intrusion tolerance” appeared originally, a number of isolated IT protocols and systems emerged. BFT [9] is an efficient state-machine replication algorithm. It has been used to implement an intrusion-tolerant NFS server. Rampart provides tools for building IT distributed services: reliable multicast, atomic multicast and membership protocols. SecureRing is a view-synchronous group communication system based on the Totem single-ring protocols [10]. Both Rampart and SecureRing can be used to build servers using the state-machine replication approach. Fleet [11] use Byzantine quorum systems [12] to build IT data stores, respectively for data abstractions like variables and locks, and for Java objects. The protocol suite CLIQUES supports group key agreement operations for dynamic groups of processes [13]. More recently, two projects have focused on intrusion tolerance, OASIS and MAFTIA, developing several results that will be detailed ahead.

6 Conclusions The intrusion tolerance technology is the third generation security technology which refers to the core survival technology. We have presented a survey of the main concepts, mechanisms and strategies relevant to intrusion tolerant system. In our opinion, Intrusion tolerance as a body of knowledge is, and will continue to be for a while, the main catalyst of the evolution of the area of dependability.

References 1. Fraga, J.S., Powell, D.: A fault- and intrusion-tolerant file system. In: 3rd International Conference on Computer Security, pp. 203–218 (1985) 2. Paulo, E.V., et al.: Intrusion-Tolerant Architectures: Concepts and Design. LNCS. Springer, Heidelberg (2003)


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3. Fengmin, G., Katerina, G.P., Feiyi, W., Rong, W., et al.: Characterizing Intrusion Tolerant Systems Using A State Transition Model. In: DARPA Information Survivability Conference and Exposition (DISCEX II), pp. 211–221. IEEE Press, Anaheim (2001) 4. Correia, M., Verissimo, P., Neves, N.F.: The design of a COTS real-time distributed security kernel. In: Bondavalli, A., Thévenod-Fosse, P. (eds.) EDCC 2002. LNCS, vol. 2485, pp. 234–254. Springer, Heidelberg (2002) 5. Veríssimo, P., Rodrigues, L.: Distributed Systems for System Architects. Kluwer Academic Publishers, Dordrecht (2001) 6. Veríssimo, P., Casimiro, A., Fetzer, C.: The Timely Computing Base: Timely actions in the presence of uncertain timeliness. In: International Conference on Dependable Systems and Networks, pp. 533–542. IEEE Computer Society, Los Alamitos (2000) 7. Powell, D., et al.: Delta-4: A Generic Architecture for Dependable Distributed Processing. Research Reports ESPRIT. Springer, Heidelberg (1991) 8. Cachin, C., Poritz, J.A.: Hydra: Secure replication on the internet. In: International Conference on Dependable Systems and Networks, pp. 167–176. IEEE Press, New York (2002) 9. Castro, M., Liskov, B.: Practical Byzantine fault tolerance. In: 3rd Symposium on Operating Systems Design and Implementation, pp. 173–186. ACM SIGOPS, Berkeley (1999) 10. Kihlstrom, K.P., Moser, L.E., Melliar-Smith, P.M.: The SecureRing group communication system. ACM Transactions on Information and System Security 4, 371–406 (2001) 11. Malkhi, D., Reiter, M.K., Tulone, D., Ziskind, E.: Persistent objects in the Fleet system. In: 2nd DARPA Information Survivability Conference and Exposition (DISCEX II), pp. 126– 136. IEEE Press, New York (2001) 12. Alvisi, L., Malkhi, D., Pierce, E., Reiter, M.K., Wright, R.N.: Dynamic Byzantine quorum systems. In: IEEE International Conference on Dependable Systems and Networks, pp. 283–292. IEEE Press, New York (2000) 13. Ateniese, G., Steiner, M., Tsudik, G.: New multi-party authentication services and key agreement protocols. IEEE J. of Selected Areas on Communications 18, 628–639 (2000)

Study on the DS/CDMA-CSMA Multi-user Communication Systems Yun-xiao Zu, Xiao Wu, and Yu-qin Lv School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China [email protected]

Abstract. The multi-user random communication system is a new researchful subject. Now most researches focus on fixed assignment multiple access method. A new approach that combine the fixed multiple access technology with the random multiple access technology is proposed in this paper, that is the DS/CDMA-time slot-nonpersistent CSMA access approach. The Markov-Chain mathematics model is set up. And then the transition of channel states is depicted and the formula of the network throughput is found. The factors influencing the performance of the DS/CDMA time-slot nonpersistent CSMA multi-user communication systems have been analyzed by Monte-Carlo simulation. It has been proved that the performance of the communication systems can be improved by using this approach. Keywords: DS/CDMA, time-slot nonpersistent CSMA, multi-user communication systems, Markov-Chain, Monte-Carlo.

1 Introduction The most common MAC layer protocols that used to manage the uplink multiple access is CSMA/CD (Carrier Sense Multiple Access with Collision Detection) in DS/CDMA-CSMA multi-user communication systems[1-3]. However, the transmission delay in this protocol has decreased the channel utilization. Considering the fixed multiple access technology, for example CDMA, can not only suppress the uplink noise effectively, but also utilize the bandwidth of the uplink channel availably, and the random multiple access technology has the better performance for unpredictable effects on signal propagation. So a new approach that combine the fixed multiple access technology with the random multiple access technology is proposed in this paper, that is the DS/CDMA time-slot nonpersistent CSMA access approach. The performance of DS/CDMA-CSMA multi-user communication systems been studied using this approach. The throughput of the system in different conditions has been investigated by Monte-Carlo method. This work shows that this transmission process is more efficient than traditional transmission approaches and will improve network performance, particularly for networks of today and the future which require more and more bandwidth. P. Bond (Ed.): ChinacomBiz 2008, CCIS 26, pp. 98–104, 2009. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009

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2 DS/CDMA Time-Slot Nonpersistent CSMA Multi-user Random Access Model 2.1 Multiple Access Model Multiple access protocol is used to manage the users sharing the medium resources efficiently. At present, there are two main multiple access protocols, that are ALOHA series and CSMA series. The maximum throughput

S max of the system for ALOHA

[4]

series and CSMA series is shown in table 1 . Table 1. The maximum throughput of the system for ALOHA series and CSMA series

Access Mode

Smax

ALOHA Slot time ALOHA Persistent CSMA Slot time –persistent CSMA P(0.1)- persistent CSMA nonpersistent CSMA P(0.03)-persistent CSMA slot time-nonpersistent CSMA

0.184 0.368 0.529 0.531 0.791 0.815 0.827 0.857

From Table 1 we can see that the system performance using time-lot nonpersistent CSMA multiple access technology is better than that of others. 2.2 DS/CDMA Time-Slot Nonpersistent CSMA Uplink Multi-users Random Access Model The free time is divided into time slot with β width in time-slot nonpersistent CSMA protocol as shown in Fig.1[4]. The slot length equals the end-to-end propagation delay. If the frame arrives at the free time slot, then it will be transmitted during the next time slot. The frame length L is an integral times of time slot.

Fig. 1. Principle model of time-slot nonpersistent CSMA

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The uplink channel is divided into M independent sub-channels with CDMA technology, that is the uplink random terminals are divided into M groups and each group has a code. The time-slot nonpersistent CSMA multiple access protocol is used in each independent sub-channels. The time-slot nonpersistent CSMA protocol is bus structure. A single transmission medium is shared by all terminals. The terminals represent computer resources which are connected to the network and compete for the access to the channel. When a terminal receives a message frame and transmits it again during a slot time, the terminal monitors the channel in order to find if there are other on-going transmissions. The terminal will capture the channel and transmit the message if the channel is free. Otherwise, if the channel is not free, the terminal will try again after some random delay. The maximum channel utilization is determined by the length of the frame and the propagation time. For simple analyzing the hypotheses are given. (1) There are infinite users. (2) The frame arrives in Poisson[5]. (3) The length of the frame is in geometrical distribution[6]. That is ⎧⎪ ρ (1 − ρ p [ T h e le n g th o f f ra m e = x ] = ⎨ ⎪⎩ 0 ,

)x −1 , x

= 1, 2 ,3 K

(1)

o th e r s

Where p [ T h e le n g th o f fr a m e = x ] is the probability of that the length of frame equals to x. The unit of x is time slot and its average value is 1 / ρ . (4) There are reliable channel and ideal power control. The capture effect is neglected[6]. (5) The terminal sends acknowledge signal immediately after it received data correctly. The length of the acknowledge frame is fixed and the transmission time is neglected. (6) The propagation delay is λ time slots. (7) The network load G is the sum of the service load of every channel. 2.3 Markov Model for DS/CDMA Time-Slot Nonpersistent CSMA Protocol There is only one uplink channel if M =1. The channel state of the n+1 time-slot only dependent on that of the n time-slot and have not any relations with the channel state before. So the discrete time Markov model can be constructed. There are five channel states in time-slot nonpersistent CSMA protocol. The transition of the channel state is shown in Fig.2. The meaning of every state is as follows:


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6

6

6

6

6

Fig. 2. Transition of the channel state

S1 —— idle slot, which means that the channel is free, no transmission and no propagation.

S2 —— message transmission slot, which means that the channel is transmitting message.

S3 —— response transmission slot, which means that the channel is transmitting response.

S4 —— collision slot between message and response. When the message and the response of the last message are transmitted at the same slot, there will be a collision.

S5 —— collision slot between messages. Two or more terminals attempt to transmit message at the same slot, a collision is said to occur. The probability of no message arriving is e − λG because the frame arrives in Poisson. So the following state transition matrix P can be obtained. ⎛ e − λG ⎜ ⎜ 0 P = ⎜⎜ 1 ⎜ 1 ⎜ ⎝ 1

λGe −λG 1− ρ

0

ρe

− λG

(

0

ρ1− e

0

0

0

0

0

0

0

0

0

−λG

)

1 − e − λG − λGe − λG ⎞⎟ ⎟ 0 ⎟ 0 ⎟ ⎟ 0 ⎟ 0 ⎠

(2)

In which the element P (i,j) is the transition probability from state i to state j. Let Π = [π 1

π 2 π 3 π 4 π 5 ] indicates the steady state probability vector. Where πj is the steady state probability of state Sj. Then from π j = ∑ π i Pij and ∑ π j = 1 we can get: π1 = ρ λGe− λG − ρ e− λG + 2 ρ π 2 = λGe − λG λGe − λG − ρ e − λG + 2 ρ

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π 3 = λGe−2 λG ρ (λGe− λG − ρ e− λG + 2 ρ )

π 4 = λ Ge − λG ρ (1 − e − λG ) λ Ge − λ G − ρ e − λ G + 2 ρ

π 5 = ρ (1 − e − λ G − λ Ge − λ G ) λ Ge − λG − ρ e − λG + 2 ρ

The network throughput is the sum of the throughput of message transmission frame and response transmission frame, so the following normalized network throughput S can be obtained. S = π 2 + π 3 = λ Ge − λG (1 + ρ e − λG ) (λ Ge − λG − ρ e − λ G + 2 ρ )

(3)

The average network load of each CDMA channel is G/M if M>1. The network throughput of each CDMA channel is: S ′ = λ G ′e − λG ′ (1 + ρ e − λ G ′ ) (λ G ′e − λ G ′ − ρ e − λ G′ + 2 ρ ) G′ = G M

(4)

The total network throughput is the sum of that of independent CDMA subchannels, that is, S M = MS ′ = M [λG′e − λG′ (1 + ρ e − λG′ )] (λG′e− λG′ − ρ e − λG′ + 2 ρ )

(5)

3 Simulation and Analysis of DS/CDMA Time-Slot Nonpersistent CSMA Multi-user Communication Systems 3.1 The Pseudo-random Code Effects on Network Throughput The Pseudo-random code, such as Walsh code, m-sequence code, Gold code and Kasami code, are used as the spreading code of DS/CDMA. There are some noises in 0.9 Walsh Codes m-sequemce Gold Codes Kasami Codes

0.8 0.7 0.6

S

0.5 0.4 0.3 0.2 0.1 0

0

100

200

300

400

500 G

600

700

800

900

1000

Fig. 3. Network throughput S versus network load G with different spreading codes


103

the channel except signal when signal is transmitted in one channel. The noises will influence the transmission performance including the network throughput. Now there are three kinds of noises are considered in this paper and they are AWGN (Gaussian white noise), pulse jamming and multiple access interference. Assuming the transmission delay λ=0.01, the number of users M =20 and the parameter of the frame length ρ =0.2, then the relationship between the network throughput and the network load can be achieved by Monte-Carlo simulation in different spreading codes such as Walsh Code, m-sequence, Gold Code and Kasami Code. It is shown in Fig.3. It can be seen from Fig.3 that the impact of different spreading codes on system throughput is obvious. Compared with other spreading codes, m-sequence’s is better because it has good correlation. So we just discuss the system that m-sequence is used as the spreading code in the following. 3.2 The Frame Length Effects on Network Throughput The frame length is an important factor that influences the network throughput from formula (5). The impact of the frame length on network throughput is depicted in Fig.4 under the conditions that the transmission delay λ=0.01, the user number M = 20, ρ = 0.2, 0.4, and 0.6 respectively and m-sequence as the spreading code. 0.7

0.6 ρ=0.2

0.5

S

0.4

ρ=0.4

0.3

0.2

ρ=0.6

0.1

0

0

100

200

300

400

500 G

600

700

800

900

1000

Fig. 4. Network throughput S versus network load G in different frame lengths

Fig. 4 shows that the network throughput strongly depends on the frame length. The bigger the parameter ρ of the frame length, or the smaller the frame length, the smaller the network throughput, the worse the performance of the network. 3.3 The Transmission Delay Effects on Network Throughput The transmission delay is another important factor that influences the network throughput from formula (3). Fig. 5 shows the relationship of network throughput

104


versus network load for the transmission delay λ=0.01, 0.02, and 0.03, the user number M=20, the parameter of the frame length ρ =0.2, and m-sequence as the spreading code. It can be seen from Fig.5 that the smaller the transmission delay, the bigger the network throughput. 0.7 λ=0.01 λ=0.02 λ=0.03

0.6

0.5

S

0.4

0.3

0.2

0.1

0

0

100

200

300

400 G

500

600

700

800

Fig. 5. Network throughput S versus network load G in different delays

4 Conclusions A mixed multiple access protocol, that is DS/CDMA slot-time nonpersistent CSMA multi-user access protocol is developed in this paper based on fixed multiple access technology and random multiple access technology. A Markov model for DS/CDMA time-slot nonpersistent CSMA protocol is established. The factors influencing the performance of the multi-user communication systems are analyzed by Monte-Carlo simulation. The results indicate that the DS/CDMA slot–time nonpersistent CSMA multi-user access mode has improved the performance of the original CSMA access mode greatly.

References 1. Garg, V.K.: Spread Spectrum (SS) and CDMA Systems. Wireless Communications & Networking, 317–367 (2007) 2. Aad, I., Ni, Q., Barakat, C., Turletti, T.: Enhancing IEEE 802.11 MAC in congested environments. Computer Communications 28(14), 1605–1617 (2005) 3. Chen, J., Sheu, S.-T.: Distributed multichannel MAC protocol for IEEE 802.11 ad hoc wireless LANs. Computer Communications 28(9), 1000–1013 (2005) 4. Ting, Z.: The research of checking control tree collision resolution in random multi-access system [Master degree thesis], Yunnan University (2004) 5. Xinlin, C.: Queuing Theory in Modern Communications. Publishing House of Electronics Industry (2000) 6. Fei-yan, S., Zhao-yang, Z., Wen-zheng, C.: On Throughput Performance of the CDMA Reservation-ALOHA Multiple Access System for the HFC Network. Journal of Electronics 29(11), 1552–1554 (2001)

Author Index

Chu, Jianfeng

90

Nurbol

Dittmann, Lars

10, 25

Fang, Guangwei

60

Gondim, Paulo Roberto de Lira Hearty, Paul 36 Herr, Laurin 36 Hu, Liang 10, 25, 90 Iversen, Villy B.

10

Kailay, Pooja 48 Khan, Moazzam 48 Krsek, Michal 36 Li, Nanjun 40 Lin, Chuang 76 Liu, Weidong 76 Luo, Wei 84 Lv, Yu-qin 98 Mattes, Rainer 1 Mattes, Tina 1

90

Polat, Bahadir K.

68

48

Ren, Fei 90 Ribeiro Junior, Sebasti˜ ao Boanerges Schiller, Frank 1 Schulze, Jurgen 36 Shen, Changxiang 84 Sun, Xianjun 76 Tuli, Nikhil

48

Wang, Yingjie 84 Weber, Uwe 1 Wu, Xiao 98 Xiao, Yanping

76

Zhao, Kuo 90 Zheng, Xiao 60 Zorn, Werner 40 Zu, Yun-xiao 98

68

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