vanet

  • View
    10

  • Download
    5

Embed Size (px)

Text of vanet

Faculty of Science, Technology andCommunicationMaster in Information and Computer SciencesMetaheuristics for Optimal Transfer of P2PInformation in VANETsAuthorJamal Toutouh El AlaminSupervisorPascal BouvryUNIVERSITY OF LUXEMBOURGAcknowledgementsMy sincere thanks to my advisor, Dr. Pascal Bouvry, for his support and guidancethroughouttheMasterThesiswork; theComputerScienceandCommunicationsresearch unit colleges,for sharing their knowledge with me; and the University ofLuxembourg, for oering me the opportunity of presenting this Master Thesis.I also would like to make a special reference to my family; my parents Sakinaand Ahmed, and my brothers Abdeslam, Mohamed, and Said; for working with meto make our dreams come true.I am deeply indebted to my girlfriend, Rosa, who has borne up the whole processwhich nishes with this Master Thesis.I cannot go without mentioning Networking and Emerging Optimization researchunit at University of Mlaga, specially its head Dr. Enrique Alba, for introducingme to the wonderful world of scientic research.Moreover, I have to thank to my friends for the great times that we have spenttogether and their support, specially to the Neudorfers.3Metaheuristics for Optimal Transfer ofP2P Information in VANETsMaster in Information and Computer SciencesJamal Toutouh El Alaminjamal@lcc.uma.esJune 8, 20102Contents1 Introduction 92 VANET Networks 132.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2 VANET Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3 Wireless Access Technologies . . . . . . . . . . . . . . . . . . . . . . . 172.3.1 Ad-hoc Network Technologies . . . . . . . . . . . . . . . . . . 172.3.2 Cellular Technoligies . . . . . . . . . . . . . . . . . . . . . . . 212.4 Routing Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.4.1 Communication Patterns. . . . . . . . . . . . . . . . . . . . . 232.4.2 Routing Protocols Classication. . . . . . . . . . . . . . . . . 232.4.3 Routing Protocols for VANETs . . . . . . . . . . . . . . . . . 252.5 Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.5.1 Safety-related Applications . . . . . . . . . . . . . . . . . . . . 312.5.2 Transportation Eciency Applications . . . . . . . . . . . . . 322.5.3 Information and Entertainment Applications. . . . . . . . . . 332.6 Simulation of VANETs . . . . . . . . . . . . . . . . . . . . . . . . . . 332.6.1 VANET Simulation Alternatives . . . . . . . . . . . . . . . . . 342.6.2 VanetMobiSim/Ns-2 Simulator . . . . . . . . . . . . . . . . . 352.7 Research Challenges in VANETs . . . . . . . . . . . . . . . . . . . . . 363 Metaheuristics 393.1 Denition of Metaheuristic. . . . . . . . . . . . . . . . . . . . . . . . 393.2 Metaheuristics Classication. . . . . . . . . . . . . . . . . . . . . . . 433.2.1 Trajectory-based Metaheurstics. . . . . . . . . . . . . . . . . 443.2.2 Population-based Metaheurstics . . . . . . . . . . . . . . . . . 473.3 Algorithms used in this Work . . . . . . . . . . . . . . . . . . . . . . 493.3.1 Simulated Annealing (SA) . . . . . . . . . . . . . . . . . . . . 503.3.2 Genetic Algorithm (GA) . . . . . . . . . . . . . . . . . . . . . 523CONTENTS3.3.3 Evolutionary Strategies (ES) . . . . . . . . . . . . . . . . . . . 573.3.4 Particle Swarm Optimization (PSO) . . . . . . . . . . . . . . 593.3.5 Dierential Evolution (DE) . . . . . . . . . . . . . . . . . . . 624 VDTP Protocol Optimization Problem 654.1 File Transfer in VANETs. . . . . . . . . . . . . . . . . . . . . . . . . 654.2 VDTP Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664.2.1 VDTP Protocol Operation. . . . . . . . . . . . . . . . . . . . 674.2.2 VDTP Protocol Conguration. . . . . . . . . . . . . . . . . . 694.3 OFTC Problem Denition . . . . . . . . . . . . . . . . . . . . . . . . 704.3.1 Search Space . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.3.2 Fitness Function . . . . . . . . . . . . . . . . . . . . . . . . . 715 Experiments 735.1 Optimization Framework. . . . . . . . . . . . . . . . . . . . . . . . . 735.1.1 Instances: VANET Scenarios . . . . . . . . . . . . . . . . . . . 745.1.2 Algorithms Parameter Settings . . . . . . . . . . . . . . . . . 775.2 Software and Hardware Tools . . . . . . . . . . . . . . . . . . . . . . 785.2.1 MALLBA Library . . . . . . . . . . . . . . . . . . . . . . . . . 785.2.2 Optimizing VDTP by using VanetMobiSim/Ns-2 . . . . . . . 795.2.3 Parallel Executions by using Condor . . . . . . . . . . . . . . 815.3 Used Metrics to compare Results . . . . . . . . . . . . . . . . . . . . 836 Results 856.1 Global Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 856.2 Algorithms Performance Study . . . . . . . . . . . . . . . . . . . . . 886.3 VANET QoS Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 917 Conclusions and Future Work 957.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 957.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97ADetailed Numerical Results 99A.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99A.2 Final Execution Times . . . . . . . . . . . . . . . . . . . . . . . . . . 106Bibliografy 1094List of Tables2.1 Current bluetooth classes . . . . . . . . . . . . . . . . . . . . . . . . . 192.2 Features of wireless network technologies proposed to deploy VANETs. 212.3 Main features of routing protocols applied on VANETs. . . . . . . . . 265.1 VANET instance specication . . . . . . . . . . . . . . . . . . . . . . 765.2 Parameterization of SA optimization algorithm. . . . . . . . . . . . . 775.3 Parameterization of PSO optimization algorithm. . . . . . . . . . . . 775.4 Parameterization of GA, ES, and DE optimization algorithms. . . . . 786.1 Final tness values for both VANET scenarios and the ve optimiza-tion algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 866.2 KS-testresults for the ve algorithms and two scenarios. . . . . . . . 876.3 Friedman Rank test with condence level95%. . . . . . . . . . . . . 886.4 Mean execution time (seconds) per independent run of each algorithmfor both, urban and highway, scenarios. . . . . . . . . . . . . . . . . . 906.5 Optimal congurations achievedinthemedianexecutionandtheCARLINK experts one for VDTP protocol and simulation values inurban scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916.6 Optimal congurations achievedinthemedianexecutionandtheCARLINK experts one for VDTP protocol and simulation values inhighway scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92A.1 Results obtained by simulating in urban scenario the PSO congura-tion (chunk_size=41358, timeout=10.00000, max_attempts=3). . . . 100A.2 Results obtained by simulating in urban scenario the DE congura-tion (chunk_size=23433, timeout=8.00000, max_attempts=10). . . . 100A.3 Results obtained by simulating in urban scenario the GA congura-tion (chunk_size=31196, timeout=3.83673, max_attempts=9). . . . 1015LIST OF TABLESA.4 Results obtained by simulating in urban scenario the ES conguration(chunk_size=28278, timeout=6.00000, max_attempts=9). . . . . . . 101A.5 Results obtained by simulating in urban scenario the SA conguration(chunk_size=19756, timeout=6.43308, max_attempts=3). . . . . . . 102A.6 Results obtained by simulating in urban scenario the CARLINK con-guration [24] (chunk_size=25600, timeout=8.0, max_attempts=8). 102A.7 Results obtained by simulating in highway scenario the PSO cong-uration (chunk_size=29257, timeout=6.42140, max_attempts=9). . . 103A.8 Results obtained by simulating in highway scenario the DE congu-ration (chunk_size=19810, timeout=6.91179, max_attempts=8). . . 103A.9 Results obtained by simulating in highway scenario the GA congu-ration (chunk_size=34542, timeout=9.54986, max_attempts=10). . . 104A.10 Results obtained by simulating in highway scenario the ES congu-ration (chunk_size=38490, timeout=8.15197, max_attempts=12). . . 104A.11 Results obtained by simulating in highway scenario the SA congu-ration (chunk_size=32002, timeout=8.21363, max_attempts=4). . . 105A.12 ResultsobtainedbysimulatinginhighwayscenariotheCARLINKconguration (chunk_size=25600, timeout=8.0, max_attempts=8). . 105A.13 Execution time in seconds for the algorithms to nd the best solution(Best sol.)and to nish the whole process (Total) solving the OFTCproblemintheurbanscenario. Eachrowindicatesanindependentrun of 30 (#). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107A.14 Execution time in seconds for the algorithms to nd the best solution(Best sol.)and to nish the whole process (Total) solving the OFTCproblem in the highway scenario. Each row indicates an independentrun of 30 (#). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1086List of Figures1.1 VANET use case: Warn of obstacle in the road. . . . . . . . . . . . . 102.1 Example of a MANET network. . . . . . . . . . . . . . . . . . . . . . 142.2 Example of a VANET application: Warning of a trac accident. . . . 152.3 Example of a VANET application: Warning of obstacles in the road. . 152.4 DSRC Channel assignment in North America. . . . . . . . . . . . . . 202.5 Accident warning by using GPRS. . . . . . . . . . . . . . . . . . . . . 212.6 Communication pattern representations. . . . . . . . . . . . . . . . . 242.7 Blind-Flooding protocol representation. . . . . . . . . . . . . . . . . . 262.8 CDS protocol representation with dominant nodes in black and pas-sive nodes in white. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.9 Discovery procedure used by DSR routing protocol. . . . . . . . . . . 282.10VANET application: Cooperative forward collision warning. . . . . . 312.11VANET application: Approaching motorcycle warning. . . . . . . . . 322.12VANET application: Publicity board podcasting of a cinema. . . . . . 332.13VanetMobiSim/Ns-2 basic architecture. . . . . . . . . . . . . . . . .