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Research On Routing Protocol And Optimization Algorithm In Vehicular Ad Hoc Network

Posted on:2015-01-12Degree:MasterType:Thesis
Country:ChinaCandidate:X Y CuiFull Text:PDF
GTID:2268330428497786Subject:Signal and Information Processing
Abstract/Summary:PDF Full Text Request
As a new type of wireless network technology, Vehicular Ad HocNetwork(VANET) has been widely studied and applied. In urban traffic, VANET isone of the important applications of intelligent transportation systems. It can providevehicles with a communication platform and reduce the incidence of traffic accidents.The topology is changing fast, which lead to the decline of the networkperformance. Therefore, designing a high reliability and real-time vehicle routingprotocol is an important task in VANET.In this paper, the main work is as follows:(1) Through analysing the features of VANET and AODV protocol, we combinethe ant colony algorithm to improve the AODV protocol. Because it suits to discreteproblems. We can get the effective optimal solution because of the positive feedbackin ant colony algorithm;(2)Based on the research of ant colony algorithm, we regard the vehicle motionstate as constraint parameters. This paper constructs mathematical model throughmodifying the pheromone concentration calculation formula and the heuristicinformation. These are the theoretical basis for improving the AODV protocol;(3) According to the characteristics of VANET, the vehicular mobility model hasbeen analyzed. The packet loss rate, average end-to-end delay and normalized routingoverhead have been defined three performance indexes. The simulation and analysesof the AODV, DSDV and DSR protocol have been designed in six city scenario;(4) We redesign the message of route request, route reply and route error ofAODV routing protocol. The improvement is to change them to the forward antmessage, the backward ant message and the error message with motion stateinformation;(5) Using the NS-2as a network simulation platform, we construct differentsimulation. The environment is set using TCL scripts. Then we analyse thedifferences of PIACO-AODV routing protocol and the traditional AODV protocol.According to the result of simulation, we prove that the stability of routing is verified,which can achieve the goal of optimizing packet arrival rate and reducingpacket loss rate.The innovations of this paper are as follows:(1) The ant colony algorithm is introduced in the AODV protocol. The algorithmis based on the pheromone of ant colony algorithm in the route discovery and routeselection process. It can perfect optimization ability of the protocol in the pathsearch.At the same time, it can improve the convergence speed and the performanceof the protocol.(2) This paper presents PIACO-AODV routing protocol, which establishs aprotocol operational processes to find the most stable and optimized routing. ThePIACO-AODV protocol combines the characteristics of vehicle Ad Hoc network, aswell as improved AODV protocol. These following improvements could be included:The position information acquisition to predict the motion position node possiblefuture, so it is concluded that the relay node may be connected stability longer. Thesecond improvement was PIACO-AODV routing protocol used the ant colonyalgorithm to improve node selection mechanism, in order to select more stability inmultiple paths as the preferred path. The third improvement was PIACO-AODVrouting protocol could amend the error message response mechanism, so that nodescan make response in time to changes in network topology.The running track of vehicle nodes is fixed and distributed dense relatively inurban traffic environment. It can provide a more stable result for vehicle nodes. Thesimulation results confirm that the PIACO-AODV protocol has an improvedperformance and more advantages in stable maintaining network. So it is moresuitable for the condition of VANET with high speed and fast changing topology. ThePIACO-AODV protocol can be widely used in city traffic scenario.
Keywords/Search Tags:VANET, AODV protocol, mobility model, ant colony algorithm
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