Community Based Algorithms to Optimize Reliable and Dynamic Route Discovery for Internet of Vehicles

Abstract

The Internet of Vehicles (IoV) is a distributed network through which the newlinedata is created by connected vehicles. A vehicular network is an autonomous newlinesystem where connected devices follow through a wireless link. With the rapid newlinegrowth of urban population, data communication is based on Vehicle to Vehicle newline(V2V) and Vehicle to Roadside (V2R). During the communication of data, there is newlinea chance of route failure due to the dynamic mobility of vehicles, so that it is newlinedifficult for the source node to find the exact position of the neighboring node and newlineoptimal route identification in IoV. Several routing algorithms have been designed newlinebut the fast mobility and link connectivity between vehicles is a challenging task in newlineobtaining efficient and reliable data transmission. In order to solve these issues, this newlineresearch work proposes four algorithms namely the four proposed works such as newlineIdentical Destination Based Community algorithm in IoV (IDCIoV), Trilateral newlineLocation Identified Maximum Weighted Directive Spanning Tree (TLIMWDST), newlineProbit Regressive Chaotic Bio-inspired Grey Wolf Optimization (PRCBGWO), and newlineQuantile Regressive Fish Swarm Optimized Deep Convolutional Neural Learning newline(QRFSODCNL) used to improve the reliability of data transmission with minimum newlinedelay and data loss. All the four contributions are implemented using the NS2.34 newlinesimulator. newlineIDCIoV and TLIMWDST detect the location of the neighboring node and newlinefind the optimal path for data transmission using Acyclic Directed Tree and newlineMaximum weighted spanning tree consecutively. PRCBGWO and QRFSODCNL newlineuse the Bio inspired optimization algorithm to find the optimized path among newlinemultiple path. The performance of four methods is analyzed using different metrics newlinesuch as packet delivery ratio, Packet Loss Rate, End to End Delay and Throughput. newlineSimulation results of the proposed algorithms perform better than the other existing newlinemethods newline