IWHO-Based Cluster Head Selection for Vehicle-to-Vehicle Communication in Intelligent Transport System

Abstract

In order to facilitate effective communication between the V2V infrastructure, Vehicular Ad hoc Networks (VANETs) are utilized.Problems with routing, security, and node management are now plaguing VANETs that use vehicle-to-vehicle communication.New avenues for investigation into VANET routing, security, and mobility management have opened up because to intelligent transportation systems.Optimal routing for targeted traffic scenarios is one of the main issues in VANETs.Because VANET vehicles are constantly moving at high speeds, traditional protocols like AODV, OLSR, and DSDV are not suitable for this network.In a similar vein, swarm intelligence routing algorithms like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have had some success in optimizing routing in VANET scenarios involving dense, sparse, and realistic traffic.Furthermore, most metaheuristics methods have issues with slow convergence speed, premature convergence, and becoming stuck in local optima.Hence, a new metaheuristic approach to selecting the cluster head is suggested in the study, which employs an improved wild horse optimization algorithm (IWHO).The social behaviour of wild horses served as an inspiration for the development of IWHO.The ethics of the horse informed the proposed approach.The next step is to cluster the vehicles according to the reliability of linkages criteria.Subsequently, a maintenance phase is suggested for the purpose of redistributing vehicles within the clusters and updating the cluster heads.Lastly, a MATLAB simulation is run on a real-life urban setting to assess the efficacy of the proposed strategy.A 76% decrease in change rate is indicative of improved stability, while a 37% rise in throughput and a 19% decrease in average latency are indicators of improved performance.

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