Energy Aware Cross Layer Based Clustering and Congestion Control Using Mexican Axolotl Algorithm in VANET

Abstract

– In recent years, wireless communication networks have been developing rapidly, which causes many challenges to be faced in vehicular ad hoc networks (VANETs). Congestion is a degradation of the quality of service in which messages begin to be delivered less often to the recipient. So, in this paper, to optimize the energy efficiency of network cross layer based clustering protocol is presented. For clustering, Reputation based Weighted Clustering Protocol (RBWCP) is presented. To enhance the clustering performance of RBWCP, clustering parameters of the protocol are optimally chosen using Mexican Axolotl Algorithm (MAA). In this work, cluster head is selected in every cluster using the weight vehicle’s reputation such as speed, direction and position. After the formation of cluster, mean value of vehicle density (MVVD) threshold is estimated depend on the received signal strength of the vehicles. This threshold value is compared with the density of each vehicle inside the cluster. If the density of the vehicle is greater than the threshold, then the cluster is divided into sub-clusters. It leads to control the congestion in the network. The execution of the proposed model is calculated in terms of cluster lifetime, delivery ratio, delay, overhead and throughput.