Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

Abstract

A network of wirelessly connected vehicles by using any mean of connectivity is termed as the Internet of Vehicle (IoV). Managing this type of network is a challenging task. Clustering is a technique to efficiently manage resources in this type of network. In a cluster, all inter/intra cluster communication is managed by a cluster head (CH). Load on each CH, the lifetime of the cluster and the total number of clusters in a network are some parameters to measure the efficiency of the network. In this paper, a novel technique based on moth flame clustering algorithm for IoV (MFCA-IoV) is proposed. Moth flame optimizer is a nature-inspired algorithm. MFCA-IoV generates optimized clusters for robust transmission and is evaluated experimentally with renowned techniques. These techniques are Grey-Wolf-optimization-based method used for the clustering called as GWOCNETs, multi-objective particle-swarm-optimization (MOPSO), clustering algorithm based on Ant colony optimization for vehicular ad-hoc networks termed as CACONET and comprehensive learning particle-swarm-optimization (CLPSO). To assess the comparative efficiency of these algorithms, numerous experiments are performed. The parameters like network grid-size, number of nodes, speed, direction, and transmission-range of the nodes are considered for optimized clustering. The results indicate, MFCA-IoV is showing 73% nodes, which are not selected as a cluster head while existing techniques are providing 57%, 50%, 51%, and 58% for GWOCNETs, CLPSO, MOPSO, and CACONET, respectively. Hence, lesser the nodes are selected as CH, the more optimal result will be considered.