CAMONET: Moth-Flame Optimization (MFO) Based Clustering Algorithm for VANETs

Abstract

A network aggregated of wirelessly connected vehicles is recognized as vehicular ad hoc networks (VANETs). Clustering in vehicular network is a technique among many others, which targets to improve communication proficiency in VANETs. In each cluster, there is one cluster head (CH) used to manage the whole cluster. All the communications are accomplished by the CHs, i.e., inter-cluster and the intra-cluster communications. The efficiency of a network is measured by number of CHs, load on each CH and lifetime of clusters. In this paper, a novel Clustering Algorithm centered on Moth-Flame Optimization for VANETs (CAMONET) is anticipated. This is a nature-inspired algorithm. CAMONET generates optimized clusters for robust transmission. CAMONET is evaluated experimentally with renowned techniques, such as multiobjective particle swarm optimization, clustering algorithm based on ant colony optimization for VANETs, and comprehensive learning particle swarm optimization. To assess the comparative efficiency of these algorithms, numerous experiments are performed. The results are accomplished by modifying the values of grid size of the network, the number of nodes in the network, and the transmission range of nodes. The speed, direction, and transmission range of the nodes are the notable factors considered for optimized clustering. The results indicate that CAMONET delivers near optimal results that develops it into an efficient method to perform vehicular clustering in order to improve the overall performance of the network.

Keywords

• Intelligent transportation system (ITS)
• vehicular ad hoc networks (VANETs)
• ant colony optimization (ACO)
• moth-flame optimization (MFO)
• clustering
• meta heuristic algorithms