CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.

Farhan Aadil,Khalid Bashir Bajwa,Salabat Khan,Nadeem Majeed Chaudary,Adeel Akram,Wen-Bo Du

doi:10.1371/journal.pone.0154080

Abstract

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

Highlights

Clustering is a technique for assembling a group of nodes inside a geographical locality according to certain regulations
This paper presents a detailed analysis of multi-objective evolutionary algorithms in vehicular ad hoc network (VANET)
The node clustering is done efficiently, and near optimal solutions are generated by the proposed algorithm

Summary

Introduction

Clustering is a technique for assembling a group of nodes (mobile gadgets, devices, automobiles, etc.) inside a geographical locality according to certain regulations. Such regulations vary from one algorithm to another and, are the decisive aspect in creating dependable clusters [1]. Each cluster is composed of cluster nodes (CN), which nominate or elect a single CH. The group of nodes within a CH’s transmission range is referred to as its neighborhood. Any CN can be elected as the CH; in several algorithms, some types of nodes possess more effective properties for becoming the CH. Cluster size depends on the nodes’ transmission range, and as a result varies from cluster to cluster [4,5,6]

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Conclusion