An Evolutionary Approach for Optimized VANET Clustering

Abstract

Vehicular Ad-hoc Networks (VANETs) are the core of intelligent transportation systems that aim to provide road users with safety and infotainment. Clustering the vehicles in VANETs allows better utilization of the network resources, more reliable routing and enhanced security in addressing the faced threats. As the case of all VANETs protocols, optimizing the clustering algorithm is essential to achieve the best clustering performance. In this paper, we formulate the clustering algorithm optimization problem as a many-objective optimization problem. Then, we propose an approach to optimize clustering algorithm configuration parameters. NSGA-III many-objective metaheuristic is used as the optimization tool in this approach. The proposed approach is evaluated experimentally by optimizing the double head clustering (DHC) algorithm and comparing its performance with and without the optimization algorithm. The experimental results show that the optimal configuration increases the cluster lifetime by up to 134% and reduces the clustering packet overhead by un to 30%.