Cluster Head Selection Based on ACO in Vehicular Ad-Hoc Networks

Abstract

A critical aspect of Vehicular Ad-Hoc Networks (VANETs) is the selection of cluster heads. In this paper, an approach to cluster head selection in VANETs based on Ant Colony Optimization (ACO) is presented. By utilizing the collective intelligence of ants, the proposed algorithm will guide the selection process and optimize network performance. The algorithm selects cluster heads by taking into account a variety of parameters, as vehicle proximity, communication range, residual energy, and the attractiveness of neighboring vehicles based on pheromone values. Using extensive simulations and performance evaluations, it has been verified that the proposed algorithm has shown a significant improvement in network performance, as it reduces packet loss, improves network throughput, and extends the life of the network, also, the algorithm exhibits adaptability and self-organization, allowing it to dynamically adjust to changes in the vehicular environment in real-time. The results of this study highlight an overall improvement of communication and data dissemination in vehicular networks.