A Multiple-Criteria Decision Analysis Clustering and Cluster Head Selection Algorithm in Vehicular Network

Vehicle ad-hoc network (VANET) faces heavy traffic with communication burden to connect with various nodes in the network, uses clustering techniques with neighboring vehicles, and elects a powerful and efficient vehicle as a cluster head. Cluster head vehicle collects all the information and pass them out and transmit outside data to other members in the cluster. However, clustering faces issues such as the inability of choosing feasible and stable clusters for a long time. On the other hand, the nomination of cluster heads fails to adhere to real-world parameters. To address these issues, in this paper, a k-means algorithm and direction vector-based clustering techniques and a cluster head selection method with the utilization of fuzzy-based PROMETHEE multi-criteria decision techniques are proposed. Moreover, mathematical and rational analysis shows that the method is implementable and achievable. Finally, a range of future directions of the study is shown.