Presenting an approach for scalable routing based on K-means clustering and the Gray Wolf algorithm for inter-vehicle networks

Vehicular Ad-hoc Networks are networks that are created by combining moving vehicles and related infrastructures and have challenges, the most important of which are routing and power reduction due to the dynamic nature of this network, which is due to the increase in the number of nodes. In this research, clustering methods such as K-Means and evolutionary algorithms are used to improve routing in these networks. In fact, the K-Means algorithm divides the network into smaller parts (several smaller clusters) and a cluster head is generated in each of the clusters, which is used to transfer information between vehicles inside and outside a cluster, and choosing a suitable and reliable cluster head is of great importance. Therefore, the Gray Wolf algorithm is used to choose the best and most suitable cluster head for each of the clusters. In the simulation of the proposed method, various experiments were conducted to investigate the effect of the number of data transmission clusters, packet delivery rate, message sending delay, and message number on the transmission performance. The results showed that by increasing the number of vehicles in the proposed method, fewer clusters are required, the path of each message and the stopping time at each step are stored, and the proposed method has better performance than the basic method.