An Enhanced AI-Enabled Routing Optimization Algorithm for Internet of Vehicles (IoV)

Smart automobiles have become popular in recent years, facilitating the expansion of the Internet of Vehicles (IoV) networks. The Internet of Vehicles (IoV) is a network of automobiles with the ability to exchange and analyse data in real-time, necessitating a well-organized and effective data transmission method. Key problems in identifying an optimal path among the cars are cluster stability and dynamic topology change in IoV. The novelty of this manuscript lies in the route optimization method dependent on grid size, orientation, velocity, node number, and range. The proposed approach for creating and evaluating the best cluster head (CH) is derived from Harris Hawks' Optimization for Intelligent Route Clustering, for the optimal discovery of routes amongst the vehicles in the Internet of Vehicles networks. To analyse and validate the proposed method, other cutting-edge techniques are analysed. Considering the constraints such as the number of clusters and network, variable communication ranges, and vehicle quantity, our results suggest that the proposed method performs better than other techniques in the literature. Further experimentations have been performed considering Packet Delivery Ratio (PDR), bandwidth utilization, and latency which shows supremacy over other existing approaches. Furthermore, statistical analysis shows improvement in cluster optimization (by 80%) and increase stability of cluster (by 90.6 R-squared).