Intelligent Metaheuristic-based Handover Algorithm for Vehicular Ad hoc Networks

—Recently, Vehicular Ad hoc Networks (VANETs) are becoming increasingly popular. VANETs are a subcategory of Mobile Ad hoc Networks (MANETs) in which nodes represent vehicles equipped with On-Board Units (OBUs). The fundamental reason is that VANETs improve safety for road users by providing vehicles with real-time road-related information. However, the increasing number of vehicles being introduced into these networks causes handover delays, and end-to-end delays, among other things. Therefore, the Quality of Service (QoS) is affected. This article proposes an Intelligent Metaheuristic-based Handover Algorithm (IMHA) to improve QoS in VANETs. The proposed IMHA is designed and implemented by integrating two of the most popular and recent optimization methods, namely disturbance Particle Swarm Optimization (d-PSO) and Ant Colony Optimization (ACO), wherein d-PSO assigns different priority levels to vehicles on the road to ensure safety meanwhile ACO determines the most profitable routes from the source to the destination. Furthermore, the Congestion Problem Reduction (CPR) algorithm is implemented in the IMHA to define the requests to process in priority order. The ACO and d-PSO hybrid methods have been tested and evaluated in real-world VANETs, giving us more confidence in their performance and robustness. Network Simulator 2 (NS-2) is used to simulate the proposed algorithm. Based on the outcomes, IHMA reduces end-to-end and handover delays and improves throughput at different vehicle velocities and network packet sizes. Consequently, this proposed solution guarantees improved QoS in VANETs. The experiment results show the proposed method outperforms existing handover algorithms, with a throughput of 92%, an end-to-end delay of 0.8 seconds, a handover delay and a computation time of less than 2.0 seconds, and an average memory usage of 60%.