Intelligent Hybrid Sand Cat and Humboldt Squid Optimization Algorithm–Based Dynamic Reliable Data Routing Mechanism for VANETs

Abstract

Vehicular ad hoc networks (VANETs) have seen progressive growth in recent years because of the increase in the popularity of intelligent vehicles and its relationship with the edge vehicular applications. Optimal route determination between the vehicles during interaction is highly challenging because of the dynamic change in the network topology. Specifically, the stability of the constructed clusters aids in achieving better routing process in the network for the purpose of maximizing energy efficiency. This communication between vehicles also requires an intelligent clustering mechanism for minimizing the delay during data delivery. In this paper, an intelligent hybrid sand cat and Humboldt squid optimization algorithm (HSCHSOA)–based dynamic reliable data routing mechanism is proposed with energy-efficient clustering strategy for achieving maximized data delivery in the network. This proposed HSCHSOA mechanism included the factors of grid size, orientation, velocity, number of nodes, and communication range into account during the process of fitness function evaluation. This fitness function evaluation achieved during the implementation of the sand cat optimization algorithm (SCOA) helps in confirming better selection of cluster head (CH) in the network. It further used the Humboldt squid optimization algorithm (HSOA) to guarantee the route optimization process, which enables reliable routes between the vehicular nodes under interaction. The simulation experiment of this HSCHSOA confirmed the minimized number of clusters constructed by 23.98%. It also maximized the stability of the clusters in terms of energy and delay by a margin of 18.42% and 20.36%, compared with the baseline approaches.