Fr-Aro: Secure interference aware fuzzy based clustering and hybrid optimization driven data routing in VANETs

Abstract

Vehicular ad hoc network (VANET) is composed of a group of nodes or vehicles that enable communication with other connected vehicles despite a lack of infrastructure. To design a reliable routing protocol is a major challenge for ensuring effective data communication among vehicles. This research develops a fuzzy logic concept to cluster the nodes and a Cluster Head is selected based on the fitness constraints. Thereafter, the optimal path used to route the data packets are selected using the proposed Fr-ARO based on the fitness, like energy consumption, delay, distance, Signal-to-Interference Ratio (SIR), entropy-weighted model, and trust factors. Some of the trust parameters considered for the routing mechanism are direct trust, indirect trust, historical trust, recent trust, and availability factor. Moreover, the optimal path is computed by the developed Fr-ARO algorithm that is devised through the incorporation of Aquila Optimizer (AO) with Remora Optimization Algorithm (ROA). The proposed model attains higher performance with the metrics, like energy, trust, distance, and SIR of 0.5744 J, 0.6123, 3.062 m, and 38.4674.