Modeling of Optimal Adaptive Weighted Clustering Protocol for Vehicular Ad hoc Networks

Abstract

Vehicular ad hoc network (VANET) is a mobile adhoc network widely used in intelligent transportation systems (ITS). Owing to the unique features of VANET like self-organized, recurrent link interruptions, and quick topology modifications, the design of an effective clustering protocol is a challenging problem. The clustering process is considered an optimization problem and can be solved using metaheuristic algorithms. Therefore, this paper presents an adaptive weighted clustering protocol with artificial fish swarm optimization (AWCP-AFSO) algorithm for VANET. The proposed AWCP-AFSO technique aims to select the CHs effectively and thereby accomplishes energy efficiency. To construct clusters, the AWCP-AFSO algorithm derives an objective function from electing an optimal set of CHs. A wide range of simulations are performed, and the results are investigated in terms of several performance measures. The experimental values showcased the betterment of the AWCP-AFSO technique over the recent techniques.