A Reputation based Weighted Clustering Protocol in VANET: A Multi-objective Firefly Approach

Abstract

Vehicular Ad hoc NETworks (VANETs) possess a dominant role in the development of Intelligent Transport Systems (ITS). VANETs, due to the rapid mobility of vehicles are a highly dynamic network. In order to make the network topology suitable for effective communication, clustering algorithms are widely used. Clustering algorithms enable VANET to efficiently handle the changing topology for Medium Access Control (MAC), routing and several other applications. In this work we put forward a Reputation based Weighted Clustering protocol (RWCP) for VANETs. The RWCP is framed by taking the direction of vehicles, position, velocity, number of nearby vehicles, lane ID, and the reputation of each node into consideration for stabilizing the VANET topology. On the other hand, dealing with diverse control parameters of RWCP makes optimizing a challenging task. The work employs a multi-objective problem which takes the RWCP’s parameters as the input and aims at providing enhanced cluster lifetime, Improved packet delivery ratio and reduced cluster overhead. Multi Objective Firefly Algorithm (MOFA), an evolutionary approach is used for optimizing the RWCP’s parameters. Simulations were done using the TETCOS NetSim simulator and MOEA framework for optimization. The results are evaluated with similar evolutionary optimization techniques. Experiments were conducted with realistic maps from OpenStreet Maps and its results were compared with other multi-objective optimization techniques: Multi-objective Particle Swarm Optimization (MOPSO) and Comprehensive learning Particle Swarm Optimization (CL-PSO). The investigation proposes that, the proposed methodology performs well concerning the Mean Cluster Lifetime, Packet Delivery Ratio and Control Packer Overhead.