A Fuzzy-based Cluster Head Selection Technique for Optimizing Communication of VANETs

Abstract

The continuous developments in vehicular communication technology have brought a significant interest in Vehicular Ad-Hoc Networks (VANETs). VANETs aim to enhance road safety, improve traffic management, and provide a suite of infotainment services to passengers. This type of network is characterized by high-speed, dynamically varying mobility, leading to increased Energy Consumption (EC), End-to-End (E2E) delay, and Routing Overhead (RO) during network communication. Various researchers have developed ways to overcome this drawback through the employment of clustering techniques in VANETs. However, utilizing clustering techniques in VANETs is critical as it requires maintaining robust communication links, optimizing resource allocation, and minimizing E2E delay. Subsequently, this paper proposes an improved Fuzzy-based Cluster Head Selection (FCHS) technique to enhance the overall performance of VANET.  In VANET, the clustering is formed from Cluster Head (CH), Cluster Child (CC), and Backup-Cluster Head (BCH) along with the other network nodes. The FCHS optimizes the CH selection using a fuzzy logic algorithm based on various VANET parameters, including average distance, satisfaction degree, EC, Packet Delivery Ratio (PDR), and vehicle connectivity level. The performance of the proposed FCHS technique is simulated utilizing Network Simulator (NS) 2.35 with the Simulation of Urban MObility (SUMO) platform. The performance metrics that are considered for the result evaluation are PDR, EC, E2E delay, and RO. The overall results of the VANET is compared with two recent methods. The results show that the VANET  performance with the aid of the proposed FCHS technique achieves the highest PDR, low EC, E2E delay, and RO.