A Fuzzy Logic-Based Cluster Head Selection Algorithm for Dynamic Vehicular Ad Hoc Networks

Abstract Vehicular Ad Hoc Networks (VANETs) represent a distinctive application domain in which vehicles traveling at high speeds on extensive roadways cause rapid, frequent topology changes. Under such highly dynamic conditions, designing an efficient and robust clustering scheme is a critical challenge. To address this issue, this paper proposes a fuzzy logic-based cluster head selection algorithm for optimal cluster-head selection in VANETs. Candidate vehicles are evaluated using three metrics—relative average velocity, following degree, and stability factor—and a fuzzy-logic aggregation rule identifies the vehicle that maximizes a composite stability index, thereby forming resilient clusters. The proposed method is benchmarked against a competition-based algorithm, a weighted algorithm, and a peer fuzzy-logic algorithm in terms of average cluster-head duration, average cluster-member duration, and clustering efficiency. Simulation results show that our approach consistently outperforms all baseline algorithms across these metrics.