Enhanced clustering approach for efficient relay vehicle selection in vehicular ad hoc networks

Vehicular Ad-hoc Networks (VANETs) face significant challenges in ensuring reliable data delivery due to the highly dynamic mobility of vehicles and frequent link disruptions. Traditional clustering approaches often suffer from unstable Cluster Head (CH) selection, which degrades packet delivery, increases communication overhead, and other relative metrics also get effected. To address these limitations, this paper proposes an Enhanced Clustering-based Efficient Relay Vehicle (ECERV) selection framework that integrates a dual-strategy CH selection mechanism with predictive relay support from Road Side Units (RSUs). The proposed method combines stability and proximity factors to improve cluster lifetime, while RSUs dynamically predict optimal relay vehicles for data forwarding in uncovered regions. Extensive NS-2 simulations demonstrate that the proposed ECERV selection scheme outperforms its competitive approaches and achieves 23% higher throughput, 12% higher packet delivery ratio, 25% higher requested data completeness, and 14% lower delay compared to baseline protocol, while also reducing control overhead by 24%, energy consumption by 8.5%, and extending the cluster stability period by 50% under a 100-vehicles scenario. These results confirm that the proposed ECERV selection scheme provides a scalable and robust solution for enhancing data dissemination in highly mobile vehicular communication scenarios.