Enhancing Urban VANETs Stability: A Single-Hop Clustering Strategy in Metropolitan Environments
Pouya Firouzmakan, Suprakash Datta
TL;DR
This paper tackles urban VANET stability by proposing SMZCA, a single-hop, zone-based clustering algorithm that eliminates reliance on RSUs and leverages public transport buses as default cluster heads. A novel area-preparation framework divides metropolitan regions into zones, combined with a zone-search mechanism to map vehicles efficiently, while CHEC and ZOTSim-driven metrics guide CH selection and cluster formation among SAVs. The study introduces a centralized clustering stability metric (VCSM) and demonstrates, via SUMO-generated GTA case studies, that SMZCA yields superior cluster stability across varied transmission ranges, even without RSUs, and outperforms Befit-based and DSCA schemes while reducing SAV churn. Overall, the work highlights the practicality of zone-based clustering for metropolitan VANETs, the reduced reliance on infrastructure, and the importance of the ZOTSim factor for CH selection; it also notes trade-offs when RSUs are deployed and points to multi-hop extensions and routing designs as future directions.
Abstract
Vehicular Ad-hoc Networks (VANETs), a subclass of Mobile Ad-hoc Networks (MANETs), are expected to play a crucial role in the future of intelligent transportation systems (ITSs). A key objective of VANETs is to enable efficient and cost-effective communication among vehicles while supporting a large number of network participants and minimizing infrastructure dependency. However, the highly dynamic nature of vehicular networks poses significant challenges to their deployment. Clustering techniques are employed to address these challenges, with a strong emphasis on stability, as they directly influence the routing process and enhance the quality of service (QoS). This paper explores the feasibility of reducing reliance on roadside units (RSUs) in metropolitan areas while improving cluster stability. We propose an efficient clustering algorithm tailored for urban environments, leveraging existing metropolitan infrastructure to compensate for the absence of RSUs. Our approach designates public transportation buses as primary cluster heads (CHs), minimizing reliance on additional infrastructure, while stand-alone vehicles (SAVs) dynamically select additional CHs. Through comprehensive case studies and comparative analysis with existing algorithms, our results demonstrate the superior performance of the proposed method across different transmission ranges (TRs).