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<p>As a fundamental aspect of the intelligent transportation system, vehicular adhoc network (VANET) has attracted considerable interest from both research and business community. The inherently dynamic characteristics of VANET, marked by swift changes in topology and frequent disconnections, create substantial obstacles for effective communication. The clustering techniques bolster network stability, minimize overhead, and enhance scalability by grouping vehicles into clusters, each overseen by a specific cluster head responsible for managing intra-cluster communication. Several metrics for cluster head selection have been presented; however, there is no systematic comparison of their efficacy under various environmental conditions. Four cluster head selection metrics—speed, stability, centroid, and hybrid (stability + centroid)—are compared and examined in this study under various urban environmental conditions, including clear, rainy, and obstacle-filled scenarios. Using K-means clustering along silhouette analysis and a MATLAB simulation, we evaluate performance in terms of delay, packet delivery ratio (PDR), and throughput. Findings reveal that centroid-based and hybrid cluster head selection present the best performance in terms of throughput, PDR, and delay. The findings support a flexible cluster head selection process that takes traffic density and the environment into account, and they provide direction on selecting cluster head metrics appropriate for both safety and non-safety applications.</p>

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Cluster Head Selection Metric in VANETs Under Dynamic Environmental Conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月2日

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  • Gurtej Kaur,
  • Meenu Khurana,
  • Amandeep Kaur

摘要

As a fundamental aspect of the intelligent transportation system, vehicular adhoc network (VANET) has attracted considerable interest from both research and business community. The inherently dynamic characteristics of VANET, marked by swift changes in topology and frequent disconnections, create substantial obstacles for effective communication. The clustering techniques bolster network stability, minimize overhead, and enhance scalability by grouping vehicles into clusters, each overseen by a specific cluster head responsible for managing intra-cluster communication. Several metrics for cluster head selection have been presented; however, there is no systematic comparison of their efficacy under various environmental conditions. Four cluster head selection metrics—speed, stability, centroid, and hybrid (stability + centroid)—are compared and examined in this study under various urban environmental conditions, including clear, rainy, and obstacle-filled scenarios. Using K-means clustering along silhouette analysis and a MATLAB simulation, we evaluate performance in terms of delay, packet delivery ratio (PDR), and throughput. Findings reveal that centroid-based and hybrid cluster head selection present the best performance in terms of throughput, PDR, and delay. The findings support a flexible cluster head selection process that takes traffic density and the environment into account, and they provide direction on selecting cluster head metrics appropriate for both safety and non-safety applications.