A-MCAC: An Adaptive Mobility- and Connectivity-Aware Clustering Framework for Stable and Efficient Internet of Vehicles Communications

The Internet of Vehicles (IoV) enables intelligent transportation services through real-time communication between vehicles and infrastructure. However, high mobility and frequent topology changes often reduce cluster stability and communication reliability. Traditional clustering approaches commonly rely on static decision criteria and periodic maintenance mechanisms, leading to frequent reclustering operations and increased communication overhead. This paper proposes an Adaptive Mobility- and Connectivity-Aware Clustering (A-MCAC) framework for IoV environments. The proposed approach combines predictive mobility analysis, link lifetime estimation, adaptive weight optimization, and event-driven cluster maintenance to improve Cluster Head (CH) selection and cluster stability. By dynamically adapting clustering decisions according to network conditions, A-MCAC aims to reduce reclustering operations and enhance communication efficiency. The proposed framework is evaluated through simulations and compared with CARAC, MetaLearn, and CPB using cluster lifetime, CH lifetime, reclustering rate, link stability, clustering overhead, packet delivery ratio, end-to-end delay, and throughput. Results demonstrate that A-MCAC improves cluster stability, increases communication reliability, maintains a favorable overhead-performance trade-off, and achieves better network performance across varying vehicle densities. These findings demonstrate that A-MCAC improves both clustering stability and communication efficiency in highly dynamic IoV environments.