Efficient IoV Resource Management Through Enhanced Clustering, Matching, and Offloading in DT-Enabled Edge Computing

The integration of edge computing with digital twins (DTs) has been instrumental in driving substantial advancements in the Internet of Vehicles (IoV) domain in recent times, particularly within the 6G wireless networks where DTs enable real-time simulation, monitoring, analysis, and high-speed transmissions for connected vehicles. Despite these benefits, several challenges arise, including dynamic network topologies resulting from the high-speed vehicle mobility, frequent edge server switches causing instability and increased latency, and the limited computing resources struggling to cope with the demanding computational tasks. This article addresses these issues by proposing a framework where the vehicles serve as the auxiliary mobile edge computing (MEC) servers. It introduces an enhanced density-based spatial clustering of applications with the noise (DBSCAN) algorithm designed to improve the clustering of vehicles under high-speed movement scenarios. Moreover, a multi-to-multi matching algorithm is devised to effectively associate vehicles with the auxiliary MEC servers. To alleviate the problem of insufficient computing resources due to intense computational loads during DT updates, a deep reinforcement learning (DRL)-based approach is utilized to make the optimal computation offloading decisions. This work further refines the offloading strategy by adopting the improved double deep Q-network (DDQN) and the dueling deep Q-network algorithms. Simulation experiments validate that the proposed clustering improvement and the DRL-based offloading decision-making scheme outperform the existing baseline methods across multiple performance metrics, such as clustering effectiveness, processing latency reduction, algorithmic efficiency, and convergence rate.