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 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 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 paper addresses these issues by proposing a framework where vehicles serve as auxiliary mobile edge computing (MEC) servers. It introduces an enhanced Density-Based Spatial Clustering of Applications with 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 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 optimal computation offloading decisions. This work further refines the offloading strategy by adopting the Improved Double Deep Q-Network (DDQN) and Dueling DQN algorithms. Simulation experiments validate that the proposed clustering improvement and DRL-based offloading decision-making scheme outperform existing baseline methods across multiple performance metrics, such as clustering effectiveness, processing latency reduction, algorithmic efficiency, and convergence rate.