Joint Optimization of Edge Collaboration and Resource Allocation in DT-Assisted IoVs

To address issues related to reduced neural training accuracy, increased latency, and high energy consumption due to resource limitations of edge servers in the Internet of Vehicles (IoV), this article proposes a joint optimization scheme for edge collaboration and resource allocation in the digital twin-assisted IoV (DT-IoV) with integrated sensing and communication (ISAC). First, a digital twin-assisted environment-aware collaborative mechanism (DT-EACM) is proposed. The DT system makes edge collaboration decisions based on task requirements and available resources, selecting either the ISAC mode or the coordinated environment-aware instruction transmission (InsT) mode. Second, an analysis of deviations in communication, sensing, and computing resources within the collaboration mechanism has been conducted, and a joint optimization problem for edge collaboration and resource allocation is formulated with the goal of minimizing cumulative latency and energy consumption. Finally, since the above problem is a mixed-integer programming problem, it is transformed into a Markov decision process (MDP). A resource scheduling algorithm based on twin-delayed deep deterministic policy gradient (TD3) is proposed, combined with the prioritized experience replay (PER) mechanism to improve algorithm convergence speed and stability, thus minimizing cumulative latency and energy consumption in edge task processing. The simulation results demonstrate that the proposed algorithm effectively reduces cumulative training latency and energy consumption in edge task collaboration compared to other algorithms.