Multi-Agent Deep Reinforcement Learning for Joint Optimization of Latency and Energy Consumption Oriented to Communication-Sensing-Computation Integration in IoV

The rapid development of the Internet of Vehicles (IoV) has driven the need for efficient Integration of Communication, Sensing, and Computation (ICSC) to support advanced applications such as autonomous driving and real-time traffic management. However, the dynamic and resource-constrained nature of IoV environment poses significant challenges in achieving optimal performance, particularly in balancing multiple optimization objectives such as latency and energy consumption. Traditional optimization methods, such as Genetic Algorithms (GA), often struggle with inefficiency and unpredictability in dynamic scenarios. In this paper, we first propose an IoV Communication-Sensing-Computation Integration architecture to address the problem that the traditional IoV architectures cannot meet the needs of the ICSC. Then, we formulate the joint optimization of latency and energy consumption in ICSC as a constrained Multi-objective Optimization Problem (MOP). We employ Multi-Agent Deep Reinforcement Learning (MADRL) with a transformer module, combined with a weighted decomposition method and a neighborhood parameter transfer technique, to solve the problem. Simulation results show that our approach outperforms traditional methods in terms of convergence and diversity, and has a lower running time.