Cooperative Sensing and Uploading for Quality-Cost Tradeoff of Digital Twins in VEC

Recent advances in sensing technologies, wireless communications and computing paradigms drive the evolution of vehicles toward intelligent and electronic consumer products. This paper investigates digital twins in vehicular edge computing (DT-VEC) via cooperative sensing and uploading of vehicles, aiming at achieving the quality-cost tradeoff in DT-VEC. First, a DT-VEC architecture is presented, and the digital twin system is modeled to reflect the real-time status of physical vehicular environments. Second, we derive the cooperative sensing model and the vehicle-to-infrastructure (V2I) uploading model by considering the timeliness and consistency of the sensed information, and the redundancy, sensing cost and transmission cost of the system. On this basis, a bi-objective problem is formulated to maximize the system quality and minimize the system cost. Third, we propose a multi-agent multi-objective (MAMO) deep reinforcement learning model, including the design of distributed actors for storing replay experiences and a learner with a dueling critic network for agent action evaluation. Finally, we give comprehensive performance evaluation, which demonstrates that MAMO outperforms existing competitive solutions around 2~5 times on maximizing system quality, while still saving system cost around 18%~44%.