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

TL;DR

This paper proposes a cooperative sensing and uploading framework for digital twins in vehicular edge computing, balancing quality and cost through a multi-agent deep reinforcement learning approach.

Contribution

It introduces a novel DT-VEC architecture, models the sensing and uploading processes, and develops a MAMO deep RL solution for optimizing quality-cost tradeoff.

Findings

MAMO deep reinforcement learning outperforms baseline methods.

The proposed model effectively balances system quality and cost.

Simulation results validate the superiority of the approach.

Abstract

Recent advances in sensing technologies, wireless communications, and computing paradigms drive the evolution of vehicles in becoming an intelligent and electronic consumer products. This paper investigates enabling digital twins in vehicular edge computing (DT-VEC) via cooperative sensing and uploading, and makes the first attempt to achieve the quality-cost tradeoff in DT-VEC. First, a DT-VEC architecture is presented, where the heterogeneous information can be sensed by vehicles and uploaded to the edge node via vehicle-to-infrastructure (V2I) communications. The digital twins are modeled based on the sensed information, which are utilized to from the logical view to reflect the real-time status of the physical vehicular environment. Second, we derive the cooperative sensing model and the V2I uploading model by considering the timeliness and consistency of digital twins, and the redundancy, sensing cost, and transmission cost. On this basis, a bi-objective problem is formulated to maximize the system quality and minimize the system cost. Third, we propose a solution based on multi-agent multi-objective (MAMO) deep reinforcement learning, where a dueling critic network is proposed to evaluate the agent action based on the value of state and the advantage of action. Finally, we give a comprehensive performance evaluation, demonstrating the superiority of MAMO.