Energy-Efficient and Load-Balanced Digital Twin Deployment In DITEN-Empowered IIoT

Digital twin (DT) is a virtual representation of physical entities or processes that enables real-time monitoring, analysis, and optimization in the field of intelligent manufacturing. By simulating and optimizing production processes, DT technology could predict and prevent equipment failures, and enhance the efficiency and quality of industrial parts production. However, effectively deploying DTs into Digital Twin Empowered Edge Network (DITEN) in complex Industrial Internet of Things (IIoT) environments remains a significant challenge. Particularly in scenarios with numerous physical entities within IIoT, optimizing the deployment of DTs on edge nodes to minimize interaction latency with physical entities, as well as reducing workload and energy consumption on edge nodes, becomes crucial. To address this challenge, this paper first designs a Bi-Layer DT architecture for IIoT. Furthermore, an Intrinsic Curiosity Module-based Multi-Agent Proximal Policy Optimization algorithm (ICM-MAPPO) is proposed to solve the optimal deployment problem for DTs in DITEN-Empowered IIoT. Numerical experiments validate the effectiveness of the ICM-MAPPO algorithm in minimizing deployment latency and interaction latency while achieving load balance and reducing energy consumption.