Exploring Accurate Monitoring for Massive IIoT: A Digital Twin-Enabled Hierarchical Scheme

The expansion of the Industrial Internet of Things (IIoT), driven by informatization and intelligence, has made real-time monitoring for ensuring system safety and efficient production more critical than ever. However, in extreme industrial environments, harsh communication conditions and limited computing power impede reliable data transmission and processing, thus posing significant challenges to precise and continuous monitoring for massive IIoT. To this end, this paper proposes a digital twin (DT)-enabled hierarchical monitoring scheme by fully considering communication, computing, and control (3C) collaboration. Specifically, we first propose a DT-enabled 3C collaboration architecture, which builds an accurate digital representation of physical entities, and then completes the missing state based on shared-specific features to evolve continuously across the device lifecycle. Next, we design a multi-agent reinforcement learning (MARL)-based collaborative monitoring method, which formulates the joint optimization problem of compression rate and region assignment for monitoring nodes according to 3C performance. Then, we propose a mask state-assisted MARL scheduling method to refine the mask state space, take advantage of MARL’s distributed decision-making and centralized evaluation, and ensure timely monitoring of massive IIoT. Finally, we build a mine industry simulation platform and verify the effectiveness of the proposed method. The numerical results demonstrate a marked improvement in monitoring utility over traditional methods.