Digital Twin and Meta RL Empowered Fast-Adaptation of Joint User Scheduling and Task Offloading for Mobile Industrial IoT

The industrial Internet of Things (IoT) system is integrated with the emerging artificial intelligence (AI) paradigms to empower industrial automation and self-evolving capabilities. AI-driven resource allocation across cyber-physical domains for mobile industrial IoT must consider its fundamental requirements and key characteristics such as high reliability, low latency, and environmental dynamics. The challenge is twofold. Industrial systems are fault-sensitive, which makes them intolerable of trial-and-error-based learning and optimization approaches. In addition, learning models cannot adapt to changing industrial IoT environment with dynamic communication noise and machinery disturbances. In this paper, we propose joint optimization for the nonorthogonal multiple access (NOMA) and multi-tier hybrid cloud-edge computing empowered industrial IoT that results in improved utilization of communication and computing resources. Second, we establish the fine-grained digital twin for industrial IoT (DT-IIoT) to simulate the changing industrial environment to support trial-and-error-based safe learning. Third, we leverage meta reinforcement learning (meta RL) to improve the generalization and fast adaptation of the learning models for DT-IIoT. Finally, the feasibility and efficiency of these schemes are evaluated through extensive experiments.