Scalable Energy-Efficiency Optimization in MEC-Enabled IIoT: A Deep Reinforcement Learning Perspective

This paper investigates the maximization of average energy efficiency (AEE) in MEC-enabled Industrial Internet of Things (IIoT) networks through a cache-based association strategy (CBAS). The system comprises autonomous mobile robots (AMRs), small-cell base stations (SBSs), and macro-cell base stations (MBSs) operating under line-of-sight obstructions, fading, and interference. To jointly optimize transmit power, SBS cache size, MBS cache size, and SBS intensity, parameters critical to CBAS efficiency, we propose a deep Q-network (DQN)-based approach that circumvents the computational burden of solving NP-hard formulations through exhaustive search. In addition to performance benchmarking, we enhance explainability by analyzing the learned state embeddings using t-SNE. Simulation results show that our approach achieves near-optimal AEE with significantly lower overhead, making it scalable and suitable for real-time IIoT deployments.