Industrial Deterministic Computation and Networking Resource Scheduling via Deep Reinforcement Learning

In this paper, a dueling double deep Q network (D3QN)-based resource scheduling algorithm is proposed for industrial Internet of things (IoT) to achieve the flexible adaptation of network resources. In the considered network scenario, the time-sensitive networking (TSN)-fifth generation (TSN-5G) network architecture, primarily composed of TSN switches and 5G base stations, is designed accordingly. Simulation results show that when network resources are limited, the D3QN-based resource scheduling algorithm can significantly improve the efficiency of task allocation, making it an ideal solution for reducing latency and optimizing resource utilization in industrial IoT.