P-DRR: PPO-Based Efficient Dynamic Resource Reallocation Scheme in Industrial Internet of Things

The emergence of edge computing (EC) and artificial intelligence (AI) is driving the rapid growth of industrial internet of things (IIoT). However, few works comprehensively consider the impact of resource reallocation and number of reallocation on the system delay in dynamic industrial scenarios with time-varying geographic location characteristics. This paper takes the dynamic resource reallocation problem between the physical layer and edge layer within a time-varying factory scenario into account, proposes a reallocation-decision variable and reduces the computational stress on edge nodes caused by frequent reallocation. An optimization problem with the objective of minimizing the system average delay is established and a proximal policy optimization (PPO) based dynamic resource reallocation (P-DRR) algorithm is proposed for the problem solving. Experimental results show that P-DRR algorithm can effectively reduce average delay compared to the baseline algorithms without causing large computational pressure on edge nodes.