Dual-Q network deep reinforcement learning-based computation offloading method for industrial internet of things

Abstract

In the development of industrial internet of things (IIoT), the application of mobile edge computing (MEC) has significantly enhanced the efficiency of task computation offloading. However, the risk of data privacy leakage persists during the computation offloading process. Considering the diversity of task data sensitivity and the variability in server security protection capabilities, this paper proposes a method for assessing privacy satisfaction. To ensure offloading efficiency while effectively enhancing privacy security, we have proposed an IIoT cloud-edge-device computation offloading algorithm based on dual-Q network deep reinforcement learning, named D2PCO, to optimize the computation offloading process in IIoT tasks. The incorporation of the dual-Q network notably enhances the algorithm’s learning ability and efficiency in dealing with complex decision-making problems. Experimental results show that the proposed D2PCO algorithm significantly improves user privacy satisfaction while ensuring low delay. Compared with MA3MCO, DDPG, shortest distance priority, and random scheduling algorithms, it reduces the average offloading delay by 4.15%, 9.98%, 13.2%, and 26.47% and increases privacy satisfaction by 0.8%, 4.26%, 10.15%, and 30.30%, respectively.