Combining Lyapunov Optimization With Actor-Critic Networks for Privacy-Aware IIoT Computation Offloading

Opportunistic computation offloading is an effective way to improve the computing performance of Industrial Internet of Things (IIoT) devices. However, as more and more computing tasks are being offloaded to mobile-edge computing (MEC) servers for processing, it can lead to IIoT privacy and security issues, such as personal usage habits. In this paper, we aim to design a Lyapunov-based privacy-aware framework that defines the amount of IIoT user privacy and designs a “reduced amount of privacy” mechanism. We first define the cumulative privacy amount for each IIoT user and trigger the privacy protection mechanism when the cumulative privacy amount exceeds the set privacy threshold. The offloading data generated by the IIoT user is then transferred to local processing, and finally, the cumulative privacy amount of the IIoT user is reduced. This model ensures that the cumulative privacy of all IIoT users remains stable. We further combine the advantages of Lyapunov optimization and actor-critic networks to address the problem of how to make the model learn the optimal policy and maintain the minimum energy consumption in the long run. Especially, this framework integrates model-based optimization and model-free actor-critic networks to handle the offloading problem with very low computational complexity, and Lyapunov optimization ensures that this framework minimizes energy consumption while stabilizing the data queue. It is demonstrated through experimental simulation results that the proposed scheme can maintain data queue stability and minimize energy consumption under strict security.