Learning-Based Joint Optimization of Energy Delay and Privacy in Multiple-User Edge-Cloud Collaboration MEC Systems

Abstract

The emergence of mobile edge computing (MEC) enables resource-limited user devices to run computation-intensive applications with the aid of edge server, but the untrustworthiness of the third-party edge server raises the leakage risk of users’ privacy. In this article, we consider an edge-cloud collaboration (ECC) scenario, consisting of multiple user devices with energy harvesting component, one edge server and one cloud server, and we concern about the issues of time latency, energy consumption, and privacy level of user devices in the process of task offloading. Specifically, we formulate the tradeoff between offloading cost and privacy level as a jointly optimization problem, and further model it as a Markov decision process (MDP). We then propose a privacy-preserving task offloading building upon the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DQN), which enables user devices to make the optimal offloading decision to decrease delay, reduce energy cost, and enhance privacy level. Extensive simulation results prove that our method can reduce the offloading cost whilst boosting the privacy level of user devices compared to conventional reinforcement learning (RL) algorithm and two baselines.