Joint Optimization of Energy Conservation and Privacy Preservation for Intelligent Task Offloading in MEC-Enabled Smart Cities

Abstract

Internet of Thing-based mobile devices (MDs) make the vision of smart cities become reality. Nevertheless, MDs are subjected to some shortcomings and cannot effectively handle the explosive growth of applications. Fortunately, the performance of MDs can be augmented by offloading latency-critical tasks to edge service providers (ESPs). Nevertheless, there is a competitive relationship among MDs as the resources of ESPs are limited. Moreover, there is a certain risk of privacy leakage during computation offloading. In view of this, we study the computation offloading and resource allocation which is formulated as a Stackelberg game with the aims of maximizing the utilities of MDs and the profits of ESPs under the consideration of energy efficiency by optimizing the strategies of prices, computation offloading and the privacy investment. Additionally, both the cooperation scenario and non-cooperation among ESPs are investigated. Besides, the social effect of MDs on privacy concerns is also considered. Technically, the Stackelberg equilibrium is solved by utilizing the distributed Alternating Direction Method of Multipliers algorithm in a distributed manner. Numerous simulation results have illustrated that the method is effective and also has fast convergence and high scalability.