A Privacy-aware Stackelberg Game Approach for Joint Pricing, Investment, Computation Offloading and Resource Allocation in MEC-enabled Smart Cities

Mobile edge computing (MEC), which is regarded as a promising paradigm, is proposed to provide smart cities that are supported by the Internet of Things (IoT) with low processing latency at the edge of the network, by offloading latency-critical tasks from MDs to edge service providers (ESPs). In this paper, we study the interaction between ESPs and MDs by formulating a Stackelberg game model, to optimize the strategies of computation offloading and resource allocation of the MDs, and the prices and investment spending on the privacy level of ESPs. Additionally, the social effect of MDs on privacy concerns is incorporated to study the impacts on the payoffs of players. We utilize distributed Alternating Direction Method of Multipliers (ADMM) algorithm to address the Stackelberg equilibrium problem in a distributed manner. Finally, numerical results illustrate that our proposed scheme can jointly achieve the maximum profits of ESPs and utilities of MDs.