A Pricing Approach Toward Incentive Mechanisms for Participant Mobile Crowdsensing in Edge Computing

Owing to the acceleration of urbanization and the rapid development of mobile Internet, mobile crowd sensing (MCS) has been recognized as a promising method to acquire massive volume of data. However, due to the massive perception data in participatory MCS system, the data privacy of mobile users and the response speed of data processing in cloud platform are hard to guarantee. Stimulating the enthusiasm of participants could be challenging at the same time. In this paper, we first propose a three-layer MCS architecture which introduces edge servers to process raw data, protects users' privacy and improve response time. In order to maximize social welfare, we consider two-stage game in three-layer MCS architecture. Then, we formulate a Markov decision process (MDP)-based social welfare maximization model and investigate a convex optimization pricing problem in the proposed three-layer architecture. Combined with the market economy model, the problem could be considered as a Walrasian equilibrium problem according to market exchange theory. We propose a pricing approach toward incentive mechanisms based on Lagrange multiplier method, dual decomposition and subgradient iterative method. Finally, we derive the experimental data from real-world dataset and extensive simulations demonstrate the performance of our proposed method.