IMRSG: Incentive Mechanism Based on Rubinstein-Starr Game for Mobile CrowdSensing

Exploiting mobile crowdsensing to conduct data collection and analysis has become an emerging trend in IoT computing models. However, in practice, mobile users in crowdsensing networks are unwilling to participate in perceptual tasks without effective stimulation due to limited resources and strategic selfishness. To tackle the problem, a new incentive mechanism is proposed by adopting game theory in this article. Specifically, the mobile users' preference is considered comprehensively and their energy is changed dynamically through the scheme. Furthermore, the users' preference and energy changes are exploited as equation factors between subsequent users and the platform. To motivate users, we introduce the concept of virtual currency and set a price function that rewards users for participating in sensing tasks in the form of virtual currency. To fit into practical scenarios, we map task interactions to a Rubinstein-Starr bargaining game between a user and sensing platforms based on a fully informative turn-based bargaining model. Through multiple rounds of games between users and crowdsensing platforms, we prove that there is a unique Nash Equilibrium point in the sub-game process of both parties. The analysis based on these models lays a theoretical foundation on the incentive process of mobile crowdsensing. The soundness of modeling and the accuracy of analysis are verified through extensive simulation, which also guides the design of more sophisticated incentive schemes.