Incentive Mechanism for Mobile Crowdsensing with Social-Aware Users: A Two-Stage Stackelberg Game
Hai Bo Yu, Peng Li, Qin Xu, Lei Nie, Haizhou Bao, Qin Liu · 2024
In mobile crowdsensing, the quality and quantity of data play an important role in the design of incentive mechanisms. However, existing work seldom considers the impact of social relationships among users on the quality and quantity of data. In this paper, to effectively recruit mobile users and improve data quality, we design a social-aware incentive mechanism (SIM) based on a two-stage Stackelberg game that considers social relationships. First, we consider the utility of both the users and the service provider, designing distinct utility functions for each. The utility function for the user considers personal utility, social utility, and historical reputation. Second, we model the payment problem as a two-stage game between the two parties, analyze the optimal incentives for both the service provider and the users using backward induction, and then derive the optimal strategy groups to maximize the utility of the two parties. Through theoretical analysis, we prove the unique existence of Stackelberg equilibrium, resulting in a multi-win situation. Numerical results confirm that the introduction of social networks significantly increases task participation and rewards for users, while also helping service providers gain greater revenue.