Incentive Mechanism for Socially-Aware Mobile Crowdsensing: A Bayesian Stackelberg Game

Abstract

Incentive mechanisms are pivotal in encouraging mobile users to participate to contribute their sensing information. However, most studies on incentive mechanisms merely considered individual behaviors of the users rather than their interdependency. The interdependent behaviors of the users are common as they originate from the social network effects that exist in the underlying mobile social domain. For example, a user from a crowdsensing-based traffic condition application can obtain a more accurate traffic mapping if other users share their road traffic information. Moreover, the incomplete information problem is also a critical but open issue in the real-life applications of crowdsensing. To address these issues, we propose a novel incentive mechanism considering both the social network effects and the incomplete information situation. In particular, we develop a Bayesian Stackelberg game, and study the participation strategies of users as well as the incentive mechanism through backward induction method. We then analytically prove that the Bayesian Stackelberg equilibrium is uniquely determined. Moreover, the numerical results are provided to evaluate the proposed socially-aware incentive mechanisms.