A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Abstract

Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.