Mobile Crowd Sensing via Online Communities: Incentive Mechanisms for Multiple Cooperative Tasks

Mobile crowd sensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Many incentive mechanisms for mobile crowd sensing have been proposed. However, none of them takes into consideration the cooperative compatibility of users for multiple cooperative tasks. In this paper, we design truthful incentive mechanisms to minimize the social cost such that each of the cooperative tasks can be completed by a group of compatible users. We consider that the mobile crowd sensing is launched in an online community. We study two bid models and formulated the Social Optimization Compatible User Selection (SOCUS) problem for each model. We also define three compatibility models and use real-life relationships from social networks to model the compatibility relationships. We design two reverse auction based incentive mechanisms, MCT-M and MCT-S. Both of them consist of two steps: compatible user grouping and reverse auction. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality and truthfulness. In addition, MCT-M can output the optimal solution.