Quality-Aware Incentive Mechanism for Social Mobile Crowd Sensing

Abstract

The mobile crowd sensing perception paradigm relies on the participation of generous users, and an appropriate incentive mechanism can promote the participation of more users. Moreover, social relationships among users can promote users’ willingness to complete the sensing tasks, which in turn boosts the collection of high-quality data for the platform. In this letter, we study the problem of designing a suitable incentive mechanism combined with data quality under social influence. Specifically, we divide the design of the incentive mechanism into two stages: participant selection considering social influence; reward payment considering quality measurement. We use the KKT system to get an optimal time of users in stage I. Furthermore, the reward payment stage can be equivalently transformed into a mixed-integer nonlinear problem, and we devise a heuristic algorithm HEAL to solve the problem. Finally, we conduct certain numerical comparative simulation experiments to evaluate the effectiveness and correctness of HEAL.