False-name-proof mechanism for time window coverage tasks in mobile crowdsensing

Mobile crowdsensing has been regarded as an efficient paradigm for performing large-scale sensing tasks. In this paper, we consider a specific scenario, where the crowdsensing platform needs to collect the sensing data in a requested time window (RTW), and mobile users would bid for their sensing time windows. This process could be modeled as a reverse auction. In this context, the Vickrey-Clark-Groves (VCG) mechanism becomes a generic auction mechanism that uniquely guarantees both truthfulness and efficiency, but it is vulnerable to false-name bidding and generates high overpayment for the platform. Thus in this paper, we design the core-selecting mechanism to solve VCG’s vulnerability and improve the revenue. We demonstrate that our proposed mechanism achieves the properties of RTW feasibility, efficiency, individual rationality, and false-name-proofness. Besides, to minimize the incentives of users to deviate from truthful-telling, we adopt a VCG-nearest payment rule and propose an efficient algorithm called CCG-TWC. Our extensive simulation results show that the core-selecting mechanism could reduce VCG’s overpayment by about 10%.