Incentive Mechanisms for Discretized Mobile Crowdsensings

In crowdsensing to mobile phones, each user needs incentives to participate. Mobile devices with sensing capabilities have enabled a new paradigm of mobile crowdsensing with a broad range of applications. A major challenge in achieving stable crowdsensing on a large scale is the incentive issue. Proper incentive mechanisms are necessary to keep the crowdsensing working. However, most existing incentive mechanisms for crowdsensing assume the system admit continuous strategies like sensing time in opposite of the fact that many digital devices and crowdsensing models only admit discretized strategies. In this paper, we show that discretization, like rounding method, can make the existing crowdsensing incentive mechanisms invalid. To address this problem, we design the incentive mechanism for discrete crowdsensing in which each user has a uniform sensing subtask length. We rigorously show that our mechanism can achieve perfect Bayesian equilibrium (PBE) and maximize the platform utility. Our algorithm is efficient since its complexity is linear to the number of users. We also consider the cases in which the users have diverse subtask lengths, and propose another two incentive mechanisms to achieve PBEs and maximize platform utility. Extensive simulations verify our mechanisms are efficient, individual-rational, and system-optimal.