Crowdsensing incentive mechanisms for mobile systems with finite precisions

Abstract

Mobile devices with sensing capabilities have enabled a new paradigm of mobile crowdsensing with a broad range of applications. A major challenge in achieving a stable crowdsensing system in a large scale is the incentive issue for each participant. Proper incentive mechanisms are necessary to keep the crowdsensing working. However, most existing incentive mechanisms for crowdsensing assume the system has infinite precisions in opposite of the fact that digital devices round the results to discrete floating numbers. In this paper, we show that finite precisions and rounding can make the existing crowdsensing incentive mechanisms invalid. To address this problem, we design an incentive mechanism for discrete crowdsensing that achieves Perfect Bayesian Equilibrium (PBE) and maximizes platform utility. Our mechanism is efficient since its computational complexity is linear to the number of users. We also consider the case that different participants have diverse precisions, and design another incentive mechanism to achieve mixed PBE and maximize platform utility in the statistical sense. Extensive simulations verify our mechanisms are efficient, individual-rational and system-optimal.