Pay as How You Behave: A Truthful Incentive Mechanism for Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) is widely applied in large-scale distributed networks for collecting sensing data from workers. In an MCS system, workers are recruited to complete tasks for data requesters, and they will get profits. Accordingly, how to establish an effective incentive mechanism has become an important issue to consider. Since workers are naturally selfish, they try to maximize individual benefits while minimize costs. In this article, we propose a truthful incentive mechanism which pays for the workers by the workers’ performance in the task just completed and the reputation. For each worker, through the future prediction function, we get the reputation of the worker by utilizing the previous performances. In the proposed scheme, partial payment for the workers is distributed depending on workers’ reputation. The final payment is based on punishments and rewards according to the performances. Moreover, data accuracy and response time are introduced to evaluate the worker performance in the task. It can be demonstrated that the mechanism provides continuous incentives to workers compared to the single ex-ante and ex-post pricing schemes. The experimental results show that our mechanism is effective.