A Truthful Incentive Mechanism for Mobile Crowdsourcing

In a mobile crowdsourcing system, the platform utilizes ubiquitous smartphones to perform sensing tasks. For a successful mobile crowdsourcing application, the consideration of the heterogeneity of quality of sensing from different users as well as proper incentive mechanism to motivate users to contribute to the system are essential. In this thesis, we introduce quality of sensing into incentive mechanism design. Under a budget constraint, the platform aims to maximize the valuation of the performed tasks, which depends on the quality of sensing of the users. We propose ABSee, an auction-based budget feasible mechanism, which consists of a winning bid selection rule and a payment determination rule. We obtain the approximation ratio of ABSee, which significantly improves the approximation ratio of existing budget feasible mechanisms. ABSee also satisfies the properties of computational efficiency, truthfulness, individual rationality, and budget feasibility. Extensive simulation results show that ABSee provides a higher valuation to the platform when compared with an existing mechanism in the literature.