Reverse Auction Based Incentive Mechanism for Location-Aware Sensing in Mobile Crowd Sensing

In mobile crowd sensing (MCS), incentive mechanism is one of the most critical issues, and plays an important role on ensuring the quantity of participants and the coverage rate of sensing task. In this paper, we tackle the problem of stimulating enough smartphone users to join in MCS activities with their smartphones. Moreover, the scenario that the selected users drop out of the sensing tasks with random probability during their sensing processes is also taken into account. Therefore, we propose a novel incentive mechanism called IMRAL - an Incentive Mechanism based on Reverse Auction for Location-aware sensing. IMRAL aims to enhance the participants' enthusiasm by maximizing their expected profits. It consists of two parts: winner selection algorithm and payment determination scheme. In the first part, we formulate a winner selection problem by considering the service coverage of mobile users. Due to the NP-hardness of the problem, we introduce a task-centric method to determine the winning bids with polynomial time complexity. The second part is a payment scheme, which determines the payment to winners by a time proportional share rule to ensure the truthful of IMRAL and consider the effects of the randomness of the selected users, and the winners can obtain the maximum utility. Through rigid theoretical analysis, we demonstrate that the proposed mechanism satisfies the properties of computational efficiency, individual rationality, budget feasibility and truthfulness. Simulation results show that, compared with TRAC (truthful auction for location-aware collaborative sensing in mobile crowdsourcing) and IMC-SS (incentive mechanism for crowdsourcing in the single-requester single-bid-model), the IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio.