A Reverse Auction-Based Incentive Mechanism for Mobile Crowdsensing

Incentive mechanism has been an important research direction in mobile crowdsensing. An effective incentive mechanism is critical to ensure the adequate number of participants/workers by providing them proper rewards. However, existing incentive mechanisms lack consideration on potential contributions of individual workers when recruiting new workers and retaining existing workers in the system. In this article, we propose a reverse auction-based incentive mechanism (RAIN), which considers participants' potential contributions when recruiting new workers, performing reverse auctions, and retaining existing workers. The design objective is to optimize the worker composition in the system while reducing the system cost. In RAIN, the potential contribution of a user to the system is measured as the degree at which the user's joining or staying in the system can remedy the inadequacy of workers for task auction/execution at the frequently visited locations of the user. We present design details of RAIN which includes selective worker recruitment, reverse auction based on biased bids, and selective retaining of auction losers, all based on individual users' potential contributions to the system. Extensive simulation results show that RAIN can effectively optimize the worker composition in a system and also effectively reduce the system cost.