Optimal Distributed Auction for Mobile Crowd Sensing.

Mobile crowd sensing, also called crowd sensing over smartphones, has been an appealing paradigm for collecting sensory data over a vast urban area, due to advantages of low deployment cost and widely spatial coverage of geographically distributed smartphones or other smart devices. In the paper, we focus on a nontrivial problem of making an agreement between crowdsourcers and smartphone users to find the most efficient assignment of sensing tasks to smartphone users. However, there exist several technical challenges such as the incentive issue to encourage participation of smartphone users, preserving private information and distributed implementation in practice. Existing approaches usually have several limitations, e.g. the absence of proper incentives, the assumption of a centralized auctioneer or platform. To this end, we propose a distributed auction framework that explicitly models the interaction between crowdsourcers and smartphone users, achieving the optimal social profit and providing proper incentives to entities without disclosing their privacy as well. We demonstrate that the proposed distributed auction algorithm satisfies a lot of good properties, including optimality of social profit, computation efficiency, convergence, individual rationality through both solid theoretical analysis and extensive experiments.