An online mechanism for task allocation and pricing in crowd sensing systems.

In crowd sensing systems, mobile users provide requesters with access to the resources of their mobile devices, such as the core processor, memory, and camera, to execute tasks in return for monetary payment. Existing works consider the offline setting where information about mobile users and requesters is publicly known. However, this assumption does not hold for crowd sensing systems in the real world, where mobile users and requesters can arrive and leave at any time. We address the problem of online task allocation and pricing in crowd sensing systems without making any assumptions about the future information of mobile users and requesters. We formulate this problem in an auction-based online setting and propose a feasible and online double auction mechanism. The proposed online mechanism considers one-to-many mapping, which permits multiple mobile devices to work together to complete the same task at different times in order to improve resource utilization. In addition, we show that the proposed mechanism maintains individual rationality, budget-balance, and computational tractability. Furthermore, we analyze the approximation ratio of the proposed approximation algorithm. The experimental results show that a user cannot increase her utility by untruthful declaration, and the proposed mechanism brings more economic benefit for the auctioneer and stimulates users to join the system. [ABSTRACT FROM AUTHOR]