Auction Meets Queuing: Information-Driven Data Purchasing in Stochastic Mobile Crowd Sensing
Chongyu Zhou, Chen‐Khong Tham, Mehul Motani · 2017
The pervasiveness of mobile phones and the increasing sensing capabilities of their built-in sensors have made mobile crowd sensing (MCS) a promising approach for large-scale event detection and collective knowledge formation. In a typical MCS system, the crowdsourcer purchases sensing data from some mobile phone users (i.e., contributors) and sells it to consumers for revenue. This kind of sensing data exchange has its unique challenges in practical MCS systems. On one hand, the crowdsourcer wants to maximize the information utility to get the most revenue under heterogeneous requests from the consumers while offering incentives to strategic contributors. On the other hand, the contributors need to make optimal real-time sensing and data selling decisions by considering their real-time sensing cost and quality of information, in order to maximize their own profit. In this paper, we propose a novel Information-driven Data Auction (IDA) scheme for data exchange in practical stochastic MCS systems, which offers optimal strategies for both the crowdsourcer and the contributors. By applying stochastic Lyapunov optimization and mechanism design theory, IDA is able to achieve a near-optimal time-averaged system-wide utility, while offering incentives to the contributors. Moreover, IDA achieves favourable economic properties including truthfulness, individual rationality, and budget balance. We demonstrate the efficacy of IDA through rigorous theoretical analysis and comprehensive simulations.