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The recent proliferation of human-carried mobile devices has given rise tomobile crowd sensing (MCS) systems that outsource sensory data collection tothe public crowd. In order to identify truthful values from (crowd) workers'noisy or even conflicting sensory data, truth discovery algorithms, whichjointly estimate workers' data quality and the underlying truths throughquality-aware data aggregation, have drawn significant attention. However, thepower of these algorithms could not be fully unleashed in MCS systems, unlessworkers' strategic reduction of their sensing effort is properly tackled. Toaddress this issue, in this paper, we propose a payment mechanism, namedTheseus, that deals with workers' such strategic behavior, and incentivizeshigh-effort sensing from workers. We ensure that, at the Bayesian NashEquilibrium of the non-cooperative game induced by Theseus, all participatingworkers will spend their maximum possible effort on sensing, which improvestheir data quality. As a result, the aggregated results calculated subsequentlyby truth discovery algorithms based on workers' data will be highly accurate.Additionally, Theseus bears other desirable properties, including individualrationality and budget feasibility. We validate the desirable properties ofTheseus through theoretical analysis, as well as extensive simulations.

Theseus: Incentivizing Truth Discovery in Mobile Crowd Sensing Systems