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