Mobile crowdsensing leverages mobile devices (e.g., smart phones) and humanmobility for pervasive information exploration and collection; it has beendeemed as a promising paradigm that will revolutionize various research andapplication domains. Unfortunately, the practicality of mobile crowdsensing canbe crippled due to the lack of incentive mechanisms that stimulate humanparticipation. In this paper, we study incentive mechanisms for a novel MobileCrowdsensing Scheduling (MCS) problem, where a mobile crowdsensing applicationowner announces a set of sensing tasks, then human users (carrying mobiledevices) compete for the tasks based on their respective sensing costs andavailable time periods, and finally the owner schedules as well as pays theusers to maximize its own sensing revenue under a certain budget. We prove thatthe MCS problem is NP-hard and propose polynomial-time approximation mechanismsfor it. We also show that our approximation mechanisms (including both offlineand online versions) achieve desirable game-theoretic properties, namelytruthfulness and individual rationality, as well as O(1) performance ratios.Finally, we conduct extensive simulations to demonstrate the correctness andeffectiveness of our approach.
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