6533b873fe1ef96bd12d4489
RESEARCH PRODUCT
IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility
Pierluca FerraroSajal K. DasSimone SilvestriGiuseppe Lo ReFrancesco Restucciasubject
FOS: Computer and information sciencesOptimizationMonitoringComputer Networks and CommunicationsComputer scienceDistributed computingMobile computingCrowdsensing02 engineering and technologyComputer Science - Networking and Internet ArchitectureReverse auctionSmart phoneCrowdsensingGame Theory0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringSensorNetworking and Internet Architecture (cs.NI)Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniMechanism designMobile computing020206 networking & telecommunicationsAuctionNavigationCore (game theory)RoadComputer Networks and CommunicationSensingTask analysisTask analysiParticipatoryState (computer science)MechanismSmartphoneWireless sensor networkIncentiveSoftwaredescription
Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 |