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IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility

  • Autori: Restuccia, Francesco; Ferraro, Pierluca; Silvestri, Simone; Das, Sajal K.; Lo Re, Giuseppe
  • Anno di pubblicazione: 2018
  • Tipologia: Articolo in rivista (Articolo in rivista)
  • OA Link:


Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile crowdsensing system, it is paramount to incentivize smartphone users to provide sensing services in a timely and reliable manner. 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, which is often uncertain. For this reason, in this paper we propose IncentMe, a framework that solves this fundamental problem 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%.