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

Francesco Restuccia, Pierluca Ferraro, Simone Silvestri, Sajal K. Das, Giuseppe Lo Re

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


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.

Original languageEnglish
Article number8425784
Pages (from-to)1571-1584
Number of pages14
JournalIEEE Transactions on Mobile Computing
Issue number7
StatePublished - Jul 1 2019

Bibliographical note

Publisher Copyright:
© 2002-2012 IEEE.


  • Participatory
  • auction
  • crowdsensing
  • game theory
  • incentive
  • mechanism
  • optimization
  • sensing
  • smartphone

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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