IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility
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%.
F. Restuccia et al., "IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility," IEEE Transactions on Mobile Computing, vol. 18, no. 7, pp. 1571-1584, Institute of Electrical and Electronics Engineers (IEEE), Jul 2019.
The definitive version is available at https://doi.org/10.1109/TMC.2018.2863288
Keywords and Phrases
Computation theory; Computer games; Game theory; Job analysis; Machine design; Mechanisms; Mobile computing; Monitoring; Navigation; Optimization; Sensors; Taxicabs; Auction; Crowd sensing; Incentive; Participatory; Roads; Sensing; Task analysis; Smartphones; Crowdsensing; Sensors; Smart phones
International Standard Serial Number (ISSN)
Article - Journal
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