A truthful incentive mechanism for online recruitment in mobile crowd sensing system

Xiao Chen, Min Liu*, Yaqin Zhou, Zhongcheng Li, Shuang Chen, Xiangnan He

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
8 Downloads (Pure)


We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare.

Original languageEnglish
Article number79
Pages (from-to)1-17
Number of pages17
Issue number1
Publication statusPublished - Jan 2017
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • mobile crowd sensing system
  • online incentive
  • truthful mechanism
  • single-parameter mechanism


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