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Synergistic Based Social Incentive Mechanism in Mobile Crowdsensing

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Wireless Algorithms, Systems, and Applications (WASA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10874))

Abstract

Most Mobile Crowdsensing (MCS) applications are large-scale and the quality of sensing for sensing tasks is interdependent. Previous incentive mechanisms have focused on quantifying participants’ contribution to the quality of sensing and provide incentives directly to them, which are not applicable to the above scenario. To tackle this problem, in this article, we introduce a novel approach for MCS, called the synergistic based social incentive mechanism. The basic idea is to leverage the social ties among participants to promote cooperation. To maximize the utility of service provider, a moral hazard model is used to analyze the optimal contract between service providers and mobile users in the case of asymmetric information. Experiments show that the synergistic based social incentive mechanism can give users continuous encouragement while maximizing the utility of the principal.

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Author information

Authors and Affiliations

  1. School of Software, Central South University, Changsha, China

    Can Liu & Feng Zeng

  2. Department of Computer Science, New York Institute of Technology, New York, USA

    Wenjia Li

Authors
  1. Can Liu
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  2. Feng Zeng
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  3. Wenjia Li
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Corresponding authors

Correspondence to Feng Zeng or Wenjia Li .

Editor information

Editors and Affiliations

  1. University of South Florida, Tampa, Florida, USA

    Sriram Chellappan

  2. Virginia Commonwealth University, Richmond, Virginia, USA

    Wei Cheng

  3. Georgia State University, Atlanta, Georgia, USA

    Wei Li

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Liu, C., Zeng, F., Li, W. (2018). Synergistic Based Social Incentive Mechanism in Mobile Crowdsensing. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_65

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  • DOI: https://doi.org/10.1007/978-3-319-94268-1_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94267-4

  • Online ISBN: 978-3-319-94268-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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