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QuaCentive: a quality-aware incentive mechanism in mobile crowdsourced sensing (MCS)

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Abstract

Today’s smartphones with a rich set of cheap powerful embedded sensors can offer a variety of novel and efficient ways to opportunistically collect data, and enable numerous mobile crowdsourced sensing (MCS) applications. Basically, incentive is one of fundamental issues in MCS. Through appropriately integrating three popular incentive methods: reverse auction, reputation and gamification, this paper proposes a quality-aware incentive framework for MCS, QuaCentive, which, pertaining to all components in MCS, can motivate crowd to provide high-quality sensed contents, stimulate crowdsourcers to give truthful feedback about quality of sensed contents, and make platform profitable. Specifically, first, we utilize the reverse auction and reputation mechanisms to incentivize crowd to truthfully bid for sensing tasks, and then provide high-quality sensed contents. Second, in to encourage crowdsourcers to provide truthful feedbacks about quality of sensed data, in QuaCentive, the verification of those feedbacks are crowdsourced in gamification way. Finally, we theoretically illustrate that QuaCentive satisfies the following properties: individual rationality, cost-truthfulness for crowd, feedback-truthfulness for crowdsourcers, platform profitability.

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Acknowledgments

The work was sponsored by the NSFC Grant 61171092, JiangSu Educational Bureau Project 14KJA510004, Huawei Innovation Research Program, and Prospective Research Project on Future Networks (JiangSu Future Networks Innovation Institute).

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Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Yufeng Wang & Xueyu Jia

  2. Waseda University, Tokyo, Japan

    Qun Jin

  3. Hosei University, Tokyo, Japan

    Jianhua Ma

Authors
  1. Yufeng Wang
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  2. Xueyu Jia
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  3. Qun Jin
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  4. Jianhua Ma
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Correspondence to Yufeng Wang.

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Wang, Y., Jia, X., Jin, Q. et al. QuaCentive: a quality-aware incentive mechanism in mobile crowdsourced sensing (MCS). J Supercomput 72, 2924–2941 (2016). https://doi.org/10.1007/s11227-015-1395-y

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  • DOI: https://doi.org/10.1007/s11227-015-1395-y

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