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An Incentive Mechanism Based on Lottery for Data Quality in Mobile Crowdsensing

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  • Xinyu Hu

    (Tiangong Innovation School, Tiangong University, Tianjin 300387, China)

  • Shengjie Sun

    (School of Computer Science and Engineering, Central South University, Changsha 410075, China)

  • Zhi Lv

    (School of Computer Science and Engineering, Central South University, Changsha 410075, China)

  • Jiaqi Liu

    (School of Computer Science and Engineering, Central South University, Changsha 410075, China)

Abstract

Mobile Crowdsensing (MCS) leverages smart devices within sensing networks to gather data. Given that data collection demands specific resources, such as device power and network bandwidth, many users are reluctant to participate in MCS. Therefore, it is essential to design an effective incentive mechanism to encourage user participation and ensure the provision of high-quality data. Currently, most incentive mechanisms compensate users through monetary rewards, which often leads to users requiring higher prices to maintain their own profits. This, in turn, results in a limited number of users being selected due to platform budget constraints. To address this issue, we propose a lottery-based incentive mechanism. This mechanism analyzes the users’ bids to design a winning probability and budget allocation model, incentivizing users to lower their pricing and enhance data quality. Within a specific budget, the platform can engage more users in tasks and obtain higher-quality data. Compared to the ABSEE mechanism and the BBOM mechanism, the lottery incentive mechanism demonstrates improvements of approximately 47–74% in user participation and 14–66% in platform profits.

Suggested Citation

  • Xinyu Hu & Shengjie Sun & Zhi Lv & Jiaqi Liu, 2025. "An Incentive Mechanism Based on Lottery for Data Quality in Mobile Crowdsensing," Mathematics, MDPI, vol. 13(10), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1650-:d:1658383
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    References listed on IDEAS

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    1. Marianne Silva & Gabriel Signoretti & Julio Oliveira & Ivanovitch Silva & Daniel G. Costa, 2019. "A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective," Future Internet, MDPI, vol. 11(1), pages 1-20, January.
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