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Open Access

An Integrated Incentive Framework for Mobile Crowdsourced Sensing

Wei DaiYufeng Wang( )Qun JinJianhua Ma
College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Department of Human Informatics and Cognitive Sciences, Waseda University, Saitama 359-1192, Japan.
Faculty of Computer & Information Sciences, Hosei University, Tokyo 184-8584, Japan.
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Abstract

Currently, mobile devices (e.g., smartphones) are equipped with multiple wireless interfaces and rich built-in functional sensors that possess powerful computation and communication capabilities, and enable numerous Mobile Crowdsourced Sensing (MCS) applications. Generally, an MCS system is composed of three components: a publisher of sensing tasks, crowd participants who complete the crowdsourced tasks for some kinds of rewards, and the crowdsourcing platform that facilitates the interaction between publishers and crowd participants. Incentives are a fundamental issue in MCS. This paper proposes an integrated incentive framework for MCS, which appropriately utilizes three widely used incentive methods: reverse auction, gamification, and reputation updating. Firstly, a reverse-auction-based two-round participant selection mechanism is proposed to incentivize crowds to actively participate and provide high-quality sensing data. Secondly, in order to avoid untruthful publisher feedback about sensing-data quality, a gamification-based verification mechanism is designed to evaluate the truthfulness of the publisher’s feedback. Finally, the platform updates the reputation of both participants and publishers based on their corresponding behaviors. This integrated incentive mechanism can motivate participants to provide high-quality sensed contents, stimulate publishers to give truthful feedback, and make the platform profitable.

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Tsinghua Science and Technology
Pages 146-156
Cite this article:
Dai W, Wang Y, Jin Q, et al. An Integrated Incentive Framework for Mobile Crowdsourced Sensing. Tsinghua Science and Technology, 2016, 21(2): 146-156. https://doi.org/10.1109/TST.2016.7442498

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Received: 15 August 2015
Revised: 15 November 2015
Accepted: 19 February 2016
Published: 31 March 2016
© The author(s) 2016
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