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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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An Integrated Incentive Framework for Mobile Crowdsourced Sensing

Show Author's information 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.

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.

Keywords: gamification, mobile crowdsourced sensing, incentive mechanism, reverse auction, reputation updating

References(18)

[1]
Wang Y., Jia X., Jin Q., and Ma J., Mobile crowdsourcing: Architecture, applications, and challenges, Concurrency and Computation: Practice and Experience, 2016. .
[2]
Dong Y., Kanhere S., Chou C.T., and Liu R. P., Automatic image capturing and processing for petrolwatch, in Proc. ICON, 2011.
[3]
Zheng Y., Capra L., Wolfson O., and Yang H., Urban computing: Concepts, methodologies, and applications, ACM Trans. Intell. Syst. Technol., vol. 5, no. 3, p. 38, 2014.
[4]
Restuccia F., Das S. K., and Payton J., Incentive mechanisms for participatory sensing: Survey and research challenges, arXiv:1502.07687, 2015.
[5]
Gao H., Liu C. H., Wang W., Zhao J., Song Z., Su X., Crowcroft J., and Leung K. K., A survey of incentive mechanisms for participatory sensing, IEEE Communication Surveys & Tutorials, vol. 17, no. 2, pp. 918–943, 2015.
[6]
Xu H. and Larson K., Improving the efficiency of crowdsourcing contests, in Proc. Int. Conf. Auton. Agents Multi-Agent Syst., 2014.
[7]
Luo T., Tan H. P., and Xia L., Profit-maximizing incentive for participatory sensing, in Proc. IEEE INFOCOM, 2014.
[8]
Zhang X., Yang Z., Sun W., Liu Y., Tang S., Xing K., and Mao X., Incentive for mobile crowdsourcing: A survey, IEEE Communications Surveys & Tutorials, 2015. .
[9]
Mason W. and Watts D. J., Financial incentives and the performance of crowds, ACM SigKDD Explorations Newsletter, vol. 11, no. 2, pp. 100–108, 2009.
[10]
Ra M. R., Liu B., La Porta T. F., and Govindan R., Medusa: A programming framework for crowd-sensing applications, in Proc. ACM MobiSys, 2012.
[11]
Hoh B., Yan T., Ganesan D., Tracton K., Iwuchukwu T., and Lee J. S., TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services, in Proc. IEEE ITSC, 2012.
[12]
Wang Y., Jia X., Jin Q., and Ma J., QuaCentive: A quality-aware incentive mechanism in mobile crowdsourced sensing (MCS), Journal of Supercomputing, 2015. .
[13]
Amintoosi H. and Kanhere S. S., A reputation framework for social participatory sensing systems, Mobile Networks and Applications, vol. 19, no. 1, pp. 88–100, 2014.
[14]
Lasecki W. S., Teevan J., and Kamar E., The cost of asking crowd workers to behave maliciously, in Proc. the AAMAS Workshop on Human-Agent Interaction Design and Models, 2015.
[15]
Papadimitriou S S., Kitagawa H., Gibbons P., and Faloutsos C., Loci: Fast outlier detection using the local correlation integral, in Proc. the IEEE ICDE, 2003.
[16]
Deterding S., Dixon D., Khaled R., and Nacke L., From game design elements to gamefulness: Defining gamification, in Proc. MindTrek, 2011.
[17]
Eickhoff C., Harris C. G., de Vries A. P., and Srinivasan P., Quality through flow and immersion: Gamifying crowdsourced relevance assements, in Proc. ACM SIGIR, 2012.
[18]
Huang K. L., Kanhere S. S., and Hu W., On the need for a reputation system in mobile phone based sensing, Ad Hoc Networks, vol. 12, pp. 130–149, 2014.
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Publication history

Received: 15 August 2015
Revised: 15 November 2015
Accepted: 19 February 2016
Published: 31 March 2016
Issue date: April 2016

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© The author(s) 2016

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61171092) and in part by the Jiangsu Educational Bureau Project (No. 14KJA510004).

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