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Sensing-gain constrained participant selection mechanism for mobile crowdsensing

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Abstract

Participant selection is a fundamental challenge to perform sensing tasks with adequate data quality in various mobile crowdsensing (MCS) applications. In this paper, we explore participant selection mechanisms with sensing-gain constraints in MCS. First, we propose a novel quality-aware participant reputation model with active factors. Second, since user density differs in various applications, we propose two kinds of sensing-gain constrained participant selection mechanisms with both sufficient and insufficient user resources. Particularly, in the case of sufficient user resources, we formulate the sensing-gain objective on recruit cost and participant scale under constraints on data quality and task coverage, and propose a M ulti-S tage D ecision mechanism via G reedy strategy (MSD-G); in the case of insufficient user resources, we formulate the sensing-gain objective on data quality, abstract it as a 0-1 knapsack problem, and devise a S ensing-G ain C onstrained D ynamic P rogramming (SGC-DP) mechanism. Extensive simulations over a real-world dataset have verified that our participant reputation model with active factors can distinguish high-quality participants with different active levels, and our MSD-G and SGC-DP algorithms can effectively select suitable participants with ideal recruit budget and guaranteed data quality.

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Acknowledgments

Thanks Prof. Liang Liu in IoT technology laboratory of Bei**g University of Posts and Telecommunications for providing all the materials of “Third-Eye” project.

Funding

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61872027 and No. 62072029, Open Research Fund of the State Key Laboratory of Integrated Services Networks under Grant No. ISN21-16, and Bei**g NSF No. L192004.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Bei**g Jiaotong University, Bei**g, 100044, China

    Dan Tao & Hongbin Sun

  2. The State Key Laboratory of Integrated Services Networks, **dian University, **’an, 710071, China

    Dan Tao

  3. School of Software Engineering, Bei**g Jiaotong University, Bei**g, 100044, China

    Ruipeng Gao

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  1. Dan Tao
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  2. Ruipeng Gao
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  3. Hongbin Sun
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Correspondence to Dan Tao.

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Tao, D., Gao, R. & Sun, H. Sensing-gain constrained participant selection mechanism for mobile crowdsensing. Pers Ubiquit Comput 27, 631–645 (2023). https://doi.org/10.1007/s00779-020-01470-8

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