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Accuracy-Guaranteed Event Detection via Collaborative Mobile Crowdsensing with Unreliable Users

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2019)

Abstract

Recently, mobile crowdsensing has become a promising paradigm to collect rich spatial sensing data, by taking advantage of widely distributed sensing devices like smartphones. Based on sensing data, event detection can be conducted in urban areas, to monitor abnormal incidents like traffic jam. However, how to guarantee the detection accuracy is still an open issue, especially when unreliable users who may report wrong observations are considered. In this work, we focus on the problem of user recruitment in collaborative mobile crowdsensing, aiming to optimize the fine-grained detection accuracy in a large urban area. Unfortunately, the problem is proved to be NP-hard, which means there is no polynomial-time algorithm to achieve the optimal solution unless P \(=\) NP. To meet the challenge, we first employ a probabilistic model to characterize the unreliability of users, and measure the uncertainty of inferring event occurrences given collected observations by Shannon entropy. Then, by leveraging the properties of adaptive monotonicity and adaptive submodularity, we propose an adaptive greedy algorithm for user recruitment, which is theoretically proved to achieve a constant approximation ratio guarantee. Extensive simulations are conducted, which show our proposed algorithm outperforms baselines under different settings.

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Notes

  1. 1.

    We consider there are eight directions: northward, southward, westward, eastward, northwestward, northeastward, southwestward, southeastward.

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Acknowledgements

This research is supported by NSFC (No. 61772341, 61472254, and 61802245), STSCM (No. 18511103002 and No. 16010500400), and KQJSCX20180329191021388. This work is also supported by the Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, the Program for Shanghai Top Young Talents, Shanghai Engineering Research Center of Digital Education Equipment, SJTU Global Strategic Partnership Fund (2019 SJTU-HKUST), and the Shanghai Sailing Program (No. 18YF1408200).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Tong Liu, Wenbin Wu & Weiqin Tong

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China

    Tong Liu & Weiqin Tong

  3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yanmin Zhu

  4. Shanghai Key Lab of Scalable Computing and Systems, Shanghai, China

    Yanmin Zhu

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  1. Tong Liu
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  2. Wenbin Wu
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  3. Yanmin Zhu
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  4. Weiqin Tong
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Correspondence to Tong Liu .

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Editors and Affiliations

  1. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  2. Shanghai University, Shanghai, China

    Honghao Gao

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. University of Exeter, Exeter, UK

    Geyong Min

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Liu, T., Wu, W., Zhu, Y., Tong, W. (2019). Accuracy-Guaranteed Event Detection via Collaborative Mobile Crowdsensing with Unreliable Users. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_49

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  • DOI: https://doi.org/10.1007/978-3-030-30146-0_49

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  • Online ISBN: 978-3-030-30146-0

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