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MobiTrack: Mobile Crowdsensing-Based Object Tracking with Min-Region and Max-Utility

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Algorithms and Architectures for Parallel Processing (ICA3PP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13156))

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Abstract

Exploiting mobile cameras embedded on the widely-used smartphones to serve object tracking offers a new dimension to reduce the deployment cost of the stationary cameras and shorten the tracking latency, but brings the challenges in efficient task assignment and cooperations among workers due to the requirement of Mobile Crowdsensing (MCS) system. Most existing effort in the literature focuses on object tracking with MCS where the workers capture the moving object photos at pre-calculated sites. However, the contradiction between the tracking coverage and the system cost in these MCS-based tracking solutions is sharpened when tracking scenarios and worker number vary. In this paper, we investigate the tracking region to conduct the task assignment among top-k most probable sensing locations, which can achieve maximal tracking utility. Specifically, we construct a N-Gram prediction model to determine the k tracking locations and formulate the task assignment problem solved by the Kuhn-Munkras algorithm, respectively, laying a theoretical foundation. The prediction model soundness is verified statistically and the task assignment effectiveness is evaluated via large scale real-world data simulations.

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Acknowledgments

This work was supported by the Cyberspace International Governance Research Institute in Southeast University, the CERNET Southeastern China (North) Regional Network Center, the National Key Research and Development Program of China under Grant 2018YFB1800205 and the Industry-Academia-Research Innovation Fund of Chinese University (Alibaba Cloud Digital Innovation Project for University) under Grant 2021ALA03006.

Author information

Authors and Affiliations

  1. School of Cyber Science and Engineering, Southeast University, Nanjing, China

    Wenqiang Li, Jun Tao, Zuyan Wang, YiFan Xu, Xiaolei Tang & YiChao Dong

  2. Key Lab of CNII, MOE, Southeast University, Nanjing, China

    Wenqiang Li, Jun Tao, Zuyan Wang, YiFan Xu, Xiaolei Tang & YiChao Dong

  3. Purple Mountain Laboratories for Network and Communication Security, Nanjing, China

    Jun Tao

Authors
  1. Wenqiang Li
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  2. Jun Tao
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  3. Zuyan Wang
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  4. YiFan Xu
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  5. Xiaolei Tang
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  6. YiChao Dong
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Corresponding author

Correspondence to Jun Tao .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Yongxuan Lai

  2. Beijing Normal University, Zhuhai, China

    Tian Wang

  3. Xiamen University, Xiamen, China

    Min Jiang

  4. Tianjin University, Tianjin, China

    Guangquan Xu

  5. Hunan University, Changsha, China

    Wei Liang

  6. University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

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Li, W., Tao, J., Wang, Z., Xu, Y., Tang, X., Dong, Y. (2022). MobiTrack: Mobile Crowdsensing-Based Object Tracking with Min-Region and Max-Utility. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-95388-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95387-4

  • Online ISBN: 978-3-030-95388-1

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