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Data Sensing with Limited Mobile Sensors in Sweep Coverage

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Combinatorial Optimization and Applications (COCOA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12577))

Abstract

Sweep coverage has received great attention with the development of wireless sensor networks in the past few decades. Sweep coverage requires mobile sensors to cover and sense environmental information from Points Of Interests (POIs) in every sweep period. In some scenarios, due to the heterogeneity of POIs and a lack of mobile sensors, mobile sensors sense the different amounts of data from different POIs, and only part of POIs can be covered by mobile sensors. Therefore, how to schedule the mobile sensors to improve coverage efficiency is important. In this paper, we propose the optimization problem (MSDSC) to maximize sensed data with a limited number of mobile sensors in sweep coverage and prove it to be NP-hard. We then devise two algorithms named GD-MSDSC and MST-MSDSC for the problem. Our simulation results show that, with a limited number of mobile sensors, GD-MSDSC and MST-MSDSC are able to sense more data from POIs than algorithms from previous work. In addition, MST-MSDSC can sense more data while the time complexity of GD-MSDSC is better.

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Acknowledgment

This work is supported by National Natural Science Foundation of China (No. 61772154), the Shenzhen Basic Research Program (Project No. JCYJ20190806143011274).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Key Laboratory of Internet Information Collaboration, Shenzhen, China

    Zixiong Nie, Chuang Liu & Hongwei Du

Authors
  1. Zixiong Nie
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  2. Chuang Liu
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  3. Hongwei Du
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Corresponding author

Correspondence to Hongwei Du .

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Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Weili Wu

  2. Xiamen University, Xiamen, China

    Zhongnan Zhang

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Nie, Z., Liu, C., Du, H. (2020). Data Sensing with Limited Mobile Sensors in Sweep Coverage. In: Wu, W., Zhang, Z. (eds) Combinatorial Optimization and Applications. COCOA 2020. Lecture Notes in Computer Science(), vol 12577. Springer, Cham. https://doi.org/10.1007/978-3-030-64843-5_45

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  • DOI: https://doi.org/10.1007/978-3-030-64843-5_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64842-8

  • Online ISBN: 978-3-030-64843-5

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