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Spatial–temporal compression and recovery in a wireless sensor network in an underground tunnel environment

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Abstract

In an exciting new application, wireless sensor networks (WSNs) are increasingly being deployed to monitor the structure health of underground subway tunnels, promising many advantages over traditional monitoring methods. As a result, ensuring efficient data communication, transmission, and storage have become a huge challenge for these systems as they try to cope with ever increasing quantities of data collected by ever growing numbers of sensor nodes. A key approach of managing big data in WSNs is through data compression. Reducing the volume of data traveling between sensor nodes can reduce the high energy cost of data transmission, as well as save space for storage of big data. In this paper, we propose an algorithm for the compression of spatial–temporal data from one data type of sensor node in a WSN deployed in an underground tunnel. The proposed algorithm works efficiently because it considers temporal as well as spatial features of sensor data. A recovery process is required for recovering the data with a close approximation to the original data form nodes. We validate the proposed recovery technique through computational experiments carried out using the data acquired from a real WSN.

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Acknowledgments

This work is supported by National Basic Research Program of China (973 Program: Grant No. 2011CB013803), National Science foundation of China (Grant No. 51275360) and Shanghai Outstanding Academic Leaders Program (Grant No. 12XD1405100).

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Authors and Affiliations

  1. The College of Electronics and Information Engineering, Tongji University, Shanghai, China

    Bin He, Yonggang Li & Haifeng Tang

  2. The College of Civil Engineering of Tongji University, Shanghai, China

    Hongwei Huang

Authors
  1. Bin He
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  2. Yonggang Li
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  3. Hongwei Huang
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  4. Haifeng Tang
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Correspondence to Bin He.

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He, B., Li, Y., Huang, H. et al. Spatial–temporal compression and recovery in a wireless sensor network in an underground tunnel environment. Knowl Inf Syst 41, 449–465 (2014). https://doi.org/10.1007/s10115-014-0772-9

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  • DOI: https://doi.org/10.1007/s10115-014-0772-9

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