Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings

Research Article

Energy-Efficient Data Collection Using Lossless Compression for Industrial Wireless Sensor Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-74176-5_2,
        author={Xiaolan Tang and Hua Xie and Wenlong Chen and Jianwei Niu},
        title={Energy-Efficient Data Collection Using Lossless Compression for Industrial Wireless Sensor Networks},
        proceedings={Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings},
        proceedings_a={INISCOM},
        year={2018},
        month={1},
        keywords={Industrial wireless sensor networks Sensing duplication rate Data aggregation Redundant data},
        doi={10.1007/978-3-319-74176-5_2}
    }
    
  • Xiaolan Tang
    Hua Xie
    Wenlong Chen
    Jianwei Niu
    Year: 2018
    Energy-Efficient Data Collection Using Lossless Compression for Industrial Wireless Sensor Networks
    INISCOM
    Springer
    DOI: 10.1007/978-3-319-74176-5_2
Xiaolan Tang1, Hua Xie1, Wenlong Chen1,*, Jianwei Niu2
  • 1: Capital Normal University
  • 2: Beihang University
*Contact email: chenwenlong@cnu.edu.cn

Abstract

Industrial wireless sensor network is an important technology for precise monitoring in industrial systems. Sensors are deployed densely in various industry applications, where the high density of sensors results in large amounts of redundant data. Therefore, information aggregation is used to avoid forwarding redundant data and thus save limited resources. However, when decreasing transmission cost, existing aggregation schemes lead to low data accuracy and long delivery latency. In this paper, we propose an energy-efficient data collection solution using lossless compression for industrial wireless sensor networks, namely ECL, aiming for high energy efficiency and high information entropy. According to three aggregation rules, aggregation regions are constructed in a distributed way based on a preset threshold of sensing duplication rate. Therefore, the aggregated data are probably similar, and ECL has the original entropy through removing only the redundant data. Experiment results show that compared with other schemes, ECL keeps about 38% and 48% higher data accuracy and 12% and 25% shorter maximum end-to-end delay than EEUC and HEER, respectively, with a similar lifetime.