Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN

Zhi Liu, Toshitaka Tsuda, Hiroshi Watanabe

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Citations (Scopus)

    Abstract

    WSNs are good options to help monitor the scene of interest and notify the unusual happening to control center. But sensors' high sampling rates lead to tremendous network traffic over the bandwidth-limited and energy-critical WSNs, hence how to reduce the network traffic while maintaining the unusual events monitoring function becomes important. In this paper, we investigate the intra-correlations of the data generated by each sensor at different time instances. And we propose a traffic deduction algorithm exploring the sensor data's intra-correlations which could reduce the data volume significantly and guarantee the parameters needed for unusual detection are delivered.

    Original languageEnglish
    Title of host publication2015 IEEE SENSORS - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Print)9781479982028
    DOIs
    Publication statusPublished - 2015 Dec 31
    Event14th IEEE SENSORS - Busan, Korea, Republic of
    Duration: 2015 Nov 12015 Nov 4

    Other

    Other14th IEEE SENSORS
    CountryKorea, Republic of
    CityBusan
    Period15/11/115/11/4

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    ASJC Scopus subject areas

    • Instrumentation
    • Electronic, Optical and Magnetic Materials
    • Spectroscopy
    • Electrical and Electronic Engineering

    Cite this

    Liu, Z., Tsuda, T., & Watanabe, H. (2015). Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN. In 2015 IEEE SENSORS - Proceedings [7370621] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSENS.2015.7370621