Strategy for wireless transmission disturbances detection and identification in industrial wireless sensor networks: Dissertation

Marina Eskola

    Research output: ThesisDissertationMonograph

    Abstract

    This doctoral thesis presents a novel method for detecting and identifying radio channel disturbances. Wireless transmission in factory environments occasionally becomes degraded due to harsh radio signal propagation conditions. The most disruptive disturbances are caused by other wireless devices (co-channel and adjacent interference), environmental characteristics such as metal constructions and heavy equipment (signal fading), and electrical and mechanical equipment (noise). We have performed extensive measurements in different industrial environments to study the effects of different radio channel disturbances on signal propagation. The obtained measurement results led us to develop signal analysis methods and a disturbance detector based on a classifier. The classifier was tested in a real industrial environment; these tests showed an encouraging detection performance. Signal analysis is performed both in time and frequency domains. In the time domain, the probability density function (PDF) method is used. It is based on detecting the PDF shape variations calculated from the received signal and their correlation to the environmental changes in the radio channel. In the frequency domain, spectrogram analysis is performed. Here, we calculate the spectrogram of each received data packet, and by applying image analysis tools on the spectrograms, we obtain the required information on the received signal and on other radio transmissions which may have disturbed our radio signal transmission. The recognition and identification of the radio channel disturbances is based on the fusion of the signal magnitude analysis (PDF) and spectrogram analysis with a classifier. We also discuss the development process of a classifier-based tool towards an embedded solution. The classifier was developed and extensively tested using USPR N210, ETTUS SDR (Software Defined Radio). The classifier proved to be a feasible solution for detecting and identifying reliability flaws of wireless transmission in the industrial environment, and will be further developed into a portable, small-sized SDR-based tool.
    Original languageEnglish
    QualificationDoctor Degree
    Awarding Institution
    • University of Oulu
    Supervisors/Advisors
    • Heikkilä, Tapio, Supervisor
    • Silvén, Olli, Supervisor, External person
    • Juntti, Markku, Supervisor, External person
    Award date22 Nov 2016
    Place of PublicationEspoo
    Publisher
    Print ISBNs978-951-38-8468-0
    Electronic ISBNs978-951-38-8467-3
    Publication statusPublished - 2016
    MoE publication typeG4 Doctoral dissertation (monograph)

    Fingerprint

    Wireless sensor networks
    Classifiers
    Signal analysis
    Probability density function
    Frequency domain analysis
    Radio transmission
    Image analysis
    Industrial plants
    Fusion reactions
    Detectors
    Defects
    Metals

    Keywords

    • radio channel disturbances
    • software defined radio
    • classifier
    • Rician distribution
    • reliability
    • industrial wireless sensor networks

    Cite this

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    title = "Strategy for wireless transmission disturbances detection and identification in industrial wireless sensor networks: Dissertation",
    abstract = "This doctoral thesis presents a novel method for detecting and identifying radio channel disturbances. Wireless transmission in factory environments occasionally becomes degraded due to harsh radio signal propagation conditions. The most disruptive disturbances are caused by other wireless devices (co-channel and adjacent interference), environmental characteristics such as metal constructions and heavy equipment (signal fading), and electrical and mechanical equipment (noise). We have performed extensive measurements in different industrial environments to study the effects of different radio channel disturbances on signal propagation. The obtained measurement results led us to develop signal analysis methods and a disturbance detector based on a classifier. The classifier was tested in a real industrial environment; these tests showed an encouraging detection performance. Signal analysis is performed both in time and frequency domains. In the time domain, the probability density function (PDF) method is used. It is based on detecting the PDF shape variations calculated from the received signal and their correlation to the environmental changes in the radio channel. In the frequency domain, spectrogram analysis is performed. Here, we calculate the spectrogram of each received data packet, and by applying image analysis tools on the spectrograms, we obtain the required information on the received signal and on other radio transmissions which may have disturbed our radio signal transmission. The recognition and identification of the radio channel disturbances is based on the fusion of the signal magnitude analysis (PDF) and spectrogram analysis with a classifier. We also discuss the development process of a classifier-based tool towards an embedded solution. The classifier was developed and extensively tested using USPR N210, ETTUS SDR (Software Defined Radio). The classifier proved to be a feasible solution for detecting and identifying reliability flaws of wireless transmission in the industrial environment, and will be further developed into a portable, small-sized SDR-based tool.",
    keywords = "radio channel disturbances, software defined radio, classifier, Rician distribution, reliability, industrial wireless sensor networks",
    author = "Marina Eskola",
    note = "BA1609",
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    language = "English",
    isbn = "978-951-38-8468-0",
    series = "VTT Science",
    publisher = "VTT Technical Research Centre of Finland",
    number = "138",
    address = "Finland",
    school = "University of Oulu",

    }

    Strategy for wireless transmission disturbances detection and identification in industrial wireless sensor networks : Dissertation. / Eskola, Marina.

    Espoo : VTT Technical Research Centre of Finland, 2016. 101 p.

    Research output: ThesisDissertationMonograph

    TY - THES

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    PY - 2016

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    AB - This doctoral thesis presents a novel method for detecting and identifying radio channel disturbances. Wireless transmission in factory environments occasionally becomes degraded due to harsh radio signal propagation conditions. The most disruptive disturbances are caused by other wireless devices (co-channel and adjacent interference), environmental characteristics such as metal constructions and heavy equipment (signal fading), and electrical and mechanical equipment (noise). We have performed extensive measurements in different industrial environments to study the effects of different radio channel disturbances on signal propagation. The obtained measurement results led us to develop signal analysis methods and a disturbance detector based on a classifier. The classifier was tested in a real industrial environment; these tests showed an encouraging detection performance. Signal analysis is performed both in time and frequency domains. In the time domain, the probability density function (PDF) method is used. It is based on detecting the PDF shape variations calculated from the received signal and their correlation to the environmental changes in the radio channel. In the frequency domain, spectrogram analysis is performed. Here, we calculate the spectrogram of each received data packet, and by applying image analysis tools on the spectrograms, we obtain the required information on the received signal and on other radio transmissions which may have disturbed our radio signal transmission. The recognition and identification of the radio channel disturbances is based on the fusion of the signal magnitude analysis (PDF) and spectrogram analysis with a classifier. We also discuss the development process of a classifier-based tool towards an embedded solution. The classifier was developed and extensively tested using USPR N210, ETTUS SDR (Software Defined Radio). The classifier proved to be a feasible solution for detecting and identifying reliability flaws of wireless transmission in the industrial environment, and will be further developed into a portable, small-sized SDR-based tool.

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    KW - reliability

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