Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks

Marina Eskola, Tapio Heikkilä

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    The performance of wireless sensor networks (WSNs) in industrial environments may be degraded due to harsh radio signal propagation conditions. Short-term signal disturbances are of major concern due to incoming and outgoing trucks, moving around forklifts and workers, as well as large concrete and metal surfaces. We studied the effects of some environmental disturbances on the quality of the transmitted signals. We consider directly measurable transmission quality parameters Received Signal Strength Indicator (RSSI) and Bit-Error-Rate (BER) as well as statistical characteristics of the received signal magnitude. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the short-term radio disturbances by calculating the probability density function (PDF) of the received signal magnitude and analyzing the parameters of the PDFs. The results show that tracking the changes of the PDF of the received signal magnitudes contributes to recognize and characterize the temporal changes in the radio link environment and in that way to choose the right actions in order to overcome the communication problems.
    Original languageEnglish
    Pages (from-to)201-208
    JournalJournal of Networks
    Volume10
    Issue number4
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Wireless sensor networks
    Probability density function
    Radio links
    Bit error rate
    Trucks
    Concretes
    Communication
    Metals

    Keywords

    • radio distrubances
    • Wireless Sensor Networks
    • Rician distribution
    • Software Defined Radio
    • industrial measurements

    Cite this

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    title = "Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks",
    abstract = "The performance of wireless sensor networks (WSNs) in industrial environments may be degraded due to harsh radio signal propagation conditions. Short-term signal disturbances are of major concern due to incoming and outgoing trucks, moving around forklifts and workers, as well as large concrete and metal surfaces. We studied the effects of some environmental disturbances on the quality of the transmitted signals. We consider directly measurable transmission quality parameters Received Signal Strength Indicator (RSSI) and Bit-Error-Rate (BER) as well as statistical characteristics of the received signal magnitude. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the short-term radio disturbances by calculating the probability density function (PDF) of the received signal magnitude and analyzing the parameters of the PDFs. The results show that tracking the changes of the PDF of the received signal magnitudes contributes to recognize and characterize the temporal changes in the radio link environment and in that way to choose the right actions in order to overcome the communication problems.",
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    author = "Marina Eskola and Tapio Heikkil{\"a}",
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    language = "English",
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    Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks. / Eskola, Marina; Heikkilä, Tapio.

    In: Journal of Networks, Vol. 10, No. 4, 2015, p. 201-208.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Detection of short-term radio signal disturbances in industrial Wireless Sensor Networks

    AU - Eskola, Marina

    AU - Heikkilä, Tapio

    PY - 2015

    Y1 - 2015

    N2 - The performance of wireless sensor networks (WSNs) in industrial environments may be degraded due to harsh radio signal propagation conditions. Short-term signal disturbances are of major concern due to incoming and outgoing trucks, moving around forklifts and workers, as well as large concrete and metal surfaces. We studied the effects of some environmental disturbances on the quality of the transmitted signals. We consider directly measurable transmission quality parameters Received Signal Strength Indicator (RSSI) and Bit-Error-Rate (BER) as well as statistical characteristics of the received signal magnitude. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the short-term radio disturbances by calculating the probability density function (PDF) of the received signal magnitude and analyzing the parameters of the PDFs. The results show that tracking the changes of the PDF of the received signal magnitudes contributes to recognize and characterize the temporal changes in the radio link environment and in that way to choose the right actions in order to overcome the communication problems.

    AB - The performance of wireless sensor networks (WSNs) in industrial environments may be degraded due to harsh radio signal propagation conditions. Short-term signal disturbances are of major concern due to incoming and outgoing trucks, moving around forklifts and workers, as well as large concrete and metal surfaces. We studied the effects of some environmental disturbances on the quality of the transmitted signals. We consider directly measurable transmission quality parameters Received Signal Strength Indicator (RSSI) and Bit-Error-Rate (BER) as well as statistical characteristics of the received signal magnitude. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the short-term radio disturbances by calculating the probability density function (PDF) of the received signal magnitude and analyzing the parameters of the PDFs. The results show that tracking the changes of the PDF of the received signal magnitudes contributes to recognize and characterize the temporal changes in the radio link environment and in that way to choose the right actions in order to overcome the communication problems.

    KW - radio distrubances

    KW - Wireless Sensor Networks

    KW - Rician distribution

    KW - Software Defined Radio

    KW - industrial measurements

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    DO - 10.4304/jnw.10.4.201-208

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