Controlling Interferences in Smart Building IoT Networks using Machine Learning

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The coexistence of many IoT networks in smart buildings poses a major challenge because they interfere mutually. In most settings this results in a greedy approach where each IoT node optimizes its own performance parameters like increasing transmit power, etc. However, this means that interference levels are increased, battery powers are wasted, and spectrum resources are exhausted in high dense settings. In order to control the interference levels, share the spectrum resources, and lower the overall power-consumption this paper proposes a centralized control scheme based on a nonlinear cost function. This cost function is optimized by using machine learning in form of a binary particle swarm optimization algorithm. It has been found that this approach shares the spectrum in a fair way, it saves power and lowers the interference levels, and it dynamically adapt to network changes.
Original languageDanish
JournalInternational Journal of Sensor Networks
ISSN1748-1279
Publication statusAccepted/In press - 2019

Keywords

    Cite this

    @article{6e9ff94be7184ba0adfac15a740cffa6,
    title = "Controlling Interferences in Smart Building IoT Networks using Machine Learning",
    abstract = "The coexistence of many IoT networks in smart buildings poses a major challenge because they interfere mutually. In most settings this results in a greedy approach where each IoT node optimizes its own performance parameters like increasing transmit power, etc. However, this means that interference levels are increased, battery powers are wasted, and spectrum resources are exhausted in high dense settings. In order to control the interference levels, share the spectrum resources, and lower the overall power-consumption this paper proposes a centralized control scheme based on a nonlinear cost function. This cost function is optimized by using machine learning in form of a binary particle swarm optimization algorithm. It has been found that this approach shares the spectrum in a fair way, it saves power and lowers the interference levels, and it dynamically adapt to network changes.",
    keywords = "maskine l{\ae}ring, smart buildings",
    author = "Per Lynggaard",
    year = "2019",
    language = "Dansk",
    journal = "International Journal of Sensor Networks",
    issn = "1748-1279",
    publisher = "Inderscience Publishers",

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    Controlling Interferences in Smart Building IoT Networks using Machine Learning. / Lynggaard, Per.

    In: International Journal of Sensor Networks, 2019.

    Research output: Contribution to journalJournal articleResearchpeer-review

    TY - JOUR

    T1 - Controlling Interferences in Smart Building IoT Networks using Machine Learning

    AU - Lynggaard, Per

    PY - 2019

    Y1 - 2019

    N2 - The coexistence of many IoT networks in smart buildings poses a major challenge because they interfere mutually. In most settings this results in a greedy approach where each IoT node optimizes its own performance parameters like increasing transmit power, etc. However, this means that interference levels are increased, battery powers are wasted, and spectrum resources are exhausted in high dense settings. In order to control the interference levels, share the spectrum resources, and lower the overall power-consumption this paper proposes a centralized control scheme based on a nonlinear cost function. This cost function is optimized by using machine learning in form of a binary particle swarm optimization algorithm. It has been found that this approach shares the spectrum in a fair way, it saves power and lowers the interference levels, and it dynamically adapt to network changes.

    AB - The coexistence of many IoT networks in smart buildings poses a major challenge because they interfere mutually. In most settings this results in a greedy approach where each IoT node optimizes its own performance parameters like increasing transmit power, etc. However, this means that interference levels are increased, battery powers are wasted, and spectrum resources are exhausted in high dense settings. In order to control the interference levels, share the spectrum resources, and lower the overall power-consumption this paper proposes a centralized control scheme based on a nonlinear cost function. This cost function is optimized by using machine learning in form of a binary particle swarm optimization algorithm. It has been found that this approach shares the spectrum in a fair way, it saves power and lowers the interference levels, and it dynamically adapt to network changes.

    KW - maskine læring

    KW - smart buildings

    M3 - Tidsskriftartikel

    JO - International Journal of Sensor Networks

    JF - International Journal of Sensor Networks

    SN - 1748-1279

    ER -