Use of wireless sensor networks for distributed event detection in disaster management applications

M. Bahrepour, Nirvana Meratnia, Mannes Poel, Zahra Taghikhaki, Paul J.M. Havinga

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity.
    Original languageUndefined
    Pages (from-to)58-69
    Number of pages12
    JournalInternational Journal of Space-Based and Situated Computing
    Volume2
    Issue number1
    DOIs
    Publication statusPublished - Feb 2012

    Keywords

    • situated computing
    • EWI-21739
    • Event Detection
    • IR-80433
    • Wireless Sensor Networks
    • WSNs
    • METIS-296048
    • Disaster early warning systems

    Cite this

    @article{cce36918a4e449a792eda37cd9a91ad4,
    title = "Use of wireless sensor networks for distributed event detection in disaster management applications",
    abstract = "Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity.",
    keywords = "situated computing, EWI-21739, Event Detection, IR-80433, Wireless Sensor Networks, WSNs, METIS-296048, Disaster early warning systems",
    author = "M. Bahrepour and Nirvana Meratnia and Mannes Poel and Zahra Taghikhaki and Havinga, {Paul J.M.}",
    note = "eemcs-eprint-21739",
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    month = "2",
    doi = "10.1504/IJSSC.2012.045569",
    language = "Undefined",
    volume = "2",
    pages = "58--69",
    journal = "International Journal of Space-Based and Situated Computing",
    issn = "2044-4893",
    publisher = "Inderscience Publishers",
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    }

    Use of wireless sensor networks for distributed event detection in disaster management applications. / Bahrepour, M.; Meratnia, Nirvana; Poel, Mannes; Taghikhaki, Zahra; Havinga, Paul J.M.

    In: International Journal of Space-Based and Situated Computing, Vol. 2, No. 1, 02.2012, p. 58-69.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Use of wireless sensor networks for distributed event detection in disaster management applications

    AU - Bahrepour, M.

    AU - Meratnia, Nirvana

    AU - Poel, Mannes

    AU - Taghikhaki, Zahra

    AU - Havinga, Paul J.M.

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    AB - Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity.

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    KW - Event Detection

    KW - IR-80433

    KW - Wireless Sensor Networks

    KW - WSNs

    KW - METIS-296048

    KW - Disaster early warning systems

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