Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks

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    Abstract

    Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optimal set of sensors as well as use of learning mechanisms and Artificial Intelligence (AI) techniques. They have only made some assumptions on what might be considered as appropriate sensor or an arbitrary AI technique has been used. By closing the gap between traditional fire detection techniques and modern wireless sensor network capabilities, in this paper we present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection. Additionally, applicability of a feed forward neural network (FFNN) and Naïve Bayes Classifier is investigated and results in terms of detection rate and computational complexity are analyzed.
    Original languageUndefined
    Title of host publicationAIAI 2009 Workshop Proceedings
    PublisherCEUR
    Pages311-321
    Number of pages11
    Publication statusPublished - 3 Jul 2009
    Event5th IFIP Conference on Artificial Intelligence Applications & Innovations, AIAI 2009 - Capsis Hotel Conference Centre, Thessaloniki, Greece
    Duration: 23 Apr 200925 Apr 2009
    Conference number: 5
    http://delab.csd.auth.gr/aiai2009/

    Publication series

    Name
    Publisherceur-ws.org
    Volume475
    ISSN (Print)1613-0073

    Workshop

    Workshop5th IFIP Conference on Artificial Intelligence Applications & Innovations, AIAI 2009
    Abbreviated titleAIAI
    CountryGreece
    CityThessaloniki
    Period23/04/0925/04/09
    Internet address

    Keywords

    • METIS-263909
    • Neural Networks
    • EWI-15673
    • IR-67514
    • Sensor Networks
    • Event Detection

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