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Using Causality Relationships for a Progressive Management of Hazardous Phenomena with Sensor Networks

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Computational Science and Its Applications – ICCSA 2009 (ICCSA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5592))

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Abstract

Sensor networks prove extremely valuable in providing geo-information for any decision support system particularly those aiming to manage hazardous events. A thorough understanding and use of the semantics of this information allows for the identification and handling of impending hazardous events. An appropriate representation of the geo-information should boost this process. In this paper, we propose to encode causality relationships about natural phenomena and their effects in time and space with the concept of conceptual graphs. Using this encoding, we define the concepts of event and spatial propagation paths that enable the system to delimit the scope of sensed areas and use of sensing resources. These concepts also enable the system to set up priorities between the sensor network activities. These priorities are used to implement a progressive approach for the management of hazardous events.

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Author information

Authors and Affiliations

  1. Computer Science Department, Dhofar Unversity, P.B. 2509, Postal Code 211, Salalah, Oman

    Nafaa Jabeur

  2. Computer Science Department, Laval University, Québec, Canada

    Hedi Haddad

Authors
  1. Nafaa Jabeur
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  2. Hedi Haddad
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  2. School of Business Systems, Monash University, 3800, Clayton, Victoria, Australia

    David Taniar

  3. L.I.S.U.T. - D.A.P.I.T., University of Basilicata, Viale dell’Ateneo Lucano 10, 85100, Potenza, Italy

    Beniamino Murgante

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, 06123, Perugia, Italy

    Antonio Laganà

  5. School of Computing, Computer Communication Laboratory, SoongSil University, 1-1 Sang-do 5 Dong, Dong-jak Ku, 156-743, Seoul, Korea

    Youngsong Mun

  6. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

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© 2009 Springer-Verlag Berlin Heidelberg

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Jabeur, N., Haddad, H. (2009). Using Causality Relationships for a Progressive Management of Hazardous Phenomena with Sensor Networks. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02454-2_1

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  • DOI: https://doi.org/10.1007/978-3-642-02454-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02453-5

  • Online ISBN: 978-3-642-02454-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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