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