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Modelling and Monitoring Environmental Risks through a Semantic Framework

Modelling and Monitoring Environmental Risks through a Semantic Framework
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Author(s): Domenico Calcaterra (Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy), Marco Cavallo (Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy), Giuseppe Di Modica (Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy) and Orazio Tomarchio (Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy)
Copyright: 2016
Volume: 7
Issue: 4
Pages: 21
Source title: International Journal of Distributed Systems and Technologies (IJDST)
Editor(s)-in-Chief: Nik Bessis (Edge Hill University, UK)
DOI: 10.4018/IJDST.2016100101

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Abstract

The widespread availability of IoT technologies allows, among others, detailed monitoring of environmental parameters that can be used for effective prevention and forecasts of natural disasters. When such events occur, or are about to occur, the primary concern of authorities responsible for the public safety is to organise and enforce the relief in an as quick as possible and effective way. However, the huge amount of information that an IoT infrastructure is able to provide is useless if not correlated and contextualised on the territory where the hazard has occurred. In this paper the authors present the design of a semantic information model which integrates different information domains into a unified framework. In particular, some ontologies have been defined to represent, respectively, the information domain of hazards, the domain of sensors (which are responsible for measuring the phenomena that may trigger hazards), the geographic domain and the environmental risk domain, also including the potential damage and dangerousness of such events. In the proposed semantic framework, a place (be it a single building or an entire region) can be characterised from the environmental risk point of view. The resulting knowledge base can then be used to build monitoring services capable of identifying on the territory those sensing units whose sensed data are really useful for the setup of a relief plan. A prototype of such a monitoring tool was also implemented. Finally, two simple yet complete use case examples focusing on the wildfire hazard and on the flood risk respectively are discussed in the paper.

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