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    A Semantic IoT Early Warning System for Natural Environment Crisis Management 
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    A Semantic IoT Early Warning System for Natural Environment Crisis Management

    Volume
    3
    Pagination
    246 - 257
    DOI
    10.1109/TETC.2015.2432742
    Issue
    2
    ISSN
    2168-6750
    Metadata
    Show full item record
    Abstract
    © 2013 IEEE. An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model. We use lightweight semantics for metadata to enhance rich sensor data acquisition. We use heavyweight semantics for top level W3C Web Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a deployed EWS infrastructure.
    Authors
    Poslad, S; Middleton, SE; Chaves, F; Tao, R; Necmioglu, O; Bugel, U
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/8340
    Collections
    • Electronic Engineering and Computer Science [2362]
    Licence information
    (c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. The final publication is available at IEEE via http://dx.doi.org/10.1109/TETC.2015.2432742
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