A semantic IoT early warning system for natural environment crisis management
A semantic IoT early warning system for natural environment crisis management
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 W3CWeb 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 reployed EWS infrastructure.
crisis management, early warning system, internet of things, resilience, time-critical, scalable, semantic web
246-257
Poslad, Stefan
fad30231-02aa-46ee-b9fc-3742f206b419
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Chaves, Fernando
170ae2b1-5d9e-4c48-af9a-e4f5eaaf93d7
Tao, Ran
08d40ff6-99d4-4017-9f02-a789ec813496
Necmioglu, Ocal
c172a229-728b-4c82-9fc8-62734add9adf
Bügel, Ulrich
83cb5925-29f8-4338-ac1b-9fc60d62d2e9
June 2015
Poslad, Stefan
fad30231-02aa-46ee-b9fc-3742f206b419
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Chaves, Fernando
170ae2b1-5d9e-4c48-af9a-e4f5eaaf93d7
Tao, Ran
08d40ff6-99d4-4017-9f02-a789ec813496
Necmioglu, Ocal
c172a229-728b-4c82-9fc8-62734add9adf
Bügel, Ulrich
83cb5925-29f8-4338-ac1b-9fc60d62d2e9
Poslad, Stefan, Middleton, Stuart, Chaves, Fernando, Tao, Ran, Necmioglu, Ocal and Bügel, Ulrich
(2015)
A semantic IoT early warning system for natural environment crisis management.
[in special issue: Advances in Semantic Computing]
IEEE Transaction on Emerging Topics in Computing, 3 (2), .
(doi:10.1109/TETC.2015.2432742).
Abstract
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 W3CWeb 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 reployed EWS infrastructure.
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378318.pdf
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More information
Accepted/In Press date: 25 April 2015
e-pub ahead of print date: 18 May 2015
Published date: June 2015
Keywords:
crisis management, early warning system, internet of things, resilience, time-critical, scalable, semantic web
Organisations:
IT Innovation
Identifiers
Local EPrints ID: 378318
URI: http://eprints.soton.ac.uk/id/eprint/378318
PURE UUID: b6a3ae85-dcc8-4d21-9e46-bfede543e305
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Date deposited: 01 Jul 2015 11:43
Last modified: 15 Mar 2024 03:08
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Contributors
Author:
Stefan Poslad
Author:
Fernando Chaves
Author:
Ran Tao
Author:
Ocal Necmioglu
Author:
Ulrich Bügel
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