The University of Southampton
University of Southampton Institutional Repository

IoT and Semantic Web technologies for event-detection in natural disasters

IoT and Semantic Web technologies for event-detection in natural disasters
IoT and Semantic Web technologies for event-detection in natural disasters
Natural disasters cannot be predicted well in advance but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart col- lection, integration and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster pre- vention and management. In this paper we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earth- quake. In our proposal, a prototype system, that retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.
1532-0628
1-12
Greco, Luca
3c2a1cd8-1b60-4c0c-bb5b-0f0a2340023f
Ritrovato, Pierluigi
5e8cdc6c-8368-4c71-9710-62fc254f91c2
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Xhafa, Fatos
2ee44508-489e-4589-95fe-24e2cb47a78c
Greco, Luca
3c2a1cd8-1b60-4c0c-bb5b-0f0a2340023f
Ritrovato, Pierluigi
5e8cdc6c-8368-4c71-9710-62fc254f91c2
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Xhafa, Fatos
2ee44508-489e-4589-95fe-24e2cb47a78c

Greco, Luca, Ritrovato, Pierluigi, Tiropanis, Thanassis and Xhafa, Fatos (2018) IoT and Semantic Web technologies for event-detection in natural disasters. Concurrency and Computation: Practice & Experience, 1-12, [e4789]. (doi:10.1002/cpe.4789).

Record type: Article

Abstract

Natural disasters cannot be predicted well in advance but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart col- lection, integration and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster pre- vention and management. In this paper we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earth- quake. In our proposal, a prototype system, that retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.

Text
_system_appendPDF_proof_hi - Accepted Manuscript
Download (938kB)

More information

Accepted/In Press date: 31 May 2018
e-pub ahead of print date: 24 August 2018

Identifiers

Local EPrints ID: 423227
URI: http://eprints.soton.ac.uk/id/eprint/423227
ISSN: 1532-0628
PURE UUID: b870182a-dc41-4da3-b2a8-e8af3b57d4f3
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852

Catalogue record

Date deposited: 19 Sep 2018 16:30
Last modified: 07 Oct 2020 05:54

Export record

Altmetrics

Contributors

Author: Luca Greco
Author: Pierluigi Ritrovato
Author: Thanassis Tiropanis ORCID iD
Author: Fatos Xhafa

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×