An edge-stream computing infrastructure for real-time analysis of wearable sensors data

The fast development of IoT in general and wearable smart sensors in particular in the context of wellness and healthcare are demanding for definition of specific infrastructure supporting real time data analysis for anomaly detection, event identification, situation awareness just to mention few. T...

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Detalhes bibliográficos
Autores: Greco, Luca, Ritrovato, Pierluigi, Xhafa Xhafa, Fatos|||0000-0001-6569-5497
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/127096
Acesso em linha:https://hdl.handle.net/2117/127096
https://dx.doi.org/10.1016/j.future.2018.10.058
Access Level:acceso abierto
Palavra-chave:Internet of things
Big data
Edge Computing
Big Data
Internet de les coses
Dades massives
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Internet
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repository_id_str
spelling An edge-stream computing infrastructure for real-time analysis of wearable sensors dataGreco, LucaRitrovato, PierluigiXhafa Xhafa, Fatos|||0000-0001-6569-5497Internet of thingsBig dataInternet of thingsEdge ComputingBig DataInternet de les cosesDades massivesÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::InternetThe fast development of IoT in general and wearable smart sensors in particular in the context of wellness and healthcare are demanding for definition of specific infrastructure supporting real time data analysis for anomaly detection, event identification, situation awareness just to mention few. The explosion in the development and adoption of these smart wearable sensors has contributed to the definition of the Internet of Medical Things (IoMT), which is revolutionizing the way healthcare is tackled worldwide. Data produced by wearable sensors continuously grow and could be spread among clinical centers, hospitals, research labs, yielding to a Big Data management problem. In this paper we propose a technological and architectural solution, based on Open Source big data technologies to perform real-time analysis of wearable sensor data streams. The proposed architecture is composed of four distinct layers: a sensing layer, a pre-processing layer (Raspberry Pi), a cluster processing layer (Kafka’s broker and Flink’s mini-cluster) and a persistence layer (Cassandra database). A performance evaluation of each layer has been carried out by considering CPU and memory usage for accomplishing a simple anomaly detection task using the REALDISP datasetPeer ReviewedElsevier20192019-04-0120192019-01-17journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/127096https://dx.doi.org/10.1016/j.future.2018.10.058reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1270962026-05-27T15:37:01Z
dc.title.none.fl_str_mv An edge-stream computing infrastructure for real-time analysis of wearable sensors data
title An edge-stream computing infrastructure for real-time analysis of wearable sensors data
spellingShingle An edge-stream computing infrastructure for real-time analysis of wearable sensors data
Greco, Luca
Internet of things
Big data
Internet of things
Edge Computing
Big Data
Internet de les coses
Dades massives
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Internet
title_short An edge-stream computing infrastructure for real-time analysis of wearable sensors data
title_full An edge-stream computing infrastructure for real-time analysis of wearable sensors data
title_fullStr An edge-stream computing infrastructure for real-time analysis of wearable sensors data
title_full_unstemmed An edge-stream computing infrastructure for real-time analysis of wearable sensors data
title_sort An edge-stream computing infrastructure for real-time analysis of wearable sensors data
dc.creator.none.fl_str_mv Greco, Luca
Ritrovato, Pierluigi
Xhafa Xhafa, Fatos|||0000-0001-6569-5497
author Greco, Luca
author_facet Greco, Luca
Ritrovato, Pierluigi
Xhafa Xhafa, Fatos|||0000-0001-6569-5497
author_role author
author2 Ritrovato, Pierluigi
Xhafa Xhafa, Fatos|||0000-0001-6569-5497
author2_role author
author
dc.subject.none.fl_str_mv Internet of things
Big data
Internet of things
Edge Computing
Big Data
Internet de les coses
Dades massives
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Internet
topic Internet of things
Big data
Internet of things
Edge Computing
Big Data
Internet de les coses
Dades massives
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Internet
description The fast development of IoT in general and wearable smart sensors in particular in the context of wellness and healthcare are demanding for definition of specific infrastructure supporting real time data analysis for anomaly detection, event identification, situation awareness just to mention few. The explosion in the development and adoption of these smart wearable sensors has contributed to the definition of the Internet of Medical Things (IoMT), which is revolutionizing the way healthcare is tackled worldwide. Data produced by wearable sensors continuously grow and could be spread among clinical centers, hospitals, research labs, yielding to a Big Data management problem. In this paper we propose a technological and architectural solution, based on Open Source big data technologies to perform real-time analysis of wearable sensor data streams. The proposed architecture is composed of four distinct layers: a sensing layer, a pre-processing layer (Raspberry Pi), a cluster processing layer (Kafka’s broker and Flink’s mini-cluster) and a persistence layer (Cassandra database). A performance evaluation of each layer has been carried out by considering CPU and memory usage for accomplishing a simple anomaly detection task using the REALDISP dataset
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-04-01
2019
2019-01-17
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/127096
https://dx.doi.org/10.1016/j.future.2018.10.058
url https://hdl.handle.net/2117/127096
https://dx.doi.org/10.1016/j.future.2018.10.058
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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