Virtual sensor-based proxy for black carbon estimation in IoT platforms

Black carbon (BC) has been under the spotlight of research during the last few years due to its non-regulation, its role in air pollution, and its hazardous effects. Given the high cost of the instrumentation needed to measure BC concentrations, data-driven techniques have been adopted to implement...

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Autores: Ferrer Cid, Pau|||0000-0003-2112-8516, Paredes Ahumada, Juan Antonio, Barceló Ordinas, José María|||0000-0002-9738-2425, García Vidal, Jorge|||0000-0001-5969-1182
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/423815
Acceso en línea:https://hdl.handle.net/2117/423815
https://dx.doi.org/10.1016/j.iot.2024.101284
Access Level:acceso abierto
Palabra clave:Air quality
Proxy
Virtual sensor
Machine learning
Black carbon
Internet of things
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
id ES_26eeecc6bdae7aabca36fcf0a00bc3eb
oai_identifier_str oai:upcommons.upc.edu:2117/423815
network_acronym_str ES
network_name_str España
repository_id_str
spelling Virtual sensor-based proxy for black carbon estimation in IoT platformsFerrer Cid, Pau|||0000-0003-2112-8516Paredes Ahumada, Juan AntonioBarceló Ordinas, José María|||0000-0002-9738-2425García Vidal, Jorge|||0000-0001-5969-1182Air qualityProxyVirtual sensorMachine learningBlack carbonInternet of thingsÀrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèricaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticBlack carbon (BC) has been under the spotlight of research during the last few years due to its non-regulation, its role in air pollution, and its hazardous effects. Given the high cost of the instrumentation needed to measure BC concentrations, data-driven techniques have been adopted to implement proxies that provide BC measurements from other sensor measurements. These sensors may present data quality issues due to maintenance actions, loss of data, or relocation, among others. In this paper, we propose a data-driven proxy model for BC estimation that is powered by a hybrid sensor array, including physical and virtual sensors created from machine learning techniques and governmental air quality monitoring networks. Therefore, the proposed method provides an accurate alternative to traditional data-driven BC proxies in scenarios where some physical sensors are unavailable. The results show how a BC proxy can be partially implemented using virtual sensors, obtaining only an increase in the estimation error of around 4%, allowing the estimation of BC levels even when some physical sensors are absent.This work is supported by Grant PID2022-138155OB-I00 funded by MCIN/ AEI/10.13039/501100011033 and by ‘‘ERDF A way of making Europe’’, CDTI MIG-20221061, by AGAUR regional project 2021SGR-01059, and with the support of Secretaria d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu.Peer ReviewedElsevier20242024-10-0120252025-02-11journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/423815https://dx.doi.org/10.1016/j.iot.2024.101284reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138155OB-I00 TECNICAS BASADAS EN DATOS PARA MEJORAR LA CALIDAD DE LA INFORMACION EN REDES DE NODOS IOTopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4238152026-05-27T15:37:01Z
dc.title.none.fl_str_mv Virtual sensor-based proxy for black carbon estimation in IoT platforms
title Virtual sensor-based proxy for black carbon estimation in IoT platforms
spellingShingle Virtual sensor-based proxy for black carbon estimation in IoT platforms
Ferrer Cid, Pau|||0000-0003-2112-8516
Air quality
Proxy
Virtual sensor
Machine learning
Black carbon
Internet of things
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Virtual sensor-based proxy for black carbon estimation in IoT platforms
title_full Virtual sensor-based proxy for black carbon estimation in IoT platforms
title_fullStr Virtual sensor-based proxy for black carbon estimation in IoT platforms
title_full_unstemmed Virtual sensor-based proxy for black carbon estimation in IoT platforms
title_sort Virtual sensor-based proxy for black carbon estimation in IoT platforms
dc.creator.none.fl_str_mv Ferrer Cid, Pau|||0000-0003-2112-8516
Paredes Ahumada, Juan Antonio
Barceló Ordinas, José María|||0000-0002-9738-2425
García Vidal, Jorge|||0000-0001-5969-1182
author Ferrer Cid, Pau|||0000-0003-2112-8516
author_facet Ferrer Cid, Pau|||0000-0003-2112-8516
Paredes Ahumada, Juan Antonio
Barceló Ordinas, José María|||0000-0002-9738-2425
García Vidal, Jorge|||0000-0001-5969-1182
author_role author
author2 Paredes Ahumada, Juan Antonio
Barceló Ordinas, José María|||0000-0002-9738-2425
García Vidal, Jorge|||0000-0001-5969-1182
author2_role author
author
author
dc.subject.none.fl_str_mv Air quality
Proxy
Virtual sensor
Machine learning
Black carbon
Internet of things
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Air quality
Proxy
Virtual sensor
Machine learning
Black carbon
Internet of things
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description Black carbon (BC) has been under the spotlight of research during the last few years due to its non-regulation, its role in air pollution, and its hazardous effects. Given the high cost of the instrumentation needed to measure BC concentrations, data-driven techniques have been adopted to implement proxies that provide BC measurements from other sensor measurements. These sensors may present data quality issues due to maintenance actions, loss of data, or relocation, among others. In this paper, we propose a data-driven proxy model for BC estimation that is powered by a hybrid sensor array, including physical and virtual sensors created from machine learning techniques and governmental air quality monitoring networks. Therefore, the proposed method provides an accurate alternative to traditional data-driven BC proxies in scenarios where some physical sensors are unavailable. The results show how a BC proxy can be partially implemented using virtual sensors, obtaining only an increase in the estimation error of around 4%, allowing the estimation of BC levels even when some physical sensors are absent.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-10-01
2025
2025-02-11
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/2117/423815
https://dx.doi.org/10.1016/j.iot.2024.101284
url https://hdl.handle.net/2117/423815
https://dx.doi.org/10.1016/j.iot.2024.101284
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138155OB-I00 TECNICAS BASADAS EN DATOS PARA MEJORAR LA CALIDAD DE LA INFORMACION EN REDES DE NODOS IOT
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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 15,812429

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