Data-driven leak detection and localization using LPWAN and Deep Learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/MetroInd4.0IoT54413.2022.9831619 http://hdl.handle.net/11449/245080 |
Resumo: | Management of water resources is a big challenge that draws the attention of global initiatives such as the Sustainable Development Objectives of the United Nations. The technological paradigm of the Internet of Things (IoT) provides the potential to enable Smart Cities, which emphasize rational consumption and waste reduction. This work proposes a system to monitor and identify leakages on Water Distribution Networks (WDNs). The monitoring devices must operate in Low-Power Wide Area Networks (LPWAN), networks that enable low power consumption at the cost of limited data throughput. A case study WDN was created on a software environment for data collection in various operation scenarios, including leakages in different locations. The obtained data sets were analyzed through data inference techniques to identify separable classes or features. Then, a Deep Learning algorithm was used to estimate the probable location of leaks in the WDN. The results obtained in the proposed case study indicate that the Deep Learning approach is a viable methodology to identify and locate leakages, despite the limited data throughput from LPWAN technologies. |
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Repositório Institucional da UNESP |
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Data-driven leak detection and localization using LPWAN and Deep LearningLeak detectionInternet of ThingsSmart CitiesDeep LearningGraph Neural NetworksManagement of water resources is a big challenge that draws the attention of global initiatives such as the Sustainable Development Objectives of the United Nations. The technological paradigm of the Internet of Things (IoT) provides the potential to enable Smart Cities, which emphasize rational consumption and waste reduction. This work proposes a system to monitor and identify leakages on Water Distribution Networks (WDNs). The monitoring devices must operate in Low-Power Wide Area Networks (LPWAN), networks that enable low power consumption at the cost of limited data throughput. A case study WDN was created on a software environment for data collection in various operation scenarios, including leakages in different locations. The obtained data sets were analyzed through data inference techniques to identify separable classes or features. Then, a Deep Learning algorithm was used to estimate the probable location of leaks in the WDN. The results obtained in the proposed case study indicate that the Deep Learning approach is a viable methodology to identify and locate leakages, despite the limited data throughput from LPWAN technologies.Conselho Nacional de Desenvolvimento Cient�fico e Tecnol�gico (CNPq)Sao Paulo State Univ UNESP, Sorocaba, BrazilSao Paulo State Univ UNESP, Sorocaba, BrazilCNPq: 142383/2019-8CNPq: 303967/2021-8IeeeUniversidade Estadual Paulista (UNESP)Rolle, Rodrigo P. [UNESP]Monteiro, Lucas N. [UNESP]Tomazini, Lucas R. [UNESP]Godoy, Eduardo P. [UNESP]IEEE2023-07-29T11:36:43Z2023-07-29T11:36:43Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject403-407http://dx.doi.org/10.1109/MetroInd4.0IoT54413.2022.9831619Proceedings of 2022 IEEE International Workshop on Metrology for Industry 4.0 & Iot (IEEE Metroind4.0&iot). New York: IEEE, p. 403-407, 2022.http://hdl.handle.net/11449/24508010.1109/MetroInd4.0IoT54413.2022.9831619WOS:000855570300073Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings Of 2022 Ieee International Workshop On Metrology For Industry 4.0 & Iot (ieee Metroind4.0&iot)info:eu-repo/semantics/openAccess2023-07-29T11:36:43Zoai:repositorio.unesp.br:11449/245080Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T11:36:43Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Data-driven leak detection and localization using LPWAN and Deep Learning |
title |
Data-driven leak detection and localization using LPWAN and Deep Learning |
spellingShingle |
Data-driven leak detection and localization using LPWAN and Deep Learning Rolle, Rodrigo P. [UNESP] Leak detection Internet of Things Smart Cities Deep Learning Graph Neural Networks |
title_short |
Data-driven leak detection and localization using LPWAN and Deep Learning |
title_full |
Data-driven leak detection and localization using LPWAN and Deep Learning |
title_fullStr |
Data-driven leak detection and localization using LPWAN and Deep Learning |
title_full_unstemmed |
Data-driven leak detection and localization using LPWAN and Deep Learning |
title_sort |
Data-driven leak detection and localization using LPWAN and Deep Learning |
author |
Rolle, Rodrigo P. [UNESP] |
author_facet |
Rolle, Rodrigo P. [UNESP] Monteiro, Lucas N. [UNESP] Tomazini, Lucas R. [UNESP] Godoy, Eduardo P. [UNESP] IEEE |
author_role |
author |
author2 |
Monteiro, Lucas N. [UNESP] Tomazini, Lucas R. [UNESP] Godoy, Eduardo P. [UNESP] IEEE |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Rolle, Rodrigo P. [UNESP] Monteiro, Lucas N. [UNESP] Tomazini, Lucas R. [UNESP] Godoy, Eduardo P. [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Leak detection Internet of Things Smart Cities Deep Learning Graph Neural Networks |
topic |
Leak detection Internet of Things Smart Cities Deep Learning Graph Neural Networks |
description |
Management of water resources is a big challenge that draws the attention of global initiatives such as the Sustainable Development Objectives of the United Nations. The technological paradigm of the Internet of Things (IoT) provides the potential to enable Smart Cities, which emphasize rational consumption and waste reduction. This work proposes a system to monitor and identify leakages on Water Distribution Networks (WDNs). The monitoring devices must operate in Low-Power Wide Area Networks (LPWAN), networks that enable low power consumption at the cost of limited data throughput. A case study WDN was created on a software environment for data collection in various operation scenarios, including leakages in different locations. The obtained data sets were analyzed through data inference techniques to identify separable classes or features. Then, a Deep Learning algorithm was used to estimate the probable location of leaks in the WDN. The results obtained in the proposed case study indicate that the Deep Learning approach is a viable methodology to identify and locate leakages, despite the limited data throughput from LPWAN technologies. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-07-29T11:36:43Z 2023-07-29T11:36:43Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/MetroInd4.0IoT54413.2022.9831619 Proceedings of 2022 IEEE International Workshop on Metrology for Industry 4.0 & Iot (IEEE Metroind4.0&iot). New York: IEEE, p. 403-407, 2022. http://hdl.handle.net/11449/245080 10.1109/MetroInd4.0IoT54413.2022.9831619 WOS:000855570300073 |
url |
http://dx.doi.org/10.1109/MetroInd4.0IoT54413.2022.9831619 http://hdl.handle.net/11449/245080 |
identifier_str_mv |
Proceedings of 2022 IEEE International Workshop on Metrology for Industry 4.0 & Iot (IEEE Metroind4.0&iot). New York: IEEE, p. 403-407, 2022. 10.1109/MetroInd4.0IoT54413.2022.9831619 WOS:000855570300073 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings Of 2022 Ieee International Workshop On Metrology For Industry 4.0 & Iot (ieee Metroind4.0&iot) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
403-407 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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