Cyber-Physical System for Environmental Monitoring Based on Deep Learning

Cyber-physical systems (CPS) constitute a promising paradigm that could fit variousapplications. Monitoring based on the Internet of Things (IoT) has become a research area withnew challenges in which to extract valuable information. This paper proposes a deep learningclassification sound system for...

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Detalles Bibliográficos
Autores: Monedero, Íñigo, Barbancho, Julio, Márquez, Rafael, Beltrán, Juan F.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/245394
Acceso en línea:http://hdl.handle.net/10261/245394
Access Level:acceso abierto
Palabra clave:Convolutional neural networks
Deep learning
Machine learning
Cyber-physical systems
Passive active monitoring
Internet of Things
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spelling Cyber-Physical System for Environmental Monitoring Based on Deep LearningMonedero, ÍñigoBarbancho, JulioMárquez, RafaelBeltrán, Juan F.Convolutional neural networksDeep learningMachine learningCyber-physical systemsPassive active monitoringInternet of ThingsCyber-physical systems (CPS) constitute a promising paradigm that could fit variousapplications. Monitoring based on the Internet of Things (IoT) has become a research area withnew challenges in which to extract valuable information. This paper proposes a deep learningclassification sound system for execution over CPS. This system is based on convolutional neuralnetworks (CNNs) and is focused on the different types of vocalization of two species of anurans.CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate toolfor the classification of environmental sounds. The classification results obtained are excellent (97.53%overall accuracy) and can be considered a very promising use of the system for classifying otherbiological acoustic targets as well as analyzing biodiversity indices in the natural environment. Thepaper concludes by observing that the execution of this type of CNN, involving low-cost and reducedcomputing resources, are feasible for monitoring extensive natural areas. The use of CPS enablesflexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.This project has been supported by the Doñana Biological Station (CSIC, Spain) under the title SCENA-RBD (2020/20), TEMPURA (CGL2005-00092/BOS), ACURA (CGL2008-04814-C02) and TATANKA (CGL2011-25062)Peer reviewedMultidisciplinary Digital Publishing InstituteCSIC - Estación Biológica de Doñana (EBD)Ministerio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202120212021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/245394reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://www.mdpi.com/1424-8220/21/11/3655Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2453942026-05-22T06:33:51Z
dc.title.none.fl_str_mv Cyber-Physical System for Environmental Monitoring Based on Deep Learning
title Cyber-Physical System for Environmental Monitoring Based on Deep Learning
spellingShingle Cyber-Physical System for Environmental Monitoring Based on Deep Learning
Monedero, Íñigo
Convolutional neural networks
Deep learning
Machine learning
Cyber-physical systems
Passive active monitoring
Internet of Things
title_short Cyber-Physical System for Environmental Monitoring Based on Deep Learning
title_full Cyber-Physical System for Environmental Monitoring Based on Deep Learning
title_fullStr Cyber-Physical System for Environmental Monitoring Based on Deep Learning
title_full_unstemmed Cyber-Physical System for Environmental Monitoring Based on Deep Learning
title_sort Cyber-Physical System for Environmental Monitoring Based on Deep Learning
dc.creator.none.fl_str_mv Monedero, Íñigo
Barbancho, Julio
Márquez, Rafael
Beltrán, Juan F.
author Monedero, Íñigo
author_facet Monedero, Íñigo
Barbancho, Julio
Márquez, Rafael
Beltrán, Juan F.
author_role author
author2 Barbancho, Julio
Márquez, Rafael
Beltrán, Juan F.
author2_role author
author
author
dc.contributor.none.fl_str_mv CSIC - Estación Biológica de Doñana (EBD)
Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Convolutional neural networks
Deep learning
Machine learning
Cyber-physical systems
Passive active monitoring
Internet of Things
topic Convolutional neural networks
Deep learning
Machine learning
Cyber-physical systems
Passive active monitoring
Internet of Things
description Cyber-physical systems (CPS) constitute a promising paradigm that could fit variousapplications. Monitoring based on the Internet of Things (IoT) has become a research area withnew challenges in which to extract valuable information. This paper proposes a deep learningclassification sound system for execution over CPS. This system is based on convolutional neuralnetworks (CNNs) and is focused on the different types of vocalization of two species of anurans.CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate toolfor the classification of environmental sounds. The classification results obtained are excellent (97.53%overall accuracy) and can be considered a very promising use of the system for classifying otherbiological acoustic targets as well as analyzing biodiversity indices in the natural environment. Thepaper concludes by observing that the execution of this type of CNN, involving low-cost and reducedcomputing resources, are feasible for monitoring extensive natural areas. The use of CPS enablesflexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/245394
url http://hdl.handle.net/10261/245394
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/1424-8220/21/11/3655
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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