An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains

Autores
Rodríguez , Carlos David; Riva, Guillermo; Zerbini , Carlos; Ruiz Rosero , Juan; Ramírez González, Gustavo; Corrales , Juan Carlos
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with atraditional fixed-rate approach, while preserving the accuracy of the data.
Fil: Rodríguez, Carlos David. Universidad del Cauca. Departamento de Telemática; Colombia.
Fil: Riva, Guillermo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.
Fil: Zerbini, Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.
Fil: Ruiz Rosero, Juan. Technology Innovation Institute; Emiratos Árabes Unidos.
Fil: Ramírez González, Gustavo. Universidad del Cauca. Departamento de Telemática; Colombia.
Fil: Corrales, Juan Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.
Peer Reviewed
Materia
Internet of things
Energy efficiency
Agricultura value chain
Colombiam coffe
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Attribution-NonCommercial-NoDerivatives 4.0 International
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/14619
Acceder
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oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/14619
network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling An adaptive sampling period approach for management of lo T energy consumption to agricultural value chainsRodríguez , Carlos DavidRiva, GuillermoZerbini , CarlosRuiz Rosero , JuanRamírez González, GustavoCorrales , Juan CarlosInternet of thingsEnergy efficiencyAgricultura value chainColombiam coffeThe Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with atraditional fixed-rate approach, while preserving the accuracy of the data.Fil: Rodríguez, Carlos David. Universidad del Cauca. Departamento de Telemática; Colombia.Fil: Riva, Guillermo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.Fil: Zerbini, Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.Fil: Ruiz Rosero, Juan. Technology Innovation Institute; Emiratos Árabes Unidos.Fil: Ramírez González, Gustavo. Universidad del Cauca. Departamento de Telemática; Colombia.Fil: Corrales, Juan Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.Peer ReviewedMultidisciplinary Digital Publishing Institute.2026-03-02T19:16:35Z2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfhttps://hdl.handle.net/20.500.12272/14619enginfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Rodríguez, Carlos David; Rivas, Guillermo; Zerbini, Carlos; Ruiz Rosero, Juan; Ramírez González, Gustavo; Corrales, Juan Carlos.https://creativecommons.org/licenses/by-nc-nd/4.0/reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:43:54Zoai:ria.utn.edu.ar:20.500.12272/14619instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:43:55.834Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
title An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
spellingShingle An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
Rodríguez , Carlos David
Internet of things
Energy efficiency
Agricultura value chain
Colombiam coffe
title_short An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
title_full An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
title_fullStr An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
title_full_unstemmed An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
title_sort An adaptive sampling period approach for management of lo T energy consumption to agricultural value chains
dc.creator.none.fl_str_mv Rodríguez , Carlos David
Riva, Guillermo
Zerbini , Carlos
Ruiz Rosero , Juan
Ramírez González, Gustavo
Corrales , Juan Carlos
author Rodríguez , Carlos David
author_facet Rodríguez , Carlos David
Riva, Guillermo
Zerbini , Carlos
Ruiz Rosero , Juan
Ramírez González, Gustavo
Corrales , Juan Carlos
author_role author
author2 Riva, Guillermo
Zerbini , Carlos
Ruiz Rosero , Juan
Ramírez González, Gustavo
Corrales , Juan Carlos
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Internet of things
Energy efficiency
Agricultura value chain
Colombiam coffe
topic Internet of things
Energy efficiency
Agricultura value chain
Colombiam coffe
dc.description.none.fl_txt_mv The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with atraditional fixed-rate approach, while preserving the accuracy of the data.
Fil: Rodríguez, Carlos David. Universidad del Cauca. Departamento de Telemática; Colombia.
Fil: Riva, Guillermo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.
Fil: Zerbini, Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.
Fil: Ruiz Rosero, Juan. Technology Innovation Institute; Emiratos Árabes Unidos.
Fil: Ramírez González, Gustavo. Universidad del Cauca. Departamento de Telemática; Colombia.
Fil: Corrales, Juan Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Grupo de Investigación y Transferencia en Electrónica Avanzada; Argentina.
Peer Reviewed
description The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with atraditional fixed-rate approach, while preserving the accuracy of the data.
publishDate 2021
dc.date.none.fl_str_mv 2021
2026-03-02T19:16:35Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12272/14619
url https://hdl.handle.net/20.500.12272/14619
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Rodríguez, Carlos David; Rivas, Guillermo; Zerbini, Carlos; Ruiz Rosero, Juan; Ramírez González, Gustavo; Corrales, Juan Carlos.
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Rodríguez, Carlos David; Rivas, Guillermo; Zerbini, Carlos; Ruiz Rosero, Juan; Ramírez González, Gustavo; Corrales, Juan Carlos.
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv pdf
application/pdf
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:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1877230866910937088
score 13.265058

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