Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings

This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smalle...

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Detalles Bibliográficos
Autores: Cano-Ortega, Antonio, Sánchez-Sutil, Francisco
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/6117
Acceso en línea:http://dx.doi.org/10.3390/en13030517
https://www.mdpi.com/1996-1073/13/3/517
https://hdl.handle.net/10953/6117
Access Level:acceso abierto
Palabra clave:Energy measurement device for dwellings (EMDD)
Gateway LoRa network monitor (GLNM)
Artificial bee colony (ABC)
Load profiles
LoRa network
Cloud computing
621.35
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oai_identifier_str oai:ruja.ujaen.es:10953/6117
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repository_id_str
spelling Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in DwellingsCano-Ortega, AntonioSánchez-Sutil, FranciscoEnergy measurement device for dwellings (EMDD)Gateway LoRa network monitor (GLNM)Artificial bee colony (ABC)Load profilesLoRa networkCloud computing621.35This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffc, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.MDPI202520252020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://dx.doi.org/10.3390/en13030517https://www.mdpi.com/1996-1073/13/3/517https://hdl.handle.net/10953/6117reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésEnergiesAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/61172026-06-24T12:41:07Z
dc.title.none.fl_str_mv Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
spellingShingle Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
Cano-Ortega, Antonio
Energy measurement device for dwellings (EMDD)
Gateway LoRa network monitor (GLNM)
Artificial bee colony (ABC)
Load profiles
LoRa network
Cloud computing
621.35
title_short Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_full Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_fullStr Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_full_unstemmed Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
title_sort Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
dc.creator.none.fl_str_mv Cano-Ortega, Antonio
Sánchez-Sutil, Francisco
author Cano-Ortega, Antonio
author_facet Cano-Ortega, Antonio
Sánchez-Sutil, Francisco
author_role author
author2 Sánchez-Sutil, Francisco
author2_role author
dc.subject.none.fl_str_mv Energy measurement device for dwellings (EMDD)
Gateway LoRa network monitor (GLNM)
Artificial bee colony (ABC)
Load profiles
LoRa network
Cloud computing
621.35
topic Energy measurement device for dwellings (EMDD)
Gateway LoRa network monitor (GLNM)
Artificial bee colony (ABC)
Load profiles
LoRa network
Cloud computing
621.35
description This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffc, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.
publishDate 2020
dc.date.none.fl_str_mv 2020
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://dx.doi.org/10.3390/en13030517
https://www.mdpi.com/1996-1073/13/3/517
https://hdl.handle.net/10953/6117
url http://dx.doi.org/10.3390/en13030517
https://www.mdpi.com/1996-1073/13/3/517
https://hdl.handle.net/10953/6117
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Energies
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv 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 MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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