Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms

The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diver...

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Detalhes bibliográficos
Autores: Lovón Melgarejo, Jesús, Huarcaya Canal, Oscar, Orozco Barbosa, Luis, García Varea, Ismael, Castillo-Cara, Manuel
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/12446
Acesso em linha:https://hdl.handle.net/20.500.14468/12446
Access Level:acceso abierto
Palavra-chave:Indoor positioning
fingerprinting
Bluetooth
classification model
signal processing
received signal strength indication
multipath fading
transmission power
benchmark
metaheuristic optimization algorithms
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spelling Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization MechanismsLovón Melgarejo, JesúsHuarcaya Canal, OscarOrozco Barbosa, LuisGarcía Varea, IsmaelCastillo-Cara, ManuelIndoor positioningfingerprintingBluetoothclassification modelsignal processingreceived signal strength indicationmultipath fadingtransmission powerbenchmarkmetaheuristic optimization algorithmsThe development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.Institute of Electrical and Electronics Engineerse-Spacio UNED20242024-05-2020192019-02-1520192019-02-15journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/12446reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0oai:e-spacio.uned.es:20.500.14468/124462026-06-06T12:38:31Z
dc.title.none.fl_str_mv Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
title Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
spellingShingle Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
Lovón Melgarejo, Jesús
Indoor positioning
fingerprinting
Bluetooth
classification model
signal processing
received signal strength indication
multipath fading
transmission power
benchmark
metaheuristic optimization algorithms
title_short Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
title_full Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
title_fullStr Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
title_full_unstemmed Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
title_sort Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
dc.creator.none.fl_str_mv Lovón Melgarejo, Jesús
Huarcaya Canal, Oscar
Orozco Barbosa, Luis
García Varea, Ismael
Castillo-Cara, Manuel
author Lovón Melgarejo, Jesús
author_facet Lovón Melgarejo, Jesús
Huarcaya Canal, Oscar
Orozco Barbosa, Luis
García Varea, Ismael
Castillo-Cara, Manuel
author_role author
author2 Huarcaya Canal, Oscar
Orozco Barbosa, Luis
García Varea, Ismael
Castillo-Cara, Manuel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv Indoor positioning
fingerprinting
Bluetooth
classification model
signal processing
received signal strength indication
multipath fading
transmission power
benchmark
metaheuristic optimization algorithms
topic Indoor positioning
fingerprinting
Bluetooth
classification model
signal processing
received signal strength indication
multipath fading
transmission power
benchmark
metaheuristic optimization algorithms
description The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-02-15
2019
2019-02-15
2024
2024-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/12446
url https://hdl.handle.net/20.500.14468/12446
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
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 Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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