An infrastructure-free magnetic-based indoor positioning system with deep learning

Detalhes bibliográficos
Autor(a) principal: Fernandes, Letícia
Data de Publicação: 2020
Outros Autores: Barandas, Marília, Folgado, Duarte, Leonardo, Ricardo, Santos, Ricardo, Carreiro, André, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/108222
Resumo: POCI-01-0247-FEDER-033479
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spelling An infrastructure-free magnetic-based indoor positioning system with deep learningDeep neural networksFingerprintingIndoor positioning systemsInfrastructure-freeMagnetic fieldSmartphonesAnalytical ChemistryBiochemistryAtomic and Molecular Physics, and OpticsInstrumentationElectrical and Electronic EngineeringPOCI-01-0247-FEDER-033479Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user’s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.DF – Departamento de FísicaLIBPhys-UNLRUNFernandes, LetíciaBarandas, MaríliaFolgado, DuarteLeonardo, RicardoSantos, RicardoCarreiro, AndréGamboa, Hugo2020-12-05T00:28:51Z2020-11-022020-11-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/108222eng1424-8220PURE: 26661418https://doi.org/10.3390/s20226664info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:52:51Zoai:run.unl.pt:10362/108222Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:41:09.002535Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv An infrastructure-free magnetic-based indoor positioning system with deep learning
title An infrastructure-free magnetic-based indoor positioning system with deep learning
spellingShingle An infrastructure-free magnetic-based indoor positioning system with deep learning
Fernandes, Letícia
Deep neural networks
Fingerprinting
Indoor positioning systems
Infrastructure-free
Magnetic field
Smartphones
Analytical Chemistry
Biochemistry
Atomic and Molecular Physics, and Optics
Instrumentation
Electrical and Electronic Engineering
title_short An infrastructure-free magnetic-based indoor positioning system with deep learning
title_full An infrastructure-free magnetic-based indoor positioning system with deep learning
title_fullStr An infrastructure-free magnetic-based indoor positioning system with deep learning
title_full_unstemmed An infrastructure-free magnetic-based indoor positioning system with deep learning
title_sort An infrastructure-free magnetic-based indoor positioning system with deep learning
author Fernandes, Letícia
author_facet Fernandes, Letícia
Barandas, Marília
Folgado, Duarte
Leonardo, Ricardo
Santos, Ricardo
Carreiro, André
Gamboa, Hugo
author_role author
author2 Barandas, Marília
Folgado, Duarte
Leonardo, Ricardo
Santos, Ricardo
Carreiro, André
Gamboa, Hugo
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv DF – Departamento de Física
LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Fernandes, Letícia
Barandas, Marília
Folgado, Duarte
Leonardo, Ricardo
Santos, Ricardo
Carreiro, André
Gamboa, Hugo
dc.subject.por.fl_str_mv Deep neural networks
Fingerprinting
Indoor positioning systems
Infrastructure-free
Magnetic field
Smartphones
Analytical Chemistry
Biochemistry
Atomic and Molecular Physics, and Optics
Instrumentation
Electrical and Electronic Engineering
topic Deep neural networks
Fingerprinting
Indoor positioning systems
Infrastructure-free
Magnetic field
Smartphones
Analytical Chemistry
Biochemistry
Atomic and Molecular Physics, and Optics
Instrumentation
Electrical and Electronic Engineering
description POCI-01-0247-FEDER-033479
publishDate 2020
dc.date.none.fl_str_mv 2020-12-05T00:28:51Z
2020-11-02
2020-11-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/108222
url http://hdl.handle.net/10362/108222
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1424-8220
PURE: 26661418
https://doi.org/10.3390/s20226664
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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