A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks

Detalhes bibliográficos
Autor(a) principal: Alves, Pedro
Data de Publicação: 2021
Outros Autores: Saraiva, Thaina, Barandas, Marilia, Duarte, David, Moreira, Dinis, Santos, Ricardo, Leonardo, Ricardo, Gamboa, Hugo, Vieira, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132907
Resumo: POCI-01-0247-FEDER-033479
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spelling A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile NetworksIndoor outdoor detectionlong term evolutionmachine learning algorithmsmeasurement campaignsnetwork tracessmartphoneComputer Science(all)Materials Science(all)Engineering(all)SDG 11 - Sustainable Cities and CommunitiesPOCI-01-0247-FEDER-033479The ability to locate users and estimate traffic in mobile networks is still one of the major challenges when it comes to planning and optimizing the networks. Since indoor location is not always possible or precise, having the ability to distinguish indoor from outdoor traffic can be a valuable alternative and/or improvement. In this paper, two different machine learning algorithms are presented to classify a user's environment, whether indoor or outdoor, using only data from a Long Term Evolution (LTE) network. To test both algorithms, two different measurement campaigns were done. Both campaigns used a smartphone to gather data from the user's side. The first measurement campaign was done across 6 different cities, ranging from small rural areas to large urban environments, while the second was only done on a large urban city. On the second campaign, Network Traces (NT) data was also collected from the network side. The first algorithm consists on a Random Forest (RF) and the second relies on a Long Short Term Memory (LSTM), thus covering both more traditional machine learning and deep learning approaches. The results varied from 0.75 to 0.91 on the F1-Score, depending on the validation strategy, showing promising results.DF – Departamento de FísicaLIBPhys-UNLRUNAlves, PedroSaraiva, ThainaBarandas, MariliaDuarte, DavidMoreira, DinisSantos, RicardoLeonardo, RicardoGamboa, HugoVieira, Pedro2022-02-14T23:30:02Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/132907engPURE: 36763220https://doi.org/10.1109/ACCESS.2021.3130429info: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:RCAAP2024-05-22T17:59:26Zoai:run.unl.pt:10362/132907Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:59:26Repositório 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 A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
title A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
spellingShingle A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
Alves, Pedro
Indoor outdoor detection
long term evolution
machine learning algorithms
measurement campaigns
network traces
smartphone
Computer Science(all)
Materials Science(all)
Engineering(all)
SDG 11 - Sustainable Cities and Communities
title_short A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
title_full A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
title_fullStr A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
title_full_unstemmed A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
title_sort A Novel Approach for User Equipment Indoor/Outdoor Classification in Mobile Networks
author Alves, Pedro
author_facet Alves, Pedro
Saraiva, Thaina
Barandas, Marilia
Duarte, David
Moreira, Dinis
Santos, Ricardo
Leonardo, Ricardo
Gamboa, Hugo
Vieira, Pedro
author_role author
author2 Saraiva, Thaina
Barandas, Marilia
Duarte, David
Moreira, Dinis
Santos, Ricardo
Leonardo, Ricardo
Gamboa, Hugo
Vieira, Pedro
author2_role author
author
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 Alves, Pedro
Saraiva, Thaina
Barandas, Marilia
Duarte, David
Moreira, Dinis
Santos, Ricardo
Leonardo, Ricardo
Gamboa, Hugo
Vieira, Pedro
dc.subject.por.fl_str_mv Indoor outdoor detection
long term evolution
machine learning algorithms
measurement campaigns
network traces
smartphone
Computer Science(all)
Materials Science(all)
Engineering(all)
SDG 11 - Sustainable Cities and Communities
topic Indoor outdoor detection
long term evolution
machine learning algorithms
measurement campaigns
network traces
smartphone
Computer Science(all)
Materials Science(all)
Engineering(all)
SDG 11 - Sustainable Cities and Communities
description POCI-01-0247-FEDER-033479
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
2022-02-14T23:30:02Z
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/10362/132907
url http://hdl.handle.net/10362/132907
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 36763220
https://doi.org/10.1109/ACCESS.2021.3130429
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
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 mluisa.alvim@gmail.com
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