A novel approach for user equipment indoor/outdoor classification in mobile networks
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/14783 |
Resumo: | The 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. |
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A novel approach for user equipment indoor/outdoor classification in mobile networksIndoor outdoor detectionMachine learning algorithmsLong term evolutionMeasurement campaignsSmartphoneNetwork tracesThe 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.IEEERCIPLAlves, PedroSaraiva, ThaínaBarandas, MaríliaDuarte, DavidMoreira, DinisSantos, RicardoLeonardo, RicardoGamboa, HugoVieira, Pedro2022-07-06T08:03:09Z2021-11-232021-11-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/14783engALVES, Pedro; [et al] – A novel approach for user equipment indoor/outdoor classification in mobile networks. IEEE Access. eISSN 2169-3536. Vol. 9 (2021), pp. 162671-162686.10.1109/ACCESS.2021.31304292169-3536info: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:RCAAP2023-08-03T10:11:25Zoai:repositorio.ipl.pt:10400.21/14783Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:22:31.784433Repositório 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 Machine learning algorithms Long term evolution Measurement campaigns Smartphone Network traces |
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, Thaína Barandas, Marília Duarte, David Moreira, Dinis Santos, Ricardo Leonardo, Ricardo Gamboa, Hugo Vieira, Pedro |
author_role |
author |
author2 |
Saraiva, Thaína Barandas, Marília 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 |
RCIPL |
dc.contributor.author.fl_str_mv |
Alves, Pedro Saraiva, Thaína Barandas, Marília Duarte, David Moreira, Dinis Santos, Ricardo Leonardo, Ricardo Gamboa, Hugo Vieira, Pedro |
dc.subject.por.fl_str_mv |
Indoor outdoor detection Machine learning algorithms Long term evolution Measurement campaigns Smartphone Network traces |
topic |
Indoor outdoor detection Machine learning algorithms Long term evolution Measurement campaigns Smartphone Network traces |
description |
The 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-23 2021-11-23T00:00:00Z 2022-07-06T08:03:09Z |
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/10400.21/14783 |
url |
http://hdl.handle.net/10400.21/14783 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ALVES, Pedro; [et al] – A novel approach for user equipment indoor/outdoor classification in mobile networks. IEEE Access. eISSN 2169-3536. Vol. 9 (2021), pp. 162671-162686. 10.1109/ACCESS.2021.3130429 2169-3536 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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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1799133497513738240 |