Machine learning for the dynamic positioning of UAVs for extended connectivity
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/10400.21/13834 |
Resumo: | Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed. |
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Machine learning for the dynamic positioning of UAVs for extended connectivityUnmanned aerial vehicleUAV positioningMachine learningWireless communicationsUnmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.MDPIRCIPLOliveira, FranciscoLuís, MiguelSargento, Susana2021-10-07T13:09:20Z2021-07-052021-07-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/13834engOLIVEIRA, Francisco; LUÍS, Miguel; SARGENTO, Susana – Machine learning for the dynamic positioning of UAVs for extended connectivity. Sensors. eISSN 1424-8220. Vol. 21, N.º 13 (2021), pp. 1-2210.3390/s211346181424-8220info: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:RCAAP2023-08-03T10:09:12Zoai:repositorio.ipl.pt:10400.21/13834Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:21:42.782901Repositório 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 |
Machine learning for the dynamic positioning of UAVs for extended connectivity |
title |
Machine learning for the dynamic positioning of UAVs for extended connectivity |
spellingShingle |
Machine learning for the dynamic positioning of UAVs for extended connectivity Oliveira, Francisco Unmanned aerial vehicle UAV positioning Machine learning Wireless communications |
title_short |
Machine learning for the dynamic positioning of UAVs for extended connectivity |
title_full |
Machine learning for the dynamic positioning of UAVs for extended connectivity |
title_fullStr |
Machine learning for the dynamic positioning of UAVs for extended connectivity |
title_full_unstemmed |
Machine learning for the dynamic positioning of UAVs for extended connectivity |
title_sort |
Machine learning for the dynamic positioning of UAVs for extended connectivity |
author |
Oliveira, Francisco |
author_facet |
Oliveira, Francisco Luís, Miguel Sargento, Susana |
author_role |
author |
author2 |
Luís, Miguel Sargento, Susana |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Oliveira, Francisco Luís, Miguel Sargento, Susana |
dc.subject.por.fl_str_mv |
Unmanned aerial vehicle UAV positioning Machine learning Wireless communications |
topic |
Unmanned aerial vehicle UAV positioning Machine learning Wireless communications |
description |
Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-07T13:09:20Z 2021-07-05 2021-07-05T00:00:00Z |
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/10400.21/13834 |
url |
http://hdl.handle.net/10400.21/13834 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
OLIVEIRA, Francisco; LUÍS, Miguel; SARGENTO, Susana – Machine learning for the dynamic positioning of UAVs for extended connectivity. Sensors. eISSN 1424-8220. Vol. 21, N.º 13 (2021), pp. 1-22 10.3390/s21134618 1424-8220 |
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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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 |
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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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133488930095104 |