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Machine learning for the dynamic positioning of UAVs for extended connectivity

2021-07-05Journal article Open access
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dc.contributor.authorOliveira, Francisco
dc.contributor.authorLuís, Miguel
dc.contributor.authorSargento, Susana
dc.date.accessioned2021-10-07T13:09:20Z
dc.date.available2021-10-07T13:09:20Z
dc.date.issued2021-07-05
dc.description.abstractUnmanned 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationOLIVEIRA, 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. 1-22pt_PT
dc.identifier.doi10.3390/s21134618pt_PT
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.21/13834
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationPTDC/EEIROB/28799/2017 - FCT/MECpt_PT
dc.relationUIA03-084 - European Commissionpt_PT
dc.subjectUnmanned aerial vehiclept_PT
dc.subjectUAV positioningpt_PT
dc.subjectMachine learningpt_PT
dc.subjectWireless communicationspt_PT
dc.titleMachine learning for the dynamic positioning of UAVs for extended connectivitypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage22pt_PT
oaire.citation.issue13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume21pt_PT
person.familyNameLuís
person.familyNameSargento
person.givenNameMiguel
person.givenNameSusana
person.identifier.ciencia-id3418-A2F5-3CA4
person.identifier.ciencia-id6C11-2B5E-FACB
person.identifier.orcid0000-0003-3488-2462
person.identifier.orcid0000-0001-8761-8281
person.identifier.scopus-author-id36164286400
person.identifier.scopus-author-id6603312796
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication2eadcc1c-ff03-403a-9c42-f2e45f0fd528
relation.isAuthorOfPublicatione0814076-e21c-4aab-beeb-0a06d3b01e24
relation.isAuthorOfPublication.latestForDiscoverye0814076-e21c-4aab-beeb-0a06d3b01e24

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