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Mobility planning of LoRa gateways for edge storage of IoT data

2023-02Journal article Restricted access
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dc.contributor.authorCarvalho, Rodrigo
dc.contributor.authorCorreia, Noélia
dc.contributor.authorAl-Tam, Faroq
dc.date.accessioned2023-02-15T10:34:03Z
dc.date.available2023-02-15T10:34:03Z
dc.date.issued2023-02
dc.description.abstractLoRaWAN is now a leading technology in IoT developments due to its low power consumption and simple deployment features. Despite being termed a long-distance technology, its coverage is often hampered by the harsh propagation environment. In such cases, a very cost-effective solution is to use mobile gateways for both data collection and delivery at specific places of the edge, avoiding the need for cellular communication modules. This article addresses the optimization problem of deciding how mobile gateways should move, and which edge devices should be visited (and when) for collected data to be fed into the edge distributed storage, while ensuring data usefulness. This optimization problem of gateway mobility with edge storage is formulated mathematically, and a deep reinforcement learning framework is proposed to solve it. Results show that the proposed framework can achieve near optimal results, in particular for large-dense deployments and not very strict time windows. The proposed deep reinforcement learning solution is able to capture underlying patterns, like device coverage and traffic, without prior knowledge of such information.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.comnet.2022.109521pt_PT
dc.identifier.eissn1872-7069
dc.identifier.urihttp://hdl.handle.net/10400.1/19086
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationMathematical Optimization for clinical DEcision Support and Training
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.subjectLoRaWANpt_PT
dc.subjectIoTpt_PT
dc.subjectMobilitypt_PT
dc.subjectEdgept_PT
dc.subjectReinforcement learningpt_PT
dc.subjectMathematical optimizationpt_PT
dc.titleMobility planning of LoRa gateways for edge storage of IoT datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleMathematical Optimization for clinical DEcision Support and Training
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/647573/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00631%2F2020/PT
oaire.citation.startPage109521pt_PT
oaire.citation.titleComputer Networkspt_PT
oaire.citation.volume221pt_PT
oaire.fundingStreamH2020
oaire.fundingStream6817 - DCRRNI ID
person.familyNameCorreia
person.givenNameNoélia
person.identifierR-000-DJV
person.identifier.ciencia-idDD19-1F35-B804
person.identifier.orcid0000-0001-7051-7193
person.identifier.ridM-3554-2013
person.identifier.scopus-author-id8411596100
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationfdbe5057-0478-46cd-9506-caa73ea79d9f
relation.isAuthorOfPublication.latestForDiscoveryfdbe5057-0478-46cd-9506-caa73ea79d9f
relation.isProjectOfPublication0f06378b-c9ac-4cda-83d7-eda3f3f40ce2
relation.isProjectOfPublication77b70459-1e8c-4a6c-9856-58860aaddb6b
relation.isProjectOfPublication.latestForDiscovery0f06378b-c9ac-4cda-83d7-eda3f3f40ce2

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