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A2PC: augmented advantage pointer-critic model for low latency on mobile IoT with edge computing

2024Journal article Open access
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dc.contributor.authorCarvalho, Rodrigo
dc.contributor.authorAl-Tam, Faroq
dc.contributor.authorCorreia, Noélia
dc.date.accessioned2025-05-27T13:29:04Z
dc.date.available2025-05-27T13:29:04Z
dc.date.issued2024
dc.description.abstractAs a growing trend, edge computing infrastructures are starting to be integrated with Internet of Things (IoT) systems to facilitate time-critical applications. These systems often require the processing of data with limited usefulness in time, so the edge becomes vital in the development of such reactive IoT applications with real-time requirements. Although different architectural designs will always have advantages and disadvantages, mobile gateways appear to be particularly relevant in enabling this integration with the edge, particularly in the context of wide area networks with occasional data generation. In these scenarios, mobility planning is necessary, as aspects of the technology need to be aligned with the temporal needs of an application. The nature of this planning problem makes cutting-edge deep reinforcement learning (DRL) techniques useful in solving pertinent issues, such as having to deal with multiple dimensions in the action space while aiming for optimum levels of system performance. This article presents a novel scalable DRL model that incorporates a pointer-network (Ptr-Net) and an actor-critic algorithm to handle complex action spaces. The model synchronously determines the gateway location and visit time. Ultimately, the gateways are able to attain high-quality trajectory planning with reduced latency.eng
dc.identifier.doi10.1109/tmlcn.2024.3501217
dc.identifier.issn2831-316X
dc.identifier.urihttp://hdl.handle.net/10400.1/27175
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.ispartofIEEE Transactions on Machine Learning in Communications and Networking
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLogic gates
dc.subjectLoRaWAN
dc.subjectInternet of Things
dc.subjectPlanning
dc.subjectMachine learning
dc.subjectData collection
dc.subjectComputer architecture
dc.subjectProposals
dc.subjectOptimization
dc.subjectTrajectory planning
dc.subjectAction branching
dc.subjectIoTlong-range wide-area network
dc.subjectMobilitypointer networks
dc.subjectReinforcement learning
dc.titleA2PC: augmented advantage pointer-critic model for low latency on mobile IoT with edge computingeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage16
oaire.citation.startPage1
oaire.citation.titleIEEE Transactions on Machine Learning in Communications and Networking
oaire.citation.volume3
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCarvalho
person.familyNameAl-Tam
person.familyNameCorreia
person.givenNameRodrigo
person.givenNameFaroq
person.givenNameNoélia
person.identifierR-00G-A33
person.identifierR-000-DJV
person.identifier.ciencia-id2515-AFE3-525F
person.identifier.ciencia-idDD19-1F35-B804
person.identifier.orcid0000-0001-9507-1327
person.identifier.orcid0000-0001-9718-2039
person.identifier.orcid0000-0001-7051-7193
person.identifier.ridK-7031-2016
person.identifier.ridM-3554-2013
person.identifier.scopus-author-id55246034700
person.identifier.scopus-author-id8411596100
relation.isAuthorOfPublication125c8875-49e7-4171-b2c9-38e5e4ef2f90
relation.isAuthorOfPublication15ac97f4-a867-462d-9fc6-0a47bb2919d3
relation.isAuthorOfPublicationfdbe5057-0478-46cd-9506-caa73ea79d9f
relation.isAuthorOfPublication.latestForDiscoveryfdbe5057-0478-46cd-9506-caa73ea79d9f

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