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Resilient wireless sensor actor networks through multi-objective self-adaptation

2026Artigo científico Acesso aberto
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datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
datacite.subject.sdg13:Ação Climática
dc.contributor.authorGomes, Ruben
dc.contributor.authorCorreia, Noélia
dc.date.accessioned2026-03-05T10:26:45Z
dc.date.available2026-03-05T10:26:45Z
dc.date.issued2026
dc.description.abstractWireless Sensor Actor Networks (WSAN) are a key enabler of Internet of Things applications that demand timely and reliable data exchange under dynamic conditions. Among the various domains that benefit from these networks, precision agriculture stands out, demanding adaptive strategies for effective monitoring and control. This study proposes a reinforcement learning approach that leverages the Operationalization construct of the Self-Orchestrated Web of Things (SOrWoT) framework to enhance the adaptability of Things’ internal operations. A problem is formulated as a Markov Decision Process, and a Deep Q-Learning agent is trained in a custom simulation environment to identify the most suitable Operationalizations for optimizing data accuracy and latency, under changing conditions and communication failures. The results show that during normal operation the agent favored parallel sensor data averaging to minimize read error, but after an actor failure and the consequent increase in sensor-to-actor distances, it adapted by prioritizing latency through faster Operationalization choices. Sensitivity analyses further confirmed the agent’s ability to adjust policies in response to partial failures, and to shifts in the relative importance of latency versus accuracy. These findings demonstrate that reinforcement learning can autonomously optimize WSAN performance, contributing to resilient and self-adaptive systems.eng
dc.description.sponsorshipUI/BD/152864/2022
dc.identifier.doi10.1109/tccn.2026.3656393
dc.identifier.eissn2372-2045
dc.identifier.issn2332-7731
dc.identifier.urihttp://hdl.handle.net/10400.1/28333
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relation.ispartofIEEE Transactions on Cognitive Communications and Networking
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectInternet of things
dc.subjectWireless sensor and actor networks
dc.subjectOptimization
dc.subjectDeep reinforcement learning
dc.titleResilient wireless sensor actor networks through multi-objective self-adaptationeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/00631/2020
oaire.awardNumberUIDP/00631/2020
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00631%2F2020/PT
oaire.citation.titleIEEE Transactions on Cognitive Communications and Networking
oaire.citation.volume12
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameGomes
person.familyNameCorreia
person.givenNameRuben
person.givenNameNoélia
person.identifierR-000-DJV
person.identifier.ciencia-idDD19-1F35-B804
person.identifier.orcid0000-0002-0562-2955
person.identifier.orcid0000-0001-7051-7193
person.identifier.ridM-3554-2013
person.identifier.scopus-author-id8411596100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication32214c78-dc9e-4610-8887-2d84b587f537
relation.isAuthorOfPublicationfdbe5057-0478-46cd-9506-caa73ea79d9f
relation.isAuthorOfPublication.latestForDiscovery32214c78-dc9e-4610-8887-2d84b587f537
relation.isProjectOfPublication6c1217d9-1340-45e8-91d8-e75348854f62
relation.isProjectOfPublication77b70459-1e8c-4a6c-9856-58860aaddb6b
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