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Hybrid B5G-DTN architecture with federated learning for contextual communication offloading

datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorJesús-Azabal, Manuel
dc.contributor.authorZheng, Meichun
dc.contributor.authorSoares, V.N.G.J.
dc.date.accessioned2025-09-10T15:23:58Z
dc.date.available2025-09-10T15:23:58Z
dc.date.issued2025
dc.description.abstractIn dense urban environments and large-scale events, Internet infrastructure often becomes overloaded due to high communication demand. Many of these communications are local and short-lived, exchanged between users in close proximity but still relying on global infrastructure, leading to unnecessary network stress. In this context, delay-tolerant networks (DTNs) offer an alternative by enabling device-to-device (D2D) communication without requiring constant connectivity. However, DTNs face significant challenges in routing due to unpredictable node mobility and intermittent contacts, making reliable delivery difficult. Considering these challenges, this paper presents a hybrid Beyond 5G (B5G) DTN architecture to provide private context-aware routing in dense scenarios. In this proposal, dynamic contextual notifications are shared among relevant local nodes, combining federated learning (FL) and edge artificial intelligence (AI) to estimate the optimal relay paths based on variables such as mobility patterns and contact history. To keep the local FL models updated with the evolving context, edge nodes, integrated as part of the B5G architecture, act as coordinating entities for model aggregation and redistribution. The proposed architecture has been implemented and evaluated in simulation testbeds, studying its performance and sensibility to the node density in a realistic scenario. In high-density scenarios, the architecture outperforms state-of-the-art routing schemes, achieving an average delivery probability of 77%, with limited latency and overhead, demonstrating relevant technical viability.eng
dc.identifier.citationJESÚS-AZABAL, M. ; ZHENG, M. ; SOARES, V.N.G.J. (2025) - Hybrid B5G-DTN Architecture with Federated Learning for Contextual Communication Offloading. Future Internet. Vol. 17:9, 392. DOI: 10.3390/ fi17090392
dc.identifier.doi10.3390/fi17090392
dc.identifier.issn1999-5903
dc.identifier.urihttp://hdl.handle.net/10400.11/10287
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI AG
dc.relationUID/50008: Instituto de Telecomunicações
dc.relation.ispartofFuture Internet
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBeyond 5G
dc.subjectDelay-tolerant networks
dc.subjectFederated learning
dc.subjectCommunication offloading
dc.subjectEdge AI
dc.titleHybrid B5G-DTN architecture with federated learning for contextual communication offloadingeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue9
oaire.citation.titleFuture Internet
oaire.citation.volume17
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.identifiera4GD8aoAAAAJ
person.identifier.ciencia-id5B19-E130-E382
person.identifier.orcid0000-0002-8057-5474
relation.isAuthorOfPublicationa17d4ff5-1ff3-4dcc-b180-319e7ff3961d
relation.isAuthorOfPublication.latestForDiscoverya17d4ff5-1ff3-4dcc-b180-319e7ff3961d

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