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AI-driven, Context-Aware Profiling for 5G and Beyond Networks

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dc.contributor.authorKoursioumpas, Nikolaos
dc.contributor.authorBarmpounakis, Sokratis
dc.contributor.authorStavrakakis, Ioannis
dc.contributor.authorAlonistioti, Nancy
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2023-09-21T15:20:23Z
dc.date.available2023-09-21T15:20:23Z
dc.date.issued2022-06
dc.description.abstractIn the era of Industrial Internet of Things (IIoT) and Industry 4.0, an immense volume of heterogeneous network devices will coexist and contend for shared network resources, in order to satisfy the very challenging IIoT applications, requiring ultra-reliable and ultra-low latency communications. Although novel key enablers, such as Network Slicing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have already offered significant advantages towards more efficient and flexible network and resource management approaches, the particular characteristics of IIoT applications pose additional burdens, mainly due to the complex wireless environments, high number of heterogeneous network devices, sensors, user equipments (UEs), etc., which may stochastically demand and contend for the -often scarce -computing and communication resources of industrial environments. To this end, this paper introduces PRIMATE, a novel, Artificial Intelligence (AI)-driven framework for the profiling of the networking behavior of such UEs, devices, users and things, which is able to operate in conjunction with already standardized or forthcoming, AI-based network resource management processes towards further gains. The novelty and potential of the proposed work lies on the fact that instead of attempting to either predict raw network metrics in a reactive manner, or predict the behavior of specific network entities/devices in an isolated manner, a big data-driven classification approach is introduced, which models the behavior of any network device/user from both a macroscopic, as well as service-specific perspective. The extended evaluation at the last part of this work shows the validity and viability of the proposed framework.en
dc.description.sponsorshipThis work has been partially supported by EC H2020 5GPPP 5Growth project (Grant 856709).en
dc.format.extent13
dc.identifier.bibliographicCitationKoursioumpas, N., Barmpounakis, S., Stavrakakis, I. & Alonistioti, N. (2022). AI-driven, Context-Aware Profiling for 5G and Beyond Networks. IEEE Transactions on Network and Service Management, 19(2), 1036-1048.en
dc.identifier.doihttps://doi.org/10.1109/TNSM.2021.3126948
dc.identifier.issn1932-4537 (Electronic)
dc.identifier.publicationfirstpage1
dc.identifier.publicationfirstpage1036
dc.identifier.publicationissue2
dc.identifier.publicationlastpage12
dc.identifier.publicationlastpage1048
dc.identifier.publicationtitleIEEE Transactions on Network and Service Managementen
dc.identifier.publicationvolume19
dc.identifier.urihttps://hdl.handle.net/10016/34179
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/856709
dc.rights© 2021 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherContext-aware profilingen
dc.subject.otherMachine learningen
dc.subject.otherAI-driven networkingen
dc.subject.other5Gen
dc.subject.otherResource allocationen
dc.titleAI-driven, Context-Aware Profiling for 5G and Beyond Networksen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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