Network Slicing Based Learning Techniques for IoV in 5G and Beyond Networks

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dc.contributor.authorHamdi W.
dc.contributor.authorKsouri C.
dc.contributor.authorBulut H.
dc.contributor.authorMosbah M.
dc.date.accessioned2024-08-31T07:42:33Z
dc.date.available2024-08-31T07:42:33Z
dc.date.issued2024
dc.departmentEge Üniversitesien_US
dc.description.abstractThe effects of transport development on people’s lives are diverse, ranging from economy to tourism, health care, etc. Great progress has been made in this area, which has led to the emergence of the Internet of Vehicles (IoV) concept. The main objective of this concept is to offer a safer and more comfortable travel experience through making available a vast array of applications, by relying on a range of communication technologies including the fifth-generation mobile networks. The proposed applications have personalized Quality of Service (QoS) requirements, which raise new challenging issues for the management and allocation of resources. Currently, this interest has been doubled with the start of the discussion of the sixth-generation mobile networks. In this context, Network Slicing (NS) was presented as one of the key technologies in the 5G architecture to address these challenges. In this article, we try to bring together the effects of NS implications in the Internet of Vehicles field and show the impact on transport development. We begin by reviewing the state of the art of NS in IoV in terms of architecture, types, life cycle, enabling technologies, network parts, and evolution within cellular networks. Then, we discuss the benefits brought by the use of NS in such a dynamic environment, along with the technical challenges. Moreover, we provide a comprehensive review of NS deploying various aspects of Learning Techniques for the Internet of Vehicles. Afterwards, we present Network Slicing utilization in different IoV application scenarios through different domains; terrestrial, aerial, and marine. In addition, we review Vehicle-to-Everything (V2X) datasets as well as existing implementation tools; besides presenting a concise summary of the Network Slicing-related projects that have an impact on IoV. Finally, in order to promote the deployment of Network Slicing in IoV, we provide some directions for future research work. We believe that the survey will be useful for researchers from academia and industry. First, to acquire a holistic vision regarding IoV-based NS realization and identify the challenges hindering it. Second, to understand the progression of IoV powered NS applications in the different fields (terrestrial, aerial, and marine). Finally, to determine the opportunities for using Machine Learning Techniques (MLT), in order to propose their own solutions to foster NS-IoV integration. IEEEen_US
dc.identifier.doi10.1109/COMST.2024.3372083
dc.identifier.endpage1en_US
dc.identifier.issn1553-877X
dc.identifier.scopus2-s2.0-85186965392en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/COMST.2024.3372083
dc.identifier.urihttps://hdl.handle.net/11454/103931
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Communications Surveys and Tutorialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240831_Uen_US
dc.subject5G mobile communicationen_US
dc.subjectInternet of Vehiclesen_US
dc.subjectMachine Learning Techniquesen_US
dc.subjectMarine vehiclesen_US
dc.subjectNetwork Slicingen_US
dc.subjectNetwork slicingen_US
dc.subjectQuality of serviceen_US
dc.subjectResource managementen_US
dc.subjectReviewsen_US
dc.subjectSurveysen_US
dc.titleNetwork Slicing Based Learning Techniques for IoV in 5G and Beyond Networksen_US
dc.typeArticleen_US

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