Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/105573
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dc.contributorDepartment of Computingen_US
dc.creatorWu, Den_US
dc.creatorLi, Zen_US
dc.creatorWang, Jen_US
dc.creatorZheng, Yen_US
dc.creatorLi, Men_US
dc.creatorHuang, Qen_US
dc.date.accessioned2024-04-15T07:35:07Z-
dc.date.available2024-04-15T07:35:07Z-
dc.identifier.issn1536-1284en_US
dc.identifier.urihttp://hdl.handle.net/10397/105573-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication D. Wu, Z. Li, J. Wang, Y. Zheng, M. Li and Q. Huang, "Vision and Challenges for Knowledge Centric Networking," in IEEE Wireless Communications, vol. 26, no. 4, pp. 117-123, August 2019 is available at https://doi.org/10.1109/MWC.2019.1800323.en_US
dc.titleVision and challenges for knowledge centric networkingen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationTitle on author’s file: Vision and Challenges for Knowledge Centric Networking (KCN)en_US
dc.identifier.spage117en_US
dc.identifier.epage123en_US
dc.identifier.volume26en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1109/MWC.2019.1800323en_US
dcterms.abstractIn the creation of a smart future information society, IoT and CCN break two key barriers for both front-end sensing and back-end networking. However, we still observe the missing piece of the research that dominates current networking traffic control and system management, for example, lacking the knowledge penetrated into both sensing and networking to glue them holistically. In this article, we propose leveraging emerging machine learning or deep learning techniques to create aspects of knowledge to facilitate the designs. In particular, we can extract knowledge from collected data to facilitate reduced data volume, enhanced system intelligence and interactivity, improved service quality, communication with better controllability and lower cost. We this knowledge-oriented traffic control and networking management paradigm Knowledge Centric Networking (KCN). This article presents the rationale for KCN, its benefits, related works and research opportunities.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE wireless communications, Aug. 2019, v. 26, no. 4, p. 117-123en_US
dcterms.isPartOfIEEE wireless communicationsen_US
dcterms.issued2019-08-
dc.identifier.scopus2-s2.0-85064706882-
dc.identifier.eissn1558-0687en_US
dc.description.validate202402 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberCOMP-0548-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS20981062-
dc.description.oaCategoryGreen (AAM)en_US
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