Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Adaptive LRFU replacement policy for named data network in industrial IoT

Full metadata record
DC Field Value Language
dc.contributor.authorPutra, Made Adi Paramartha-
dc.contributor.authorKim, Dong-Seong-
dc.contributor.authorLee, Jae-Min-
dc.date.accessioned2022-08-16T01:40:05Z-
dc.date.available2022-08-16T01:40:05Z-
dc.date.issued2022-06-
dc.identifier.issn2405-9595-
dc.identifier.issn2405-9595-
dc.identifier.urihttps://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21311-
dc.description.abstractIn this paper, an adaptive least recently frequently used (LRFU) replacement policy is proposed for named data network (NDN) in the industrial internet of things (IIoT) environment. Low-latency network communication has become the main focus in IIoT development. By applying NDN architecture with the proposed replacement policy, the system can minimize the network latency of IIoT due to the NDN router's capabilities to cache content. The simulation result shows that the proposed adaptive LRFU outperforms other popular replacement policies based on various network performances metrics. In addition, future research trends regarding the testbed implementation NDN replacement policy are suggested. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).-
dc.format.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisherELSEVIER-
dc.titleAdaptive LRFU replacement policy for named data network in industrial IoT-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.icte.2021.10.004-
dc.identifier.wosid000810442900018-
dc.identifier.bibliographicCitationICT EXPRESS, v.8, no.2, pp 258 - 263-
dc.citation.titleICT EXPRESS-
dc.citation.volume8-
dc.citation.number2-
dc.citation.startPage258-
dc.citation.endPage263-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.subject.keywordAuthorAdaptive LRFU-
dc.subject.keywordAuthorContent caching-
dc.subject.keywordAuthorIndustrial IoT-
dc.subject.keywordAuthorNDN replacement policy-
dc.subject.keywordAuthorDeep Learning (DL) approach [2-4]-
Files in This Item
There are no files associated with this item.
Appears in
Collections
School of Electronic Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher LEE, JAE MIN photo

LEE, JAE MIN
College of Engineering (School of Electronic Engineering)
Read more

Altmetrics

Total Views & Downloads

BROWSE