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Throughput and packet loss probability analysis of long range wide area network

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dc.contributor.authorJung, J.-Y.-
dc.contributor.authorLee, J.-R.-
dc.date.accessioned2021-09-15T03:40:11Z-
dc.date.available2021-09-15T03:40:11Z-
dc.date.issued2021-09-
dc.identifier.issn2076-3417-
dc.identifier.issn2076-3417-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/49142-
dc.description.abstractLong Range Wide Area Network (LoRaWAN) is the one of the promising low power wide area network (LPWAN) technologies at present and is expected to grow in the foreseeable future as a tool to provide connectivity among small things. In this paper, we present a simple analytical model to compute the throughput and packet loss probability of Medium Access Control (MAC) for Class-A of LoRaWAN. This analysis results can be used as a reference for deploying the appropriate number of end-devices (EDs) that can be accepted in a gateway (GW) while maximizing network throughput or guaranteeing the packet loss rate of EDs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.-
dc.language영어-
dc.language.isoENG-
dc.publisherMDPI-
dc.titleThroughput and packet loss probability analysis of long range wide area network-
dc.typeArticle-
dc.identifier.doi10.3390/app11178091-
dc.identifier.bibliographicCitationApplied Sciences (Switzerland), v.11, no.17-
dc.description.isOpenAccessY-
dc.identifier.wosid000694137500001-
dc.identifier.scopusid2-s2.0-85114176736-
dc.citation.number17-
dc.citation.titleApplied Sciences (Switzerland)-
dc.citation.volume11-
dc.type.docTypeArticle-
dc.publisher.location스위스-
dc.subject.keywordAuthorInternet of Things-
dc.subject.keywordAuthorLong range wide area network-
dc.subject.keywordAuthorLow power wide area network-
dc.subject.keywordAuthorMarkov chain model-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryEngineering, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
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