Please use this identifier to cite or link to this item: https://ruomoplus.lib.uom.gr/handle/8000/1913
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dc.contributor.authorGriva, Aikateriniel
dc.contributor.authorBoursianis, Achilles D.el
dc.contributor.authorWan, Shaohuael
dc.contributor.authorSarigiannidis, Panagiotisel
dc.contributor.authorPsannis, Konstantinosel
dc.contributor.authorKaragiannidis, George K.el
dc.contributor.authorGoudos, Sotirios K.el
dc.date.accessioned2024-12-04T17:34:21Z-
dc.date.available2024-12-04T17:34:21Z-
dc.date.issued2023-02-03-
dc.identifier.urihttps://ruomoplus.lib.uom.gr/handle/8000/1913-
dc.description.abstractThe implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.el
dc.language.isoenel
dc.relation.ispartofSensors (Basel, Switzerland)el
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Διεθνές*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFRASCATI__Natural sciences__Computer and information sciencesel
dc.subject.otherdata extraction rate (DER)el
dc.subject.otherInternet of Things (IoT)el
dc.subject.otherlong-range networkel
dc.subject.otherlow-power wide-area network (LPWAN)el
dc.subject.othernetwork energy consumption (NEC)el
dc.subject.othersmart agricultureel
dc.subject.othersmart cityel
dc.titleLoRa-Based IoT Network Assessment in Rural and Urban Scenariosel
dc.typejournal articleel
dc.identifier.doi10.3390/s23031695-
dc.identifier.pmid36772734-
dc.identifier.scopus2-s2.0-85147894785-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85147894785-
dc.relation.issn1424-8220el
dc.relation.issn1424-8220el
dc.description.volume23el
dc.description.issue3el
dc.description.startpage1695el
dc.contributor.departmentDepartment of Applied Informaticsel
dc.relation.eissn1424-8220el
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypejournal article-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.journal.journalissn1424-8220-
crisitem.journal.journaleissn1424-8220-
crisitem.author.deptUniversity of Macedonia-
crisitem.author.deptUniversity of Macedonia-
crisitem.author.deptUniversity of Macedonia-
crisitem.author.deptUniversity of Macedonia-
crisitem.author.deptUniversity of Macedonia-
crisitem.author.deptUniversity of Macedonia-
crisitem.author.departmentDepartment of Applied Informatics-
crisitem.author.orcid0000-0002-8301-0970-
crisitem.author.orcid0000-0003-0020-6394-
crisitem.author.facultySchool of Information Sciences-
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