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dc.contributor.authorMondal, Riaz
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorTurkka, Jussi
dc.date.accessioned2017-12-15T11:08:31Z
dc.date.available2018-08-16T21:35:32Z
dc.date.issued2017
dc.identifier.citationMondal, R., Ristaniemi, T., & Turkka, J. (2017). Cluster-based RF fingerprint positioning using LTE and WLAN signal strengths. <em>International Journal of Wireless Information Networks</em>, 24 (4), 413-423. <a href="https://doi.org/10.1007/s10776-017-0369-9">doi:10.1007/s10776-017-0369-9</a>
dc.identifier.otherTUTKAID_74677
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/56356
dc.description.abstractWireless Local Area Network (WLAN) positioning has become a popular localization system due to its low-cost installation and widespread availability of WLAN access points. Traditional grid-based radio frequency (RF) fingerprinting (GRFF) suffers from two drawbacks. First it requires costly and non-efficient data collection and updating procedure; secondly the method goes through time-consuming data pre-processing before it outputs user position. This paper proposes Cluster-based RF Fingerprinting (CRFF) to overcome these limitations by using modified Minimization of Drive Tests data which can be autonomously collected by cellular operators from their subscribers. The effect of environmental changes and device variation on positioning accuracy has been carried out. Experimental results show that even under these variations CRFF can improve positioning accuracy by 15.46 and 22.30% in 95 percentile of positioning error as compared to that of GRFF and K-nearest neighbour methods respectively.
dc.language.isoeng
dc.publisherSpringer New York LLC
dc.relation.ispartofseriesInternational Journal of Wireless Information Networks
dc.subject.otherRF fingerprint positioning
dc.subject.otherK-nearest neighbors
dc.subject.otherK-means clustering
dc.subject.otherhierarchical clustering
dc.subject.otherfuzzy C-means clustering
dc.titleCluster-based RF fingerprint positioning using LTE and WLAN signal strengths
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201712114595
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2017-12-11T07:15:17Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange413-423
dc.relation.issn1068-9605
dc.relation.volume24
dc.type.versionacceptedVersion
dc.rights.copyright© Springer Science+Business Media, LLC 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.doi10.1007/s10776-017-0369-9


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