A novel hybrid model for bluetooth low energy-based indoor localization using machine learning in the internet of things

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dc.contributor.authorGormez, Yasin
dc.contributor.authorArslan, Halil
dc.contributor.authorIsik, Yunus Emre
dc.contributor.authorTomac, Sercan
dc.date.accessioned2024-10-26T18:04:15Z
dc.date.available2024-10-26T18:04:15Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractIndoor localization involves pinpointing the location of an object in an interior space and has several applications, including navigation, asset tracking, and shift management. However, this technology has not yet been perfected, and many methods, such as triangulation, Kalman filters, and machine learning models have been proposed to address indoor localization problems. Unfortunately, these methods still have a large degree of error that makes them ill-suited for difficult cases in real-time. In this study, we propose a hybrid model for Bluetooth low energy -based indoor localization. In this model, the triangulation method is combined with several machine learning methods (naive Bayes, k -nearest neighbor, logistic regression, support vector machines, and artificial neural networks) that are optimized and tested in three different environments. In the experiment, the proposed model performed similarly to the solo triangulation model in easy and medium cases; however, the proposed model obtained a much smaller degree of error for hard cases than either solo triangulation or machine learning models alone.
dc.identifier.doi10.5505/pajes.2023.57088
dc.identifier.endpage43
dc.identifier.issn1300-7009
dc.identifier.issn2147-5881
dc.identifier.issue1
dc.identifier.startpage36
dc.identifier.trdizinid1283226
dc.identifier.urihttps://doi.org/10.5505/pajes.2023.57088
dc.identifier.urihttps://hdl.handle.net/20.500.12418/28832
dc.identifier.volume30
dc.identifier.wosWOS:001168170900007
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherPamukkale Univ
dc.relation.ispartofPamukkale University Journal of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectInternet of Things
dc.subjectIndoor localization
dc.subjectBluetooth low energy
dc.subjectMachine learning
dc.subjectTriangulation
dc.titleA novel hybrid model for bluetooth low energy-based indoor localization using machine learning in the internet of things
dc.typeArticle

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