RSS-based wireless LAN indoor localization and tracking using deep architectures

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dc.authorid0000-0001-6363-2732
dc.authorid0000-0002-6842-1528
dc.authorid0000-0002-9054-0005
dc.contributor.authorKarakuşak, Muhammed Zahid
dc.contributor.authorKıvrak, Hasan
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorÖzdemir, Mehmet Kemal
dc.date.accessioned2022-10-06T07:32:21Z
dc.date.available2022-10-06T07:32:21Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Fen Bilimleri Enstitüsü, Elektrik ve Elektronik Mühendisliği ve Siber Sistemler Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractWireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.
dc.description.sponsorshipECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITAKen_US
dc.identifier.citationKarakuşak, M. Z., Kıvrak, H., Ateş, H. F. ve Özdemir, M. K. (2022). RSS-based wireless LAN indoor localization and tracking using deep architectures. Big Data and Cognitive Computing, 6(3). https://doi.org/10.3390/bdcc6030084
dc.identifier.doi10.3390/bdcc6030084
dc.identifier.issn2504-2289
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85138688330
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/bdcc6030084
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9796
dc.identifier.volume6
dc.identifier.wos000859400300001en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKarakuşak, Muhammed Zahid
dc.institutionauthorAteş, Hasan Fehmi
dc.institutionauthorÖzdemir, Mehmet Kemal
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofBig Data and Cognitive Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectWireless LAN Indoor Positioning
dc.subjectPosition Tracking
dc.subjectFingerprinting-Based Localization
dc.subjectKernel Density Estimator (KDE)
dc.subjectReceived Signal Strength (RSS)
dc.titleRSS-based wireless LAN indoor localization and tracking using deep architectures
dc.typeArticle

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