RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures

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dc.authoridKivrak, Hasan/0000-0002-3782-309X
dc.authoridAtes, Hasan/0000-0002-6842-1528
dc.authoridKarakusak, Muhammed Zahid/0000-0001-6363-2732
dc.contributor.authorKarakusak, Muhammed Zahid
dc.contributor.authorKivrak, Hasan
dc.contributor.authorAtes, Hasan Fehmi
dc.contributor.authorOzdemir, Mehmet Kemal
dc.date.accessioned2024-09-29T16:08:05Z
dc.date.available2024-09-29T16:08:05Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
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.en_US
dc.description.sponsorshipECSEL Joint Undertaking; European Union's H2020 Framework Programme (H2020/2014-2020) Grant [101007321]; National Authority TUBITAK [121N350]en_US
dc.description.sponsorshipThe research leading to these results has received funding from the ECSEL Joint Undertaking in collaboration with the European Union's H2020 Framework Programme (H2020/2014-2020) Grant Agreement-101007321-StorAIge and National Authority TUBITAK with project ID 121N350.en_US
dc.identifier.doi10.3390/bdcc6030084
dc.identifier.issn2504-2289
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85138688330en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/bdcc6030084
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7333
dc.identifier.volume6en_US
dc.identifier.wosWOS:000859400300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofBig Data and Cognitive Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWireless LAN indoor positioningen_US
dc.subjectposition trackingen_US
dc.subjectfingerprinting-based localizationen_US
dc.subjectKernel Density Estimator (KDE)en_US
dc.subjectReceived Signal Strength (RSS)en_US
dc.subjectContinuous Wavelet Transform (CWT)en_US
dc.subjectdeep learningen_US
dc.subjectMulti-Layer Perceptron (MLP)en_US
dc.subjectConvolutional Neural Networks (CNN)en_US
dc.subjectLong Short Term Memory (LSTM)en_US
dc.subjectHyperparameter Optimization (HPO)en_US
dc.titleRSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architecturesen_US
dc.typeArticleen_US

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