{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:35:10Z","timestamp":1773898510777,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ECSEL Joint Undertaking in collaboration with the European Union\u2019s H2020 Framework Programme","award":["H2020\/2014-2020"],"award-info":[{"award-number":["H2020\/2014-2020"]}]},{"name":"ECSEL Joint Undertaking in collaboration with the European Union\u2019s H2020 Framework Programme","award":["121N350"],"award-info":[{"award-number":["121N350"]}]},{"DOI":"10.13039\/501100004410","name":"National Authority TUBITAK","doi-asserted-by":"publisher","award":["H2020\/2014-2020"],"award-info":[{"award-number":["H2020\/2014-2020"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"National Authority TUBITAK","doi-asserted-by":"publisher","award":["121N350"],"award-info":[{"award-number":["121N350"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Wireless 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 1.73m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2m) under the same experiment environment.<\/jats:p>","DOI":"10.3390\/bdcc6030084","type":"journal-article","created":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T21:09:18Z","timestamp":1659992958000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6363-2732","authenticated-orcid":false,"given":"Muhammed Zahid","family":"Karakusak","sequence":"first","affiliation":[{"name":"Graduate School of Engineering and Natural Sciences, Istanbul Medipol University, 34810 Istanbul, Turkey"},{"name":"Department of Electronics Technology, Karabuk University, 78010 Karabuk, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-309X","authenticated-orcid":false,"given":"Hasan","family":"Kivrak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Karabuk University, 78050 Karabuk, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6842-1528","authenticated-orcid":false,"given":"Hasan Fehmi","family":"Ates","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Istanbul Medipol University, 34810 Istanbul, Turkey"}]},{"given":"Mehmet Kemal","family":"Ozdemir","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Istanbul Medipol University, 34810 Istanbul, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/MIS.2004.30","article-title":"A new generation of military robots","volume":"19","author":"Voth","year":"2004","journal-title":"IEEE Intell. 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