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Title	Three-Dimensional Wireless Indoor Localization With Machine Learning Algorithms For Location-Based Iot Applications
ID_Doc	57348
Authors	Maduranga M.W.P.; Lakmal H.K.I.S.; Kalansooriya L.P.
Year	2024
Published	Communications in Computer and Information Science, 1995
DOI	http://dx.doi.org/10.1007/978-3-031-51135-6_1
Abstract	This paper presents a novel Machine Learning (ML)-based approach for radio signal-based indoor localizations. In modern location-based Internet of Things (IoT) applications, it is necessary to sense the location of an object within indoor environments. The traditional probabilistic and deterministic algorithms proposed in the literature have several drawbacks. In this work, we propose a localization framework using ML and Received Signal Strength Indication (RSSI) values. During the experiment, we utilized a publicly available UJIIndoorLoc localization dataset containing RSSI values received from known indoor locations. We trained ML algorithms, including Local Gaussian Regression, K-nearest neighbor, Decision Tree Classifier, XGBoost, LightGBM, Support Vector Classifier, and Gaussian Naive Bayes, to predict the location by feeding RSSI values received from the devices at known locations. The experimental results show that XGBoost provides 99% accuracy in terms of localization. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Deep Learning; Indoor Localization; Location-based Internet of Things Applications; Smart Cities

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