Improved Indoor Localization with Machine Learning Techniques for IoT applications

• URL: http://arxiv.org/abs/2402.11433v1
• Date: Sun, 18 Feb 2024 02:55:19 GMT
• Title: Improved Indoor Localization with Machine Learning Techniques for IoT applications
• Authors: M.W.P. Maduranga
• Abstract summary: This study employs machine learning algorithms in three phases: supervised regressors, supervised classifiers, and ensemble methods for RSSI-based indoor localization. The experiment's outcomes provide insights into the effectiveness of different supervised machine learning techniques in terms of localization accuracy and robustness in indoor environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of the Internet of Things (IoT) and mobile internet applications has spurred interest in location-based services (LBS) for commercial, military, and social applications. While the global positioning system (GPS) dominates outdoor localization, its efficacy wanes indoors due to signal challenges. Indoor localization systems leverage wireless technologies like Wi-Fi, ZigBee, Bluetooth, UWB, selecting based on context. Received signal strength indicator (RSSI) technology, known for its accuracy and simplicity, is widely adopted. This study employs machine learning algorithms in three phases: supervised regressors, supervised classifiers, and ensemble methods for RSSI-based indoor localization. Additionally, it introduces a weighted least squares technique and pseudo-linear solution approach to address non-linear RSSI measurement equations by approximating them with linear equations. An experimental testbed, utilizing diverse wireless technologies and anchor nodes, is designed for data collection, employing IoT cloud architectures. Pre-processing involves investigating filters for data refinement before algorithm training. The study employs machine learning models like linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regressor across various wireless technologies. These models estimate the geographical coordinates of a moving target node, and their performance is evaluated using metrics such as accuracy, root mean square errors, precision, recall, sensitivity, coefficient of determinant, and the f1-score. The experiment's outcomes provide insights into the effectiveness of different supervised machine learning techniques in terms of localization accuracy and robustness in indoor environments.

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