  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.
