Indoor Localization Data Based on SNR and RSSI within Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown, localization becomes crucial. It is crucial to identify and ascertain the machine's position. This research focuses on improving the position prediction accuracy over long-range networks using a unique machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting approach using LoRa technology, this study suggested an ML-based algorithm. Signal strength data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multi-story round layout building. The noise factor is also taken into account, and the signal-to-noise ratio (SNR) value is recorded for every RSSI measurement. This concludes the examination of reference point accuracy with the Modified KNN method (MKNN). MKNN was created to more precisely anticipate the position of the reference point. The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.