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

Citation Author(s):
Muhammad Ayoub
Submitted by:
Muhammad Ayoub Kamal
Last updated:
Tue, 02/13/2024 - 06:39
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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.


An experiment on the LoRa network was conducted for this study. For the experiment, one LoRa transmitter and four LoRa receivers are used. The Institute of Business Management's College of Computer Science & Information Systems Building served as the site for data collection. The data collection building is shaped like a circle. Two floors (2nd & 3rd) and four distinct labs (labs 6, 7, 8, & 10) were selected for data collection. Four LoRA receivers (Rx1, Rx2, Rx3, and Rx4), which are located in labs 7, 10, 6, and 8, respectively, and one LoRA transmitter, which is positioned on 35 distinct reference points (xy), one by one between lobbies and staircases, are used to gather data. One meter separates the two reference locations from one another.



Submitted by Muhammad Ayoub Kamal on Tue, 02/13/2024 - 06:40

Can I access the data? if you have a paper associated with the data, please share it as well for citation 

Submitted by abdulmajeed Alenezi on Wed, 07/03/2024 - 14:18