Open Access

ARTICLE

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach: Multistory Round Building Scenario over LoRa Network

Muhammad Ayoub Kamal^1,3, Muhammad Mansoor Alam^1,2,4,6, Aznida Abu Bakar Sajak¹, Mazliham Mohd Su’ud^2,5,*

1 Malaysian Institute of Information Technology (MIIT), Universiti Kuala Lumpur, Kuala Lumpur, 50250, Malaysia
2 Faculty of Computing and Informatics, Multimedia University, Cyberjaya, 63100, Malaysia
3 Depertment of Computer Science, DHA Suffa University, Karachi, Sindh, 75500, Pakistan
4 Riphah Institute of System Engineering (RISE), Faculty of Computing, Riphah International University, Islamabad, 46000, Pakistan
5 Malaysian France Institute (MFI), Universiti Kuala Lumpur, Kuala Lumpur, 50250, Malaysia
6 Faculty of Engineering and Information Technology, School of Computer Science, University of Technology Sydney, Ultimo, NSW 2007, Australia

* Corresponding Author: Mazliham Mohd Su’ud. Email:

Computers, Materials & Continua 2024, 80(2), 1927-1945. https://doi.org/10.32604/cmc.2024.052169

Received 25 March 2024; Accepted 23 May 2024; Issue published 15 August 2024

Abstract

In situations when the precise position of a machine is unknown, localization becomes crucial. This research focuses on improving the position prediction accuracy over long-range (LoRa) network using an optimized machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology, this study proposed an optimized machine learning (ML) based algorithm. Received signal strength indicator (RSSI) data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory 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 study 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.

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Keywords

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