Citation

Kamal, Muhammad Ayoub and Alam, Muhammad Mansoor and Abu Bakar Sajak, Aznida and Mohd Su'ud, Mazliham (2024) SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach: Multistory Round Building Scenario over LoRa Network. Computers, Materials & Continua, 80 (2). pp. 1927-1945. ISSN 1546-2226

Text TSP_CMC_52169.pdf - Published Version Restricted to Repository staff only Download (1MB)

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

Item Type:	Article
Uncontrolled Keywords:	Indoor localization, MKNN; LoRa, machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Sep 2024 00:58
Last Modified:	03 Sep 2024 00:58
URII:	http://shdl.mmu.edu.my/id/eprint/12944

Downloads

Edit (login required)