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Knowledge preserving OSELM model for Wi-Fi-based indoor localization

Al-Khaleefa, Ahmed Salih and Ahmad, Mohd. Riduan and Md. Isa, Azmi Awang and Mohd. Esa, Mona Riza and Aljeroudi, Yazan and Jubair, Mohammed Ahmed and Malik, Reza Firsandaya (2019) Knowledge preserving OSELM model for Wi-Fi-based indoor localization. Sensors, 19 (10). pp. 1-23. ISSN 1424-8220


Official URL: http://dx.doi.org/10.3390/s19102397


Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.

Item Type:Article
Uncontrolled Keywords:fingerprint, indoor localization, learning, Wi-Fi
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:89232
Deposited By: Yanti Mohd Shah
Deposited On:22 Feb 2021 14:01
Last Modified:22 Feb 2021 14:01

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