Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour

Wi-Fi based localization using machine learning has been proven to be an attractive approach for finding the location prediction with avoidance of accumulation of errors as other sensors such as odometry and inertial sensing. Researchers have developed various models to predict locations based on tr...

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Main Author:	Khirbeet, Ahmed Salih
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/25388/1/Wi-Fi%20Navigation%20Using%20Machine%20Learning%20With%20Consideration%20Of%20Cyclic%20Dynamic%20Behaviour.pdf http://eprints.utem.edu.my/id/eprint/25388/2/Wi-Fi%20Navigation%20Using%20Machine%20Learning%20With%20Consideration%20Of%20Cyclic%20Dynamic%20Behaviour.pdf
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id	my-utem-ep.25388
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Ahmad, Mohd Riduan
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Khirbeet, Ahmed Salih Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour
description	Wi-Fi based localization using machine learning has been proven to be an attractive approach for finding the location prediction with avoidance of accumulation of errors as other sensors such as odometry and inertial sensing. Researchers have developed various models to predict locations based on trained machine learning. A site survey is typically performed to collect fingerprints and a neural network is trained on those fingerprints. The trained model is then placed into operation. However, dynamic changes in the location and navigation behavior of users make the prediction process more challenging in terms of accurate prediction of location. One common mobility behavior of navigation runs is the cyclic dynamics or re-visiting the same place more than one time. Most machine learning models, developed for location prediction, lack sufficient handling of dynamic changes or leveraging them for better predictions. To fill this gap, this study builds a new simulator with two components: one for incorporating dynamic information of navigation in given Wi-Fi dataset and using them to generate the corresponding time series of any navigation run, it is named as Wi-Fi Simulator for Cyclic Dynamic (Wi-Fi-SCD) while the other is useful for converting any dataset to time series with cyclic dynamics, it is named as Cyclic Dynamic Generator (CDG). Furthermore, in this study, two novel location prediction machine learning models were developed. The first is Knowledge Preservation Online Sequential Extreme Learning Machine (KP-OSELM) and the second is Infinite Term Memory-based Online Sequential Extreme Learning Machine (ITM-OSELM). The KP-OSELM model is distinctive from other models cited in the literature, because it preserves knowledge gained in certain areas to restore again when the person re-visits the area again. In KP-OSELM, knowledge is preserved within the neural network structure and is enabled based on feature encoding. The ITM-OSELM model is distinctive from other models cited in the literature, because it carries external memory and transfers learning to preserve old knowledge and restoration. ITM-OSELM is more efficient than KP-OSELM when the percentage of active features is low. Meanwhile, KP-OSELM does not require any external blocks to be added to the neural network (unlike ITM-OSELM), which makes it much simpler. In area based scenarios, KP-OSELM and ITM-OSELM both achieved accuracies of 68%. Moreover, when evaluating KP-OSELM and ITM-OSELM on Wi-Fi-SCD, for three navigation cycles, the highest accuracies achieved were 92.74% and 92.76%, respectively. However, the execution time of KP-OSELM was 1176 second while much less time was needed for ITM-OSELM to be executed with a value of 649 second. Furthermore, when evaluating KP-OSELM and ITM-OSELM on CDG, for three cycles, 100% accuracy was achieved for both models. As a conclusion, this study has provided the literature of machine learning in general and WiFi navigation in particular with various models to support the localization without any restriction on the type of Wi-Fi that is used and with consideration of the practical and dynamic behaviors that can be leveraged to improve the localization performance.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Khirbeet, Ahmed Salih
author_facet	Khirbeet, Ahmed Salih
author_sort	Khirbeet, Ahmed Salih
title	Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour
title_short	Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour
title_full	Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour
title_fullStr	Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour
title_full_unstemmed	Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour
title_sort	wi-fi navigation using machine learning with consideration of cyclic dynamic behaviour
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electronics and Computer Engineering
publishDate	2020
url	http://eprints.utem.edu.my/id/eprint/25388/1/Wi-Fi%20Navigation%20Using%20Machine%20Learning%20With%20Consideration%20Of%20Cyclic%20Dynamic%20Behaviour.pdf http://eprints.utem.edu.my/id/eprint/25388/2/Wi-Fi%20Navigation%20Using%20Machine%20Learning%20With%20Consideration%20Of%20Cyclic%20Dynamic%20Behaviour.pdf
_version_	1747834115583901696
spelling	my-utem-ep.253882021-11-17T08:45:06Z Wi-Fi Navigation Using Machine Learning With Consideration Of Cyclic Dynamic Behaviour 2020 Khirbeet, Ahmed Salih T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Wi-Fi based localization using machine learning has been proven to be an attractive approach for finding the location prediction with avoidance of accumulation of errors as other sensors such as odometry and inertial sensing. Researchers have developed various models to predict locations based on trained machine learning. A site survey is typically performed to collect fingerprints and a neural network is trained on those fingerprints. The trained model is then placed into operation. However, dynamic changes in the location and navigation behavior of users make the prediction process more challenging in terms of accurate prediction of location. One common mobility behavior of navigation runs is the cyclic dynamics or re-visiting the same place more than one time. Most machine learning models, developed for location prediction, lack sufficient handling of dynamic changes or leveraging them for better predictions. To fill this gap, this study builds a new simulator with two components: one for incorporating dynamic information of navigation in given Wi-Fi dataset and using them to generate the corresponding time series of any navigation run, it is named as Wi-Fi Simulator for Cyclic Dynamic (Wi-Fi-SCD) while the other is useful for converting any dataset to time series with cyclic dynamics, it is named as Cyclic Dynamic Generator (CDG). Furthermore, in this study, two novel location prediction machine learning models were developed. The first is Knowledge Preservation Online Sequential Extreme Learning Machine (KP-OSELM) and the second is Infinite Term Memory-based Online Sequential Extreme Learning Machine (ITM-OSELM). The KP-OSELM model is distinctive from other models cited in the literature, because it preserves knowledge gained in certain areas to restore again when the person re-visits the area again. In KP-OSELM, knowledge is preserved within the neural network structure and is enabled based on feature encoding. The ITM-OSELM model is distinctive from other models cited in the literature, because it carries external memory and transfers learning to preserve old knowledge and restoration. ITM-OSELM is more efficient than KP-OSELM when the percentage of active features is low. Meanwhile, KP-OSELM does not require any external blocks to be added to the neural network (unlike ITM-OSELM), which makes it much simpler. In area based scenarios, KP-OSELM and ITM-OSELM both achieved accuracies of 68%. Moreover, when evaluating KP-OSELM and ITM-OSELM on Wi-Fi-SCD, for three navigation cycles, the highest accuracies achieved were 92.74% and 92.76%, respectively. However, the execution time of KP-OSELM was 1176 second while much less time was needed for ITM-OSELM to be executed with a value of 649 second. Furthermore, when evaluating KP-OSELM and ITM-OSELM on CDG, for three cycles, 100% accuracy was achieved for both models. As a conclusion, this study has provided the literature of machine learning in general and WiFi navigation in particular with various models to support the localization without any restriction on the type of Wi-Fi that is used and with consideration of the practical and dynamic behaviors that can be leveraged to improve the localization performance. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25388/ http://eprints.utem.edu.my/id/eprint/25388/1/Wi-Fi%20Navigation%20Using%20Machine%20Learning%20With%20Consideration%20Of%20Cyclic%20Dynamic%20Behaviour.pdf text en validuser http://eprints.utem.edu.my/id/eprint/25388/2/Wi-Fi%20Navigation%20Using%20Machine%20Learning%20With%20Consideration%20Of%20Cyclic%20Dynamic%20Behaviour.pdf text en public https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119762 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Engineering Ahmad, Mohd Riduan 1. 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