Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator

Wi-Fi localization is an active research topic, and various challenges are not yet resolved in this field. Researchers develop models and use benchmark datasets for Wi-Fi or fingerprinting to create a quantitative comparative evaluation. These benchmarking datasets are limited by their failure to su...

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Main Authors:	Al-Khaleefa, Ahmed Salih, Mohd Riduan, Ahmad, Azmi Awang, Md Isa, Mona Riza, Mohd Esa, Mohammed Al-Saffar, Ahmed Ali, Aljeroudi, Yazan
Format:	Article
Language:	English
Published:	IEEE 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/22446/ http://umpir.ump.edu.my/id/eprint/22446/ http://umpir.ump.edu.my/id/eprint/22446/ http://umpir.ump.edu.my/id/eprint/22446/1/Infinite-Term%20Memory%20Classifier%20for%20Wi-Fi%20Localization%20based%20on%20Dynamic%20Wi-Fi%20Simulator.pdf

id	ump-22446
recordtype	eprints
spelling	ump-224462018-11-21T04:46:19Z http://umpir.ump.edu.my/id/eprint/22446/ Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Mohammed Al-Saffar, Ahmed Ali Aljeroudi, Yazan QA76 Computer software Wi-Fi localization is an active research topic, and various challenges are not yet resolved in this field. Researchers develop models and use benchmark datasets for Wi-Fi or fingerprinting to create a quantitative comparative evaluation. These benchmarking datasets are limited by their failure to support dynamical navigation. As a result, Wi-Fi models are only evaluated as usual classifiers without including actual navigation maneuvers in the evaluation, which makes the models incapable of handling the actual navigation behavior and its impact on the performance. One common navigation behavior is the cyclic dynamic behavior, which occurs frequently in the indoor environment when a person visits the same place or location multiple times or repeats the same trajectory or similar one more than once. For this purpose, we developed two models: a simulation model for generating time series data to support actual conducted navigation scenarios and a Wi-Fi classification model to handle dynamical scenarios generated by the simulator under cyclic dynamic behavior. Various testing scenarios were conducted for evaluation, and a comparison with benchmarks was performed. Results show the superiority of our developed model which is infinite-term memory online sequential extreme learning machine (OSELM) to the benchmarks with a percentage of 173% over feature adaptive OSELM and 1638% over OSELM. IEEE 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22446/1/Infinite-Term%20Memory%20Classifier%20for%20Wi-Fi%20Localization%20based%20on%20Dynamic%20Wi-Fi%20Simulator.pdf Al-Khaleefa, Ahmed Salih and Mohd Riduan, Ahmad and Azmi Awang, Md Isa and Mona Riza, Mohd Esa and Mohammed Al-Saffar, Ahmed Ali and Aljeroudi, Yazan (2018) Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator. IEEE Access, 6. pp. 54769-54785. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2018.2870754 10.1109/ACCESS.2018.2870754
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Mohammed Al-Saffar, Ahmed Ali Aljeroudi, Yazan Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator
description	Wi-Fi localization is an active research topic, and various challenges are not yet resolved in this field. Researchers develop models and use benchmark datasets for Wi-Fi or fingerprinting to create a quantitative comparative evaluation. These benchmarking datasets are limited by their failure to support dynamical navigation. As a result, Wi-Fi models are only evaluated as usual classifiers without including actual navigation maneuvers in the evaluation, which makes the models incapable of handling the actual navigation behavior and its impact on the performance. One common navigation behavior is the cyclic dynamic behavior, which occurs frequently in the indoor environment when a person visits the same place or location multiple times or repeats the same trajectory or similar one more than once. For this purpose, we developed two models: a simulation model for generating time series data to support actual conducted navigation scenarios and a Wi-Fi classification model to handle dynamical scenarios generated by the simulator under cyclic dynamic behavior. Various testing scenarios were conducted for evaluation, and a comparison with benchmarks was performed. Results show the superiority of our developed model which is infinite-term memory online sequential extreme learning machine (OSELM) to the benchmarks with a percentage of 173% over feature adaptive OSELM and 1638% over OSELM.
format	Article
author	Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Mohammed Al-Saffar, Ahmed Ali Aljeroudi, Yazan
author_facet	Al-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Azmi Awang, Md Isa Mona Riza, Mohd Esa Mohammed Al-Saffar, Ahmed Ali Aljeroudi, Yazan
author_sort	Al-Khaleefa, Ahmed Salih
title	Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator
title_short	Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator
title_full	Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator
title_fullStr	Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator
title_full_unstemmed	Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator
title_sort	infinite-term memory classifier for wi-fi localization based on dynamic wi-fi simulator
publisher	IEEE
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22446/ http://umpir.ump.edu.my/id/eprint/22446/ http://umpir.ump.edu.my/id/eprint/22446/ http://umpir.ump.edu.my/id/eprint/22446/1/Infinite-Term%20Memory%20Classifier%20for%20Wi-Fi%20Localization%20based%20on%20Dynamic%20Wi-Fi%20Simulator.pdf
first_indexed	2023-09-18T22:33:25Z
last_indexed	2023-09-18T22:33:25Z
_version_	1777416436552040448

Infinite-term memory classifier for wi-fi localization based on dynamic Wi-Fi Simulator

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