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MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System

Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algo...

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Main Authors: AL-Khaleefa, Ahmed Salih, Mohd Riduan, Ahmad, Awang Md Isa, Azmi, AL-Saffar, Ahmed, Mohd Esa, Mona Riza, Malik, Reza Firsandaya
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2019
Online Access:http://eprints.utem.edu.my/id/eprint/24169/2/5.PDF
http://eprints.utem.edu.my/id/eprint/24169/
https://www.mdpi.com/2078-2489/10/4/146/htm
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spelling my.utem.eprints.241692020-08-04T16:03:57Z http://eprints.utem.edu.my/id/eprint/24169/ MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System AL-Khaleefa, Ahmed Salih Mohd Riduan, Ahmad Awang Md Isa, Azmi AL-Saffar, Ahmed Mohd Esa, Mona Riza Malik, Reza Firsandaya Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. The most difficult aspect of techniques based on fingerprints is the effect of dynamic environmental changes on fingerprint authentication.With the aim of dealing with this problem, many experts have adopted transfer-learning methods even though in WiFi indoor localization the dynamic quality of the change in the fingerprint has some cyclic factors that necessitate the use of previous knowledge in various situations. Thus, this paper presents the maximum feature adaptive online sequential extreme learning machine (MFA-OSELM) technique, which uses previous knowledge to handle the cyclic dynamic factors that are brought about by the issue of mobility, which is present in internal environments. This research extends the earlier study of the feature adaptive online sequential extreme learning machine (FA-OSELM).The results of this research demonstrate that MFA-OSELM is superior to FA-OSELM given its capacity to preserve previous data when a person goes back to locations that he/she had visited earlier. Also,there is always a positive accuracy change when using MFA-OSELM, with the best change achieved being 27% (ranging from eight to 27% and six to 18% for the TampereU and UJIIndoorLoc datasets,respectively), which proves the efficiency of MFA-OSELM in restoring previous knowledge. Multidisciplinary Digital Publishing Institute (MDPI) 2019-04 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24169/2/5.PDF AL-Khaleefa, Ahmed Salih and Mohd Riduan, Ahmad and Awang Md Isa, Azmi and AL-Saffar, Ahmed and Mohd Esa, Mona Riza and Malik, Reza Firsandaya (2019) MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System. INFORMATION, 10 (4). pp. 1-20. ISSN 2078-2489 https://www.mdpi.com/2078-2489/10/4/146/htm 10.3390/info10040146
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. The most difficult aspect of techniques based on fingerprints is the effect of dynamic environmental changes on fingerprint authentication.With the aim of dealing with this problem, many experts have adopted transfer-learning methods even though in WiFi indoor localization the dynamic quality of the change in the fingerprint has some cyclic factors that necessitate the use of previous knowledge in various situations. Thus, this paper presents the maximum feature adaptive online sequential extreme learning machine (MFA-OSELM) technique, which uses previous knowledge to handle the cyclic dynamic factors that are brought about by the issue of mobility, which is present in internal environments. This research extends the earlier study of the feature adaptive online sequential extreme learning machine (FA-OSELM).The results of this research demonstrate that MFA-OSELM is superior to FA-OSELM given its capacity to preserve previous data when a person goes back to locations that he/she had visited earlier. Also,there is always a positive accuracy change when using MFA-OSELM, with the best change achieved being 27% (ranging from eight to 27% and six to 18% for the TampereU and UJIIndoorLoc datasets,respectively), which proves the efficiency of MFA-OSELM in restoring previous knowledge.
format Article
author AL-Khaleefa, Ahmed Salih
Mohd Riduan, Ahmad
Awang Md Isa, Azmi
AL-Saffar, Ahmed
Mohd Esa, Mona Riza
Malik, Reza Firsandaya
spellingShingle AL-Khaleefa, Ahmed Salih
Mohd Riduan, Ahmad
Awang Md Isa, Azmi
AL-Saffar, Ahmed
Mohd Esa, Mona Riza
Malik, Reza Firsandaya
MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System
author_facet AL-Khaleefa, Ahmed Salih
Mohd Riduan, Ahmad
Awang Md Isa, Azmi
AL-Saffar, Ahmed
Mohd Esa, Mona Riza
Malik, Reza Firsandaya
author_sort AL-Khaleefa, Ahmed Salih
title MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System
title_short MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System
title_full MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System
title_fullStr MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System
title_full_unstemmed MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System
title_sort mfa-oselm algorithm for wifi-based indoor positioning system
publisher Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/24169/2/5.PDF
http://eprints.utem.edu.my/id/eprint/24169/
https://www.mdpi.com/2078-2489/10/4/146/htm
_version_ 1675330909433233408
score 13.211869

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