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Open AccessArticle

MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System

1
Broadband and Networking (BBNET) Research Group, Centre for Telecommunication and Research Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
2
Faculty of Computer System and Software Engineering, University Malaysia Pahang (UMP), Gambang 26300, Pahang, Malaysia
3
Institute of High Voltage and High Current (IVAT), School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor Bharu, Malaysia
4
Faculty of Computer Science, Universitas Sriwijaya (UNSRI), Inderalaya, Sumatera Selatan 30151, Indonesia
*
Author to whom correspondence should be addressed.
Information 2019, 10(4), 146; https://doi.org/10.3390/info10040146
Received: 27 February 2019 / Revised: 14 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
(This article belongs to the Section Information and Communications Technology)
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. View Full-Text
Keywords: indoor positioning system; fingerprint; WiFi; transfer learning; machine learning indoor positioning system; fingerprint; WiFi; transfer learning; machine learning
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AL-Khaleefa, A.S.; Ahmad, M.R.; Isa, A.A.M.; AL-Saffar, A.; Esa, M.R.M.; Malik, R.F. MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System. Information 2019, 10, 146.

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