Universiti Teknologi Malaysia Institutional Repository

MFA-OSELM algorithm for WiFi-based indoor positioning system

Al Khaleefa, Ahmed Salih and Ahmad, Mohd. Riduan and Md. Isa, Azmi Awang and Al Saffar, Ahmed and Mohd. Esa, Mona Riza and Malik, Reza Firsandaya (2019) MFA-OSELM algorithm for WiFi-based indoor positioning system. Information (Switzerland), 10 (4). pp. 1-20. ISSN 2078-2489


Official URL: http://dx.doi.org/10.3390/info10040146


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.

Item Type:Article
Uncontrolled Keywords:fingerprint, indoor positioning system, machine learning, transfer learning
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:87939
Deposited By: Yanti Mohd Shah
Deposited On:30 Nov 2020 21:37
Last Modified:30 Nov 2020 21:37

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