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Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization.

Ahmed Salih Al-Khaleefa1, Mohd Riduan Ahmad2, Azmi Awang Md Isa3

  • 1Broadband 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. ahmed.salih89@siswa.ukm.edu.my.

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Summary
This summary is machine-generated.

This study introduces Knowledge Preserving Online Sequential Extreme Learning Machine (KP-OSELM) for Wi-Fi indoor localization. The novel model leverages cyclic human movement patterns to significantly improve localization accuracy and stability.

Keywords:
Wi-Fiextreme learning machinefingerprintindoor localizationlearning

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Area of Science:

  • Computer Science
  • Machine Learning
  • Signal Processing

Background:

  • Wi-Fi is widely available for indoor localization.
  • Existing machine learning models for Wi-Fi localization do not fully exploit indoor navigation dynamics.
  • Human movement indoors often exhibits cyclic patterns.

Purpose of the Study:

  • To develop a novel machine learning model for Wi-Fi indoor localization.
  • To enhance localization accuracy and stability by exploiting the cyclic dynamic nature of indoor navigation.
  • To introduce the Knowledge Preserving Online Sequential Extreme Learning Machine (KP-OSELM).

Main Methods:

  • Modification of the Online Sequential Extreme Learning Machine (OSELM) algorithm.
  • Incorporation of cyclic dynamic behavior into the localization model.
  • Experimental validation using TampereU and UJIndoorLoc datasets.

Main Results:

  • The proposed KP-OSELM model demonstrates superior performance compared to benchmark models.
  • KP-OSELM achieved high accuracy rates: 92.74% on TampereU and 72.99% on UJIndoorLoc.
  • The model showed improved stability in localization predictions.

Conclusions:

  • Exploiting cyclic dynamic behavior is effective for improving Wi-Fi indoor localization.
  • KP-OSELM offers a promising approach for accurate and stable indoor positioning systems.
  • The developed model advances the field of machine learning-based indoor localization.