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Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

Author

Listed:
  • Fazli Subhan

    (Department of Computer Sciences, National University of Modern Languages-NUML, Islamabad 44000, Pakistan)

  • Sajid Saleem

    (Department of Computer Sciences, National University of Modern Languages-NUML, Islamabad 44000, Pakistan)

  • Haseeb Bari

    (Department of Computer Sciences, National University of Modern Languages-NUML, Islamabad 44000, Pakistan)

  • Wazir Zada Khan

    (Department of Computer Science, Comsats University, Islamabad 44000, Pakistan)

  • Saqib Hakak

    (Faculty of Computer Science, Canadian Institute for Cybersecurity, University of New Brunswick, Fredericton, NB E3B 5A3, Canada)

  • Shafiq Ahmad

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Ahmed M. El-Sherbeeny

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

Abstract

Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization error, and modeling of distance estimates from received signals. Fingerprinting based tracking solutions are also environment dependent. On the other side, machine learning-based techniques are currently attracting industries for developing tracking applications. In this paper we have modeled a machine learning method known as Linear Discriminant Analysis (LDA) for real time dynamic object localization. The experimental results are based on real time trajectories, which validated the effectiveness of our proposed system in terms of accuracy compared to naive Bayes, k-nearest neighbors, a support vector machine and a decision tree.

Suggested Citation

  • Fazli Subhan & Sajid Saleem & Haseeb Bari & Wazir Zada Khan & Saqib Hakak & Shafiq Ahmad & Ahmed M. El-Sherbeeny, 2020. "Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)," Sustainability, MDPI, vol. 12(24), pages 1-12, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10627-:d:465200
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    References listed on IDEAS

    as
    1. Yun Mo & Zhongzhao Zhang & Yang Lu & Weixiao Meng & Gul Agha, 2014. "Random Forest Based Coarse Locating and KPCA Feature Extraction for Indoor Positioning System," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, October.
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    Cited by:

    1. Cunwei Yang & Weiqing Wang & Fengying Li & Degang Yang, 2022. "One-Size-Fits-All Policies Are Unacceptable: A Sustainable Management and Decision-Making Model for Schools in the Post-COVID-19 Era," IJERPH, MDPI, vol. 19(10), pages 1-21, May.

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