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

Fazli Subhan, Sajid Saleem, Haseeb Bari, Wazir Zada Khan, Saqib Hakak, Shafiq Ahmad and Ahmed M. El-Sherbeeny
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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

Sustainability, 2020, vol. 12, issue 24, 1-12

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.

Keywords: localization; LDA; KNN; received signal strength indicator; Bluetooth (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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