Next Article in Journal
Glycinebetaine-Induced Alteration in Gaseous Exchange Capacity and Osmoprotective Phenomena in Safflower (Carthamus tinctorius L.) under Water Deficit Conditions
Previous Article in Journal
Making Sense of the Sharing Economy: A Category Formation Approach
Previous Article in Special Issue
Reinforcement Learning in Blockchain-Enabled IIoT Networks: A Survey of Recent Advances and Open Challenges
Article

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

1
Department of Computer Sciences, National University of Modern Languages-NUML, Islamabad 44000, Pakistan
2
Department of Computer Science, Comsats University, Islamabad 44000, Pakistan
3
Faculty of Computer Science, Canadian Institute for Cybersecurity, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
4
Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(24), 10627; https://doi.org/10.3390/su122410627
Received: 2 October 2020 / Revised: 26 November 2020 / Accepted: 27 November 2020 / Published: 19 December 2020
(This article belongs to the Special Issue Role of AI, Big Data, and Blockchain in IoT Devices)
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. View Full-Text
Keywords: localization; LDA; KNN; received signal strength indicator; Bluetooth localization; LDA; KNN; received signal strength indicator; Bluetooth
Show Figures

Figure 1

MDPI and ACS Style

Subhan, F.; Saleem, S.; Bari, H.; Khan, W.Z.; Hakak, S.; Ahmad, S.; El-Sherbeeny, A.M. Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE). Sustainability 2020, 12, 10627. https://doi.org/10.3390/su122410627

AMA Style

Subhan F, Saleem S, Bari H, Khan WZ, Hakak S, Ahmad S, El-Sherbeeny AM. Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE). Sustainability. 2020; 12(24):10627. https://doi.org/10.3390/su122410627

Chicago/Turabian Style

Subhan, Fazli, Sajid Saleem, Haseeb Bari, Wazir Z. Khan, Saqib Hakak, Shafiq Ahmad, and Ahmed M. El-Sherbeeny 2020. "Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)" Sustainability 12, no. 24: 10627. https://doi.org/10.3390/su122410627

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop