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Appl. Sci. 2018, 8(7), 1062; https://doi.org/10.3390/app8071062

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

1
Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan
2
Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
3
Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan
*
Author to whom correspondence should be addressed.
Received: 30 May 2018 / Revised: 22 June 2018 / Accepted: 26 June 2018 / Published: 29 June 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments. View Full-Text
Keywords: deep neural network; Internet of Things; linear discriminant analysis; Wi-Fi based indoor localization deep neural network; Internet of Things; linear discriminant analysis; Wi-Fi based indoor localization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Adege, A.B.; Lin, H.-P.; Tarekegn, G.B.; Jeng, S.-S. Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment. Appl. Sci. 2018, 8, 1062.

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