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Article

RSSI-Based for Device-Free Localization Using Deep Learning Technique

1
Centre of Excellence for Advanced Sensor Technology (CEASTech), University Malaysia Perlis, Arau, Perlis 006010, Malaysia
2
Graduate School of Integrated Research, University of Yamanashi, Kofu Yamanashi 400-8511, Japan
*
Author to whom correspondence should be addressed.
Smart Cities 2020, 3(2), 444-455; https://doi.org/10.3390/smartcities3020024
Received: 14 April 2020 / Revised: 27 May 2020 / Accepted: 27 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms. View Full-Text
Keywords: device-free localization; machine learning classifier; deep learning; big data; wireless networks; classification; received signal strength device-free localization; machine learning classifier; deep learning; big data; wireless networks; classification; received signal strength
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MDPI and ACS Style

Abdull Sukor, A.S.; Kamarudin, L.M.; Zakaria, A.; Abdul Rahim, N.; Sudin, S.; Nishizaki, H. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities 2020, 3, 444-455. https://doi.org/10.3390/smartcities3020024

AMA Style

Abdull Sukor AS, Kamarudin LM, Zakaria A, Abdul Rahim N, Sudin S, Nishizaki H. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities. 2020; 3(2):444-455. https://doi.org/10.3390/smartcities3020024

Chicago/Turabian Style

Abdull Sukor, Abdul S., Latifah M. Kamarudin, Ammar Zakaria, Norasmadi Abdul Rahim, Sukhairi Sudin, and Hiromitsu Nishizaki. 2020. "RSSI-Based for Device-Free Localization Using Deep Learning Technique" Smart Cities 3, no. 2: 444-455. https://doi.org/10.3390/smartcities3020024

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