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Deep CNN for Indoor Localization in IoT-Sensor Systems

Conservatoire National des Arts et Métiers, CEDRIC/ LAETITIA Laboratory, 75003 Paris, France
University of Carthage, Higher School of Communication of Tunis, LR-11/TIC-03 Innov’COM Laboratory, 2083 Ariana, Tunisia
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3127;
Received: 16 May 2019 / Revised: 12 June 2019 / Accepted: 20 June 2019 / Published: 15 July 2019
(This article belongs to the Special Issue Sensors, Robots, Internet of Things, and Smart Factories)
PDF [1095 KB, uploaded 16 July 2019]


Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches. View Full-Text
Keywords: Convolutional Neural Networks (CNN); deep learning; image classification; indoor localization; kurtosis; RSSI fingerprinting Convolutional Neural Networks (CNN); deep learning; image classification; indoor localization; kurtosis; RSSI fingerprinting

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Njima, W.; Ahriz, I.; Zayani, R.; Terre, M.; Bouallegue, R. Deep CNN for Indoor Localization in IoT-Sensor Systems. Sensors 2019, 19, 3127.

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