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Open AccessArticle

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea
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Electronics 2019, 8(9), 989; https://doi.org/10.3390/electronics8090989
Received: 16 July 2019 / Revised: 27 August 2019 / Accepted: 2 September 2019 / Published: 4 September 2019
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning. View Full-Text
Keywords: indoor localization; fingerprint; CNN; AlexNet; ResNet; ZFNet; Inception v3; MobileNet v2 indoor localization; fingerprint; CNN; AlexNet; ResNet; ZFNet; Inception v3; MobileNet v2
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Sinha, R.S.; Hwang, S.-H. Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization. Electronics 2019, 8, 989.

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