scholarly journals Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

2018 ◽  
Author(s):  
Ayush Mittal ◽  
Saideep Tiku ◽  
Sudeep Pasricha
Keyword(s):  
Neural Networks ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Indoor Localization ◽  
Smart Mobile ◽  
Smart Mobile Devices

scholarly journals Comparison of CNN Applications for RSSI-based Fingerprint Indoor Localization

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 989 ◽  
Author(s):  
Sinha ◽  
Hwang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Indoor Localization ◽  
Signal Strength ◽  
Test Accuracy ◽  
Computational Power ◽  
Location Error ◽  
Received Signal Strength Indicator

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.

Deflectometry measurement system for smart mobile devices

2017 ◽  
Vol 10 (2) ◽  
pp. 267-279
Author(s):  
冀翼 JI Yi ◽  
张学军 ZHANG Xue-jun ◽  
袁婷 YUAN Ting ◽  
陶小平 TAO Xiao-ping
Keyword(s):  
Mobile Devices ◽  
Measurement System ◽  
Smart Mobile ◽  
Smart Mobile Devices
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