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Paper Abstract and Keywords
Presentation 2018-07-12 13:40
Accuracy Evaluations of CNN based Transportation Modes Recognition Using Communication Quality
Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto (Waseda Univ.) NS2018-65
Abstract (in Japanese) (See Japanese page) 
(in English) Because the accelerometer sensors and global positioning system (GPS) produce additional mobile traffic and energy consumption, we propose a CNN-based high-accurate recognition method only using communication quality factors. In the proposed method, instead of using accelerometer sensors and GPS, we collect mobile TCP throughputs, received-signal strength indicators (RSSIs), and cellular base-station IDs (Cell IDs) and generate an RGB image from these three factors. In accuracy evaluations, we construct a classifier using Convolutional Neural Network (CNN) applied to computer vision tasks and compare the recognition accuracy to the classical machine learning algorithms: Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest. Our results conclude that the CNN-based method can recognize with the highest accuracy among other methods.
Keyword (in Japanese) (See Japanese page) 
(in English) mobile sensing / transportation mode recognition / communication quality / machine learning / deep learning / quality of service / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 124, NS2018-65, pp. 133-138, July 2018.
Paper # NS2018-65 
Date of Issue 2018-07-04 (NS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NS2018-65

Conference Information
Committee ASN NS RCS SR RCC  
Conference Date 2018-07-11 - 2018-07-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Hakodate Arena 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Wireless Distributed Network, Machine Learning and AI for Wireless Communications and Networks, M2M (Machine-to-Machine), D2D (Device-to-Device), IoT（Internet of Things）, etc. 
Paper Information
Registration To NS 
Conference Code 2018-07-ASN-NS-RCS-SR-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Accuracy Evaluations of CNN based Transportation Modes Recognition Using Communication Quality 
Sub Title (in English)  
Keyword(1) mobile sensing  
Keyword(2) transportation mode recognition  
Keyword(3) communication quality  
Keyword(4) machine learning  
Keyword(5) deep learning  
Keyword(6) quality of service  
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Keyword(8)  
1st Author's Name Wataru Kawakami  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Kenji Kanai  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Bo Wei  
3rd Author's Affiliation Waseda University (Waseda Univ.)
4th Author's Name Jiro Katto  
4th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2018-07-12 13:40:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2018-65 
Volume (vol) vol.118 
Number (no) no.124 
Page pp.133-138 
#Pages 6 
Date of Issue 2018-07-04 (NS) 

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