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Paper Abstract and Keywords
Presentation 2019-03-05 16:00
Adaptive Data Compression Using Deep Learning Executable on Edge Device
Masatoshi Sekine, Satoshi Ikada (OKI) NS2018-293
Abstract (in Japanese) (See Japanese page) 
(in English) Efficient collection of sensor data is important for sensing the real world and utilizing it in various applications. Generally, a large amount of sensor data is required to analyze long-term and wide-range data with high precision in applications. However, at the edge device, waste of radio resources and power due to an increase in the amount of data transmission may occur. Therefore, it is necessary to reduce the amount of data without degrading the information amount of the original data as much as possible. In this paper, we propose a method to set the data transmission efficiency and optimize it to estimate the compression ratio of the compressed sensing. It is executable at the edge device dynamically and directly, in order to improve data transmission efficiency. Generally, optimization control generally requires a large amount of calculation amount, but by using the learning model of supervised learning or reinforcement learning in deep learning learned in advance in the proposed method, the search for optimal compression ratio can be performed with less processing load. As a result of performance evaluation, using the data generated by an acceleration sensor placed on the bridge, the proposed method can reduce the computational load for estimating the optimum compression ratio, while reducing both compression ratio and reconstruction error.
Keyword (in Japanese) (See Japanese page) 
(in English) sensor networks / compressed sensing / edge device / deep learning / supervised learning / reinforcement learning / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 465, NS2018-293, pp. 575-580, March 2019.
Paper # NS2018-293 
Date of Issue 2019-02-25 (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 NS2018-293

Conference Information
Committee IN NS  
Conference Date 2019-03-04 - 2019-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2019-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Adaptive Data Compression Using Deep Learning Executable on Edge Device 
Sub Title (in English)  
Keyword(1) sensor networks  
Keyword(2) compressed sensing  
Keyword(3) edge device  
Keyword(4) deep learning  
Keyword(5) supervised learning  
Keyword(6) reinforcement learning  
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Keyword(8)  
1st Author's Name Masatoshi Sekine  
1st Author's Affiliation Oki Electric Industry Co., Ltd. (OKI)
2nd Author's Name Satoshi Ikada  
2nd Author's Affiliation Oki Electric Industry Co., Ltd. (OKI)
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Speaker Author-1 
Date Time 2019-03-05 16:00:00 
Presentation Time 20 minutes 
Registration for NS 
Paper # NS2018-293 
Volume (vol) vol.118 
Number (no) no.465 
Page pp.575-580 
#Pages 6 
Date of Issue 2019-02-25 (NS) 

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