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Paper Abstract and Keywords
Presentation 2022-03-02 10:00
Reducing Power Consumption of BLE Advertising Based on Reinforcement Learning
Ryoma Kitagawa (Tokyo Univ. of Science), Ryosuke Isogai (Seico), Hiroyuki Yasuda (Tokyo Univ.), Yoshifumi Yoshida (Seico), Song-Ju Kim, Mikio Hasegawa (Tokyo Univ. of Science) SRW2021-65
Abstract (in Japanese) (See Japanese page) 
(in English) Bluetooth Low Energy (BLE) is a short-range wireless communication characterized by low power consumption and has been applied to a variety of applications, mainly as a beacon. The BLE device periodically broadcasts packets to indicate its presence and sends such advertising packets via three advertising channels. However, previous studies have shown the possibility of further reducing power consumption by efficiently reducing the number of advertising channels (channel mask) and adjusting the interval between advertising packets. In this paper, we evaluate our proposed reinforcement learning method for efficient selection of the channel mask and advertising interval from the viewpoint of power consumption by simulations of BLE advertisement in various environments. The results show that the proposed method can achieve low power consumption without a significant loss of reliability.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Learning / Reinforcement Learning / Bluetooth Low Energy / IoT / BLE Advertise / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 393, SRW2021-65, pp. 1-6, March 2022.
Paper # SRW2021-65 
Date of Issue 2022-02-23 (SRW) 
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)
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Conference Information
Committee RCS SR SRW  
Conference Date 2022-03-02 - 2022-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To SRW 
Conference Code 2022-03-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reducing Power Consumption of BLE Advertising Based on Reinforcement Learning 
Sub Title (in English)  
Keyword(1) Machine Learning  
Keyword(2) Reinforcement Learning  
Keyword(3) Bluetooth Low Energy  
Keyword(4) IoT  
Keyword(5) BLE Advertise  
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1st Author's Name Ryoma Kitagawa  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
2nd Author's Name Ryosuke Isogai  
2nd Author's Affiliation Seico Holdings (Seico)
3rd Author's Name Hiroyuki Yasuda  
3rd Author's Affiliation Tokyo University (Tokyo Univ.)
4th Author's Name Yoshifumi Yoshida  
4th Author's Affiliation Seico Holdings (Seico)
5th Author's Name Song-Ju Kim  
5th Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
6th Author's Name Mikio Hasegawa  
6th Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
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Speaker Author-1 
Date Time 2022-03-02 10:00:00 
Presentation Time 25 minutes 
Registration for SRW 
Paper # SRW2021-65 
Volume (vol) vol.121 
Number (no) no.393 
Page pp.1-6 
#Pages
Date of Issue 2022-02-23 (SRW) 


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