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Paper Abstract and Keywords
Presentation 2024-11-04 14:55
[Poster Presentation] Performance Improvement of Reinforcement Learning Based Energy-Efficient Beaconing Scheme in Bluetooth Low Energy
Reona Nomura (TUS), Ryosuke Isogai, Yudai Imano, Yoshifumi Yoshida (SEIKO), Hiroyuki Yasuda (UTokyo), Song-Ju Kim (SOBIN), Maki Arai, Mikio Hasegawa (TUS) SR2024-54
Abstract (in Japanese) (See Japanese page) 
(in English) Bluetooth Low Energy (BLE) is a short-range wireless communication characterized by very low power consumption and is widely used in various applications for the beacon. However, since BLE-equipped devices are small and usually battery-powered, it is important to further reduce power consumption. BLE broadcasts packets periodically to announce its presence and uses three channels for reliable communication. To reduce the power consumption of the BLE, Fujisawa et al. proposed an adaptive method for masking transmission channels and selecting transmission intervals suitable for environmental conditions by applying a computationally inexpensive Multi-Armed Bandit (MAB) algorithm. In this paper, we compare several MAB algorithms to reward models and evaluate their performances through implementation on a real device. Our experimental results demonstrate that the performance of the proposed method can be further improved by using the UCB1-tuned algorithm and by setting the reward as the inverse ratio of power consumption.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / Bluetooth Low Energy / BLE advertising / Reinforcement learning / Multi-Armed Bandit problem / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 233, SR2024-54, pp. 17-23, Nov. 2024.
Paper # SR2024-54 
Date of Issue 2024-10-28 (SR) 
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 SR2024-54

Conference Information
Committee SR  
Conference Date 2024-11-04 - 2024-11-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Fraunhofer IPT 
Topics (in Japanese) (See Japanese page) 
Topics (in English) SmartCom2024 
Paper Information
Registration To SR 
Conference Code 2024-11-SR 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance Improvement of Reinforcement Learning Based Energy-Efficient Beaconing Scheme in Bluetooth Low Energy 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) Bluetooth Low Energy  
Keyword(3) BLE advertising  
Keyword(4) Reinforcement learning  
Keyword(5) Multi-Armed Bandit problem  
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1st Author's Name Reona Nomura  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Ryosuke Isogai  
2nd Author's Affiliation SEIKO FUTURE CREATION INC. (SEIKO)
3rd Author's Name Yudai Imano  
3rd Author's Affiliation SEIKO FUTURE CREATION INC. (SEIKO)
4th Author's Name Yoshifumi Yoshida  
4th Author's Affiliation SEIKO FUTURE CREATION INC. (SEIKO)
5th Author's Name Hiroyuki Yasuda  
5th Author's Affiliation The University of Tokyo (UTokyo)
6th Author's Name Song-Ju Kim  
6th Author's Affiliation SOBIN Institute LLC (SOBIN)
7th Author's Name Maki Arai  
7th Author's Affiliation Tokyo University of Science (TUS)
8th Author's Name Mikio Hasegawa  
8th Author's Affiliation Tokyo University of Science (TUS)
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Speaker Author-1 
Date Time 2024-11-04 14:55:00 
Presentation Time 60 minutes 
Registration for SR 
Paper # SR2024-54 
Volume (vol) vol.124 
Number (no) no.233 
Page pp.17-23 
#Pages 7 
Date of Issue 2024-10-28 (SR) 

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