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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SRW2021-65 |
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) |
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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 |
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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 |
6 |
Date of Issue |
2022-02-23 (SRW) |
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