Presentation | 2022-08-04 A Study of Low Power BLE Advertising Method Based on Reinforcement Learning Hiroyuki Yasuda, Minoru Fujisawa, Ryosuke Isogai, Yoshifumi Yoshida, Song-Ju Kim, Yozo Shoji, Kazuyuki Aihara, Mikio Hasegawa, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Bluetooth Low Energy (BLE) has been applied to various IoT services because of its versatility and energy efficiency. In BLE advertising, BLE devices continuously broadcast their information using up to three channels, and power saving can be achieved by efficiently reducing the number of channels and transmissions. In this paper, we propose a reinforcement learning method for autonomously determining the efficient number of channels and intervals, and evaluate the method through simulations. The proposed method can reduce up to 55.2% of power consumption by reducing the number of channels and transmissions without significant loss of reliability in environments with low interference, and can achieve over 99% advertising success rate by autonomously increasing the number of channels in environments with high interference. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Reinforcement learning / Bluetooth Low Energy / IoT / BLE advertising / Multi-armed bandit problem |
Paper # | CCS2022-33 |
Date of Issue | 2022-07-28 (CCS) |
Conference Information | |
Committee | IN / CCS |
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Conference Date | 2022/8/4(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Hokkaido University(Centennial Hall) |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Network Science, Future Network, Cloud/SDN/Virtualization, Contents Delivery/Contents Exchange, and others |
Chair | Kunio Hato(Internet Multifeed) / Megumi Akai(Hokkaido Univ.) |
Vice Chair | Tsutomu Murase(Nagoya Univ.) / Hidehiro Nakano(Tokyo City Univ.) / Masaki Aida(TMU) |
Secretary | Tsutomu Murase(KDDI Research) / Hidehiro Nakano(Nagaoka Univ. of Tech.) / Masaki Aida(NTT) |
Assistant | / Hiroyuki Yasuda(Univ. of Tokyo) / Hiroyasu Ando(Tsukuba Univ.) / Tomoyuki Sasaki(Shonan Inst. of Tech.) / Miki Kobayashi(Rissho Univ.) |
Paper Information | |
Registration To | Technical Committee on Information Networks / Technical Committee on Complex Communication Sciences |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Study of Low Power BLE Advertising Method Based on Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | Reinforcement learning |
Keyword(2) | Bluetooth Low Energy |
Keyword(3) | IoT |
Keyword(4) | BLE advertising |
Keyword(5) | Multi-armed bandit problem |
1st Author's Name | Hiroyuki Yasuda |
1st Author's Affiliation | The University of Tokyo(The Univ. of Tokyo) |
2nd Author's Name | Minoru Fujisawa |
2nd Author's Affiliation | Tokyo University of Science(Tokyo Univ. of Science) |
3rd Author's Name | Ryosuke Isogai |
3rd Author's Affiliation | SEIKO HOLDINGS CORPORATION(SEIKO HOLDINGS Corp.) |
4th Author's Name | Yoshifumi Yoshida |
4th Author's Affiliation | SEIKO HOLDINGS CORPORATION(SEIKO HOLDINGS Corp.) |
5th Author's Name | Song-Ju Kim |
5th Author's Affiliation | Tokyo University of Science(Tokyo Univ. of Science) |
6th Author's Name | Yozo Shoji |
6th Author's Affiliation | National Institute of Information and Communications Technology(NICT) |
7th Author's Name | Kazuyuki Aihara |
7th Author's Affiliation | The University of Tokyo(The Univ. of Tokyo) |
8th Author's Name | Mikio Hasegawa |
8th Author's Affiliation | Tokyo University of Science(Tokyo Univ. of Science) |
Date | 2022-08-04 |
Paper # | CCS2022-33 |
Volume (vol) | vol.122 |
Number (no) | CCS-145 |
Page | pp.pp.35-40(CCS), |
#Pages | 6 |
Date of Issue | 2022-07-28 (CCS) |