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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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
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
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)