An efficient beaconing of bluetooth low energy by decision making algorithm

被引：1

作者：

Fujisawa M. ^{[1
]}

Yasuda H. ^{[1
,2
]}

Isogai R. ^{[3
]}

Arai M. ^{[1
]}

Yoshida Y. ^{[3
]}

Li A. ^{[1
,4
]}

Kim S.-J. ^{[1
,5
]}

Hasegawa M. ^{[1
]}

机构：

[1] Department of Electrical Engineering, Tokyo University of Science, 6–3–1 Niijuku, Tokyo, Katsushika-ku

[2] Graduate Schools for Law and Politics, The University of Tokyo, 7–3–1 Hongo, Tokyo, Bunkyo-ku

[3] Seiko Future Creation Inc., 563, Takatsukashinden, Chiba, Matsudo-shi

[4] Graduate School of Informatics and Engineering, The University of Electro-Communications, 1–5–1 Chofugaoka, Tokyo, Chofu

[5] SOBIN Institute LLC., 3–38–7 Keyakizaka, Hyogo, Kawanishi

来源：

Discover Artificial Intelligence | 2024年 / 4卷 / 01期

基金：

日本学术振兴会;

关键词：

BLE advertising; Bluetooth low energy; Decision-making; IoT; Multi-armed bandit problem; Reinforcement learning;

D O I：

10.1007/s44163-024-00122-7

中图分类号：

学科分类号：

摘要：

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it’s especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments. © The Author(s) 2024.

引用

共 32 条

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