An efficient beaconing of bluetooth low energy by decision making algorithm
Abstract 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 reduc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-04-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-024-00122-7 |
| _version_ | 1827277046918676480 |
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| author | Minoru Fujisawa Hiroyuki Yasuda Ryosuke Isogai Maki Arai Yoshifumi Yoshida Aohan Li Song-Ju Kim Mikio Hasegawa |
| author_facet | Minoru Fujisawa Hiroyuki Yasuda Ryosuke Isogai Maki Arai Yoshifumi Yoshida Aohan Li Song-Ju Kim Mikio Hasegawa |
| author_sort | Minoru Fujisawa |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-24T07:14:21Z |
| format | Article |
| id | doaj.art-a852cbcb7f3240fd812d03b509dfd4a6 |
| institution | Directory Open Access Journal |
| issn | 2731-0809 |
| language | English |
| last_indexed | 2024-04-24T07:14:21Z |
| publishDate | 2024-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj.art-a852cbcb7f3240fd812d03b509dfd4a62024-04-21T11:24:51ZengSpringerDiscover Artificial Intelligence2731-08092024-04-014111510.1007/s44163-024-00122-7An efficient beaconing of bluetooth low energy by decision making algorithmMinoru Fujisawa0Hiroyuki Yasuda1Ryosuke Isogai2Maki Arai3Yoshifumi Yoshida4Aohan Li5Song-Ju Kim6Mikio Hasegawa7Department of Electrical Engineering, Tokyo University of ScienceDepartment of Electrical Engineering, Tokyo University of ScienceSeiko Future Creation Inc.Department of Electrical Engineering, Tokyo University of ScienceSeiko Future Creation Inc.Department of Electrical Engineering, Tokyo University of ScienceDepartment of Electrical Engineering, Tokyo University of ScienceDepartment of Electrical Engineering, Tokyo University of ScienceAbstract 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.https://doi.org/10.1007/s44163-024-00122-7IoTBluetooth low energyBLE advertisingDecision-makingReinforcement learningMulti-armed bandit problem |
| spellingShingle | Minoru Fujisawa Hiroyuki Yasuda Ryosuke Isogai Maki Arai Yoshifumi Yoshida Aohan Li Song-Ju Kim Mikio Hasegawa An efficient beaconing of bluetooth low energy by decision making algorithm Discover Artificial Intelligence IoT Bluetooth low energy BLE advertising Decision-making Reinforcement learning Multi-armed bandit problem |
| title | An efficient beaconing of bluetooth low energy by decision making algorithm |
| title_full | An efficient beaconing of bluetooth low energy by decision making algorithm |
| title_fullStr | An efficient beaconing of bluetooth low energy by decision making algorithm |
| title_full_unstemmed | An efficient beaconing of bluetooth low energy by decision making algorithm |
| title_short | An efficient beaconing of bluetooth low energy by decision making algorithm |
| title_sort | efficient beaconing of bluetooth low energy by decision making algorithm |
| topic | IoT Bluetooth low energy BLE advertising Decision-making Reinforcement learning Multi-armed bandit problem |
| url | https://doi.org/10.1007/s44163-024-00122-7 |
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