Energy Efficient Learning-Based Indoor Multi-Band WLAN for Smart Buildings

Broadband Internet access in the building has fundamentally changed almost every aspect of our lives. As the growing use of indoor portable systems and devices, saving their energy consumption becomes an interesting and important issue for smart buildings. Recently, multi-band WLAN where 2.4/5-GHz a...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.34324-34333
Main Authors: Wang, Xiaoyan, Umehira, Masahiro, Otsu, Hiroyuki, Kawatani, Takyuya, Takeda, Shigeki
Format: Article
Language:English
Subjects:
Broadband
Energy consumption
Energy efficiency
indoor environment
Indoor environments
Learning
learning mechanism
Local area networks
multi-band WLAN
Portable equipment
Radio frequency
Reflected waves
Service areas
smart building
Smart buildings
Wireless communication
Wireless LAN
Wireless networks
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Summary:Broadband Internet access in the building has fundamentally changed almost every aspect of our lives. As the growing use of indoor portable systems and devices, saving their energy consumption becomes an interesting and important issue for smart buildings. Recently, multi-band WLAN where 2.4/5-GHz and 60-GHz bands coexist is a promising solution to offer both ultra-high speed and robust wireless connections. For a multi-band WLAN end device, detecting the available service areas in an energy efficient way is of great importance. In the existing systems, the RF units of device need to be turned on and kept listening all the time, which leads to substantial energy consumption overhead. To solve this problem, this paper proposes an energy efficient learning-based indoor multi-band WLAN system, in which the end device predicts the distinct service areas by learning the influences of reflected waves in buildings. We have performed extensive experiments in different indoor environments, and the evaluation results demonstrate that the proposed mechanism could substantially improve the performance compared with the existing approaches.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2849094