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Advanced Wireless Sensor Networks for Sustainable Buildings Using Building Ducts

Author

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  • Heekwon Yang

    (Department of Electronics and Communications Engineering, Hanyang University, ERICA Campus, Ansan 15588, Korea)

  • Byeol Kim

    (Department of Architectural Engineering, Hanyang University, ERICA Campus, Ansan 15588, Korea)

  • Joosung Lee

    (Department of Architectural Engineering, Hanyang University, ERICA Campus, Ansan 15588, Korea)

  • Yonghan Ahn

    (Department of Architectural Engineering, Hanyang University, ERICA Campus, Ansan 15588, Korea)

  • Chankil Lee

    (Department of Electronics and Communications Engineering, Hanyang University, ERICA Campus, Ansan 15588, Korea)

Abstract

The communication technology ZigBee has been widely adopted in wireless sensor networks (WSNs) for a wide range of industrial applications. However, although ZigBee provides low-power, low-cost mesh networking, it cannot guarantee steady and predictable network performance as channels are time-variant and highly attenuated by man-made obstacles. The networks also suffer from interference, especially in the important 2.4 GHz industrial, scientific, and medical (ISM) band. These degraded channel characteristics increase the number of hops, thus increasing both the packet error rate and transmission delays. In this paper, we report the deployment of a ZigBee-based WSN inside an existing building duct system utilized for intelligent waste collection in an industrial environment. The Received Signal Strength (RSS) and path losses were measured, revealing that the duct communication channel acts as a very effective waveguide, providing a more reliable and consistent network performance than conventional free space channels.

Suggested Citation

  • Heekwon Yang & Byeol Kim & Joosung Lee & Yonghan Ahn & Chankil Lee, 2018. "Advanced Wireless Sensor Networks for Sustainable Buildings Using Building Ducts," Sustainability, MDPI, vol. 10(8), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2628-:d:160112
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    References listed on IDEAS

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    1. Zahra Pooranian & Jemal H. Abawajy & Vinod P & Mauro Conti, 2018. "Scheduling Distributed Energy Resource Operation and Daily Power Consumption for a Smart Building to Optimize Economic and Environmental Parameters," Energies, MDPI, vol. 11(6), pages 1-17, May.
    2. Ali Hassan Sodhro & Sandeep Pirbhulal & Arun Kumar Sangaiah & Sonia Lohano & Gul Hassan Sodhro & Zongwei Luo, 2018. "5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices," Sustainability, MDPI, vol. 10(4), pages 1-17, April.
    3. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
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    Cited by:

    1. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).

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