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Scholarly Works, Myers-Lawson School of Construction

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https://hdl.handle.net/10919/111114

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Browsing Scholarly Works, Myers-Lawson School of Construction by Subject "0905 Civil Engineering"

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    A Cost-Effective, Scalable, and Portable IoT Data Infrastructure for Indoor Environment Sensing
    Anik, Sheik; Gao, Xinghua; Meng, Na; Agee, Philip; McCoy, Andrew P. (2022-05-15)
    The vast number of facility management systems, home automation systems, and the ever-increasing number of Internet of Things (IoT) devices are in constant need of environmental monitoring. Indoor environment data can be utilized to improve indoor facilities and better occupants’ working and living experience, however, such data are scarce because many existing facility monitoring technologies are expensive and proprietary for certain building systems. With the aim of addressing the indoor environment data availability issue, the authors designed and prototyped a cost-effective, distributed, scalable, and portable indoor environmental data collection system, Building Data Lite (BDL). BDL is based on Raspberry Pi computers and multiple changeable arrays of sensors, such as sensors of temperature, humidity, light, motion, sound, vibration, and multiple types of gases. The system includes a distributed sensing network and a centralized server. The server provides a web-based graphical user interface that enables users to access the collected data over the Internet. To evaluate the BDL system’s functionality, cost effectiveness, scalability, and portability, the research team conducted a case study in an affordable housing community where the system prototype is deployed to 12 households. The results indicate that the system is functioning as designed, costs $73 per zone and provides 12 types of indoor environment data, is easy to scale up, and is fully portable. This research contributes to the body of knowledge by proposing an innovative way for establishing a distributed wireless IoT data infrastructure for indoor environment sensing in new or existing buildings.
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