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An index repository for Dr. Ray Gao's research on Internet of Things (IoT)-enabled smart built environment.

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Internet of Things Enabled Data Acquisition Framework for Smart Building Applications

This is a research program initiated by Dr. Ray Gao, formerly known as Xinghua Gao, in 2018, while he was working on his Ph.D. research under the guidance of Dr. Pardis Pishdad-Bozorgi and Dr. Dennis Shelden at the Georgia Institute of Technology. As a component of Dr. Gao’s Ph.D. thesis, the initial idea was to use the Internet of Things (IoT) to establish the “nervous system” of buildings, thereby creating the data foundation for future smart buildings (check the research paper).

In the years that followed, this research program gave rise to multiple associated projects, including:

Vision for the Future Smart City—An IoT Network of Smart Facilities

Based on the initial IoT-enabled facility data acquisition framework described in this paper, Dr. Gao proposes a vision for the future smart city: a network of smart buildings connected by IoT.

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In the envisioned IoT-enabled smart city, a network of smart buildings forms a city, each providing real-time data to the network. This data, collectively termed as the "basic facility data package" (BFDP), forms the foundation of various smart city applications. In addition to this, specific facilities provide "extra data", such as healthcare facilities providing medical resource information, or supermarkets providing real-time commodity data. These buildings not only provide data but also require services, referred to as the "basic facility service package" (BFSP) which includes security and emergency assistance among others. Some facilities might need "extra services", such as a shopping mall requesting real-time citizen flow information. The Cyber Physical System (CPS), acting as the city's heart, connects and synchronizes the physical and digital aspects of the city in real-time, thus offering extensive data analytics opportunities. It facilitates performance assessment against targets, future predictions based on historical data, and enables autonomous control and response to citizens' needs.

This Repository and the Source Code

This repository serves as the index for the related research results and early-stage legacy code, as described in this paper.

rpi_code contains the code that runs on the Raspberry Pi (we tested Raspberry Pi 4 model B and Raspberry Pi Zero; operating system: Raspberry Pi OS).

server_code contains the code that runs on the server.

Project Team and Contributors

Dr. Ray Gao

Sheik Murad Hassan Anik (soon to be PhD)

Dr. Na Meng

Dr. Pardis Pishdad-Bozorgi

Dr. Dennis Shelden

Dr. Philip Agee

Dr. Andrew McCoy

Dr. Angelos Stavrou

Dr. Shu Tang

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An index repository for Dr. Ray Gao's research on Internet of Things (IoT)-enabled smart built environment.

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