Date of Award

August 2021

Document Type


Degree Name

Master of Science (MS)


Computer Engineering

Committee Member

Hai Xiao

Committee Member

Adam W. Hoover

Committee Member

Richard R. Brook


In the U.S., buildings are usually responsible for about 40% of total energy consumption, indicating a great potential for energy saving. It is reported that in EU countries, about 60% to 70% of the consumed energy in buildings are for space heating, and the rest of the energy are mostly accounted by hot water and electrical appliance. On the other hand, the indoor climate comfort is strongly related to the life quality and productivity of the occupants. There is no doubt that any improvement in building control and optimization will lead to a significant energy savings while still maintain the comfort level. Sensors are crucial in the optimal control and operation of buildings for improved energy efficiency, environment comfort, safety, and security. Building management and control strategies such as building commissioning, damper fault detection, demand-controlled ventilation, duct leakage diagnostics, and optimal whole-building control can all be improved through sensors. Temperature and humidity sensors are critical to control and optimize the operation of heating, ventilation and air conditioning (HVAC) in commercial and residential buildings. We designed, implemented, and tested a cloud-based low-cost distributed temperature and humidity monitoring system for collecting temperature and humidity readings from different locations in a residential building. The system includes distributable wireless sensors, a gateway hub a cloud-based data center and a graphic web dashboard. The wireless sensor is designed with a small size, energy-efficiency, and low-cost. The LoRa protocol is used to communicate between the sensors and the hub. The hub is built around a Raspberry pi to forward the sensor data to the cloud-based data center. The cloud-based data center is implemented on Amazon Web Services to receive, process and store sensor data. Also, the cloud-based data center provides interfaces for managing sensors or querying the data. A web dashboard application is also developed to provide a friendly interface for users to manage the sensor and visualize data. Compared to existing systems, the developed system features small sensor unit size, fast measurement, long transmission range, cloud-based APIs for convenient data storage and analysis, and web applications for system management and data visualization.



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