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Date of Award
Bachelor of Science (BS)
Department of Integrated Science and Technology
Water quality monitoring traditionally occurs via resource intensive field surveys, such as when a researcher manually collects data in a stream. Limiting factors such as time, money, and accessibility often result in less oversight of impaired water bodies, significantly threatening ecosystemic health and related ecosystem services. According to the United States Environmental Protection Agency, 84% of rivers and streams within the United States remain unassessed, resulting in significant lapses in available data. Such lapses prohibit efficient and effective monitoring, restoration, and conservation efforts throughout the United States. The objective of this project was to employ an unmanned aerial vehicle to remotely collect data regarding water quality from a wireless sensor network. The site under analysis was Boones Run, a tributary of the South Fork of the Shenandoah River near Elkton, Virginia. This project served as a proof-of-concept that communication with a wireless sensor node has the capability to be deployed to collect data in remote areas efficiently and effectively. This system would be useful in areas where accessibility is difficult, and transmission of data for processing is not readily available due to the lack of network connectivity. Initial analysis of environmental data gathered by hand indicated that surrounding land use had a significant impact on Boones Run water quality. This conclusion was reached given the trends seen in dissolved oxygen, water temperature, pH, and conductivity data from upstream to downstream over time. The completion of this project also lead to the successful data flow amongst all parts in the wireless sensor network. Three sensors soldered to a breadboard and connected to an Arduino Uno were able to gather data and send it to a Raspberry Pi 0. The Raspberry Pi 0 acted as a temporary storage device for the data before it was sent wirelessly to a Raspberry Pi 3 acting as an access point. The Raspberry Pi 3 device was mounted to an unmanned aerial vehicle so it could be flown over the node to decrease data collection time as well as adding the ability to collect data from places that are otherwise difficult for humans to access.
Valentino, Gina; Yates, Laura; and Potter, Brooke, "Environmental monitoring using a drone-enabled wireless sensor network" (2019). Senior Honors Projects, 2010-current. 723.