Qualitative Study of The Perception of Wildfire Risk; IoT Sensor System for Wildfire Detection
Ross, Alexander, School of Engineering and Applied Science, University of Virginia
Powell, Harry, EN-Elec/Computer Engr Dept, University of Virginia
Ku, Tsai-Hsuan, EN-Engineering and Society, University of Virginia
The recent phenomenon known as the Internet of Things (IoT) shows us a glimpse into a future where people rely on smart connected devices to solve various problems. However, people have used IoT devices primarily in dense, urban areas, and homes because they rely on existing electronic network technologies such as Ethernet, Wi-Fi, and Bluetooth. These technologies depend on devices’ proximities to each other to transmit data reliably. However, with the emergence and ubiquity of novel wireless technologies such as Zigbee and LoRa (with the proper antennas) and ultra-low-power hardware, it is now possible to use IoT devices remotely in rural areas to gather data from a broader geographical surface area. Doing so enables more excellent coverage and data acquisition from locations that were previously difficult to monitor. The emergence of these new technologies enables engineers to solve an even greater host of problems based on wireless technologies’ increased capabilities and reliability. The sensor system designed creates a distributed IoT network that can detect and monitor hazardous conditions, such as wildfires, remotely to help humans respond to these threats and prevent large scale fires and other infrastructure damages.
Wildfire risks have been increasing across the US, and historical public perception of the risk of wildfire has been low. This paper surveys that risk perception currently, to find that public risk perception is still low. The paper finds several economic and psychological factors to explain the phenomena for why risk perception is low for a social technical system such as fighting forest fires.
BS (Bachelor of Science)
Wildfires, Internet of Things, Risk Perception
School of Engineering and Applied Science
Bachelor of Science in Electrical Engineering
Technical Advisor: Harry Powell
STS Advisor: Sharon Tsai-hsuan Ku
Technical team: Nathan Do, Shreejan Gupta, Tahmid Kazi, and Bill Yang