Year: 2025
Type: conference-paper
Abstract: Wildfires are an escalating environmental concern, closely linked to power grid infrastructure in two significant ways. High-voltage power lines can inadvertently spark wildfires when they contact vegetation, while wildfires originating elsewhere can damage the power grid, causing severe disruptions. This paper proposes a self-powered cyber-physical system framework with sensing, processing, and communication capabilities to enable early wildfire detection. The proposed framework first analyzes the probability of the presence of a wildfire using lightweight smoke detection models that can be deployed on embedded processors at the edge. Then, it identifies the Pareto-optimal configurations that co-optimize the wildfire detection probability and expected time to detect a wildfire under energy constraints. Experimental evaluations on Jetson Orin Nano and STM Nucleo boards show that the Pareto-optimal solutions achieve wildfire detection within 5-15 minutes while consuming 1.2-3.5× lower energy than transmitting images to the cloud.
Authors: Nuriye Yildirim, Gyeongju Kim, Minwoo Yun, Nivetha Elango, Jaehyun Park, Umit Y. Ogras
Institutions: University of Wisconsin–Madison, University of Ulsan
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411150229