I recently led the successful field deployment of a lightweight, networked edge-AI wildfire detection system in the Carson National Forest, working closely with Kit Carson Electric Cooperative (KCEC). Over two days in the field, Fengyu Wang and I installed several ultra-low-cost, energy-conscious devices in wildfire-prone areas. These units form a small edge-AI network capable of identifying early wildfire indicators, supporting situational awareness, and interfacing with microgrid controllers to help reduce potential impacts to critical infrastructure and maintain service continuity during high-risk events.
Wildfire remains one of the most significant risks for KCEC and for many communities in Northern New Mexico. Seeing research and ideas progress from early concepts to real-world deployments is profoundly meaningful and continues to shape the direction of my work in resilient energy systems.
This deployment also marks the sixth system I have had the opportunity to design and bring into practice over the past thirteen years—spanning research labs, a technology startup, and now academia. Each project has deepened my understanding of how field environments test assumptions and drive the evolution of practical, scalable solutions.
More deployments and testing are planned in the coming year, and the data generated from this system will contribute to my ongoing research in digital twins, edge intelligence, and energy resilience.
For more info, check: https://newsroom.nmsu.edu/news/nmsu-engineering-professors-develop-ai-detection-for-wildfires/s/09d5d29e-8dd6-4c7a-be88-577568476463
https://www.yahoo.com/news/articles/nmsu-professors-propose-ai-wildfire-225332540.html
