Neuromorphic solar edge AI for sustainable wildfire detection
Raúl Parada · Internet of Things · 2025
This paper presents a feasibility study of a solar-autonomous wildfire detection system using neuromorphic edge AI on fixed-wing drones. Through a comprehensive year-long simulation over Parc del Garraf (Catalonia), we evaluate three edge computing platforms, Raspberry Pi 4, Google Coral TPU, and BrainChip Akida, integrated into solar-optimized eBee X drones. Results show that the BrainChip Akida achieves 4,200 patrol hours per year, nearly three times that of traditional CPU systems, while maintaining 87% solar energy autonomy. The Google Coral TPU and Raspberry Pi 4 reach 66% and 52% autonomy, respectively. Fleet scaling analysis demonstrates that increasing drone count from one to eight reduces median wildfire detection time from 18 to 2.2 hours, surpassing critical response thresholds. Seasonal analysis reveals Akida-based systems can operate fully on solar energy during summer and most of spring and fall, minimizing grid dependency. These findings establish neuromorphic computing as a foundational technology for sustainable, perpetual environmental monitoring within the Internet of Robotic Things (IoRT).