Neuromorphic solar edge AI for sustainable wildfire detection
Por:
Parada, R
Publicada:
1 ene 2026
Ahead of Print:
1 ene 2026
Resumen:
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 4200 patrol hrs per yr, 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 hrs, 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). © 2025 The Author
Filiaciones:
Parada, R:
CERCA, Ctr Tecnol Telecomunicac Catalunya, Castelldefels 08860, Catalonia, Spain
FULL TEXT
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Accepted Version |
CC BY-NC-ND 4.0 |
| Accesible: 02/01/2027 |
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