An Energy-Efficient Space-Air-Ground Integrated Network (SAGIN) Solution for Forest Threat Detection via Edge AI and Link-Aware Satellite Backhaul

Abstract

Forest monitoring in remote regions faces severe connectivity and energy constraints. This study presents an energy-efficient satellite IoT system for acoustic threat detection, serving as a foundational Space-Ground segment within the Space-Air-Ground Integrated Network (SAGIN) architecture. To overcome bandwidth bottlenecks, we propose a hierarchical edge computing architecture that performs local processing of acoustic data where an optimized TinyML model processes audio locally, achieving 92% detection accuracy with a minimal power draw of 18 mA and an ultra-low latency of 0.3 s. Furthermore, we introduce a renewal-based GEO satellite link model to capture temporal state transitions, establishing a theoretical foundation for link-aware transmission strategies. Based on this model, our analytical projections demonstrate that the link-aware strategy can reduce packet loss by 43% and decrease energy consumption by 45% compared to conventional blind transmissions. Powered exclusively by implemented solar energy harvesting, the system's robustness and sustainability were successfully validated through a continuous 6-month field deployment in a mountainous forest, proving its practical applicability for remote environmental monitoring.