Sustainable Hierarchical Networking for Extreme-Edge AI-Based Wildlife Monitoring in Resource-Constrained Environments

Deploying artificial intelligence (AI) for wildlife monitoring in remote environments remains challenging due to limited energy, computational resources, and unreliable communication. Conventional edge AI systems rely on continuous sensing, high-power communication, and full-precision models, resulting in excessive energy consumption. This paper presents a green-energy-based hierarchical network architecture for sustainable wildlife monitoring at the extreme edge. The framework distributes sensing, communication, and AI inference across edge, far-edge, and extreme-edge layers. A low-power IEEE 802.11ah network with on-demand channel allocation reduces transmission overhead, while passive infrared (PIR)-based event-driven activation replaces continuous camera operation. Model quantization (FP32, FP16, INT8) and distributed split inference improve computational efficiency, and tracking-assisted communication eliminates redundant transmissions. Experimental results demonstrate significant sustainability improvements. PIR-based operation reduces daily energy consumption by 95.11% compared with continuous operation. Tracking-assisted communication reduces both bandwidth requirements and energy consumption by more than 99% (p<0.001). Token-based channel allocation provides 39.4% energy savings compared with Target Wake Time (TWT)-based scheduling (sensitivity range: 29.2–45.1%). INT8 quantization delivers 4.7× higher throughput, 61% lower memory usage, and 39% lower power consumption, with a 2.6% mean average precision (mAP) reduction. Field validation confirms the INT8 model’s effectiveness with an overall mAP of 82.89% (86.40% daytime, 79.37% nighttime) and precision above 96%. Combined with PIR activation, the INT8 system achieves 63.76 Wh/day—a 95.17% reduction versus FP32 continuous operation. The integration of renewable energy harvesting, efficient sensing, lightweight AI, and communication optimization enables scalable, autonomous, and sustainable wildlife monitoring in resource-constrained environments.