Probabilistic Graph Neural Inference for wildfire evacuation logistics networks for low-power autonomous deployments

说明与用途

为低功耗边缘设备构建轻量级概率图神经网络，用于野火疏散路径规划，将Raspberry Pi上的推理时间降低十倍，同时保持路径风险排序。

问题

Standard GNN inference on a 4 GB Raspberry Pi without GPU caused latency in seconds per inference and drained the battery within an afternoon.

结果

The probabilistic GNN achieved near‑real‑time inference on a low‑power Pi, cutting compute by an order of magnitude and enabling practical evacuation routing under uncertainty.

工作流

• 1. Deploy Pi sensor nodes along trail network. 2. Attempt standard GNN inference → high latency, battery drain. 3. Study probabilistic graphical models and quantized inference. 4. Design a two‑layer probabilistic message‑passing GNN with Gaussian messages. 5. Implement moment‑matching aggregation to approximate distribution combination. 6. Quantize the model for low‑power execution. 7. Validate that risk‑ordering of routes is preserved and latency is acceptable.

证据

When I actually tried to run a standard GNN inference stack on a 4GB Pi with no GPU, the whole thing collapsed. Latency ballooned to seconds per inference, and my battery budget evaporated within an afternoon.

来源

The trick that made this tractable on-device was treating the aggregation as a moment‑matching operation rather than a full distributional combination. It's an approximation — you lose some higher‑order structure — but in my testing it preserved the risk‑ordering of routes almost perfectly while cutting compute by an order of magnitude.

来源