用PCA动态分配传感器采样率，节省带宽同时保持高精度
PCA-Driven Adaptive Sensor Triage for Edge AI Inference
- 根据增量PCA结果按通道比例分配采样率，实时适应带宽限制
- 在50%带宽下3个数据集表现最佳，TPE上F1达0.961接近全量数据
- 无需训练参数，对丢包和噪声鲁棒，适合工业物联网边缘推理
工业物联网中的多通道传感器网络常超出可用带宽。本文提出PCA-Triage，一种流式算法，将增量PCA载荷转化为在带宽预算下的按通道比例采样率。该算法时间复杂度为O(wdk)，无可训练参数（每决策耗时0.67毫秒）。在7个基准测试（8–82个通道）上对比9种基线方法。在50%带宽下，PCA-Triage是6个数据集中3个的最佳无监督方法，6个中5个优于所有基线，效应量大（r = 0.71–0.91）。在TEP数据集上，其F1达到0.961 ± 0.001，仅比全数据性能低0.1%；在30%预算下仍维持F1 > 0.90。针对性扩展后F1提升至0.970。算法对丢包和传感器噪声具有鲁棒性（最坏情况下退化3.7–4.8%）。
原文摘要 · Abstract (English)
Multi-channel sensor networks in industrial IoT often exceed available bandwidth. We propose PCA-Triage, a streaming algorithm that converts incremental PCA loadings into proportional per-channel sampling rates under a bandwidth budget. PCA-Triage runs in O(wdk) time with zero trainable parameters (0.67 ms per decision). We evaluate on 7 benchmarks (8--82 channels) against 9 baselines. PCA-Triage is the best unsupervised method on 3 of 6 datasets at 50% bandwidth, winning 5 of 6 against every baseline with large effect sizes (r = 0.71--0.91). On TEP, it achieves F1 = 0.961 +/- 0.001 -- within 0.1% of full-data performance -- while maintaining F1 > 0.90 at 30% budget. Targeted extensions push F1 to 0.970. The algorithm is robust to packet loss and sensor noise (3.7--4.8% degradation under combined worst-case).
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