PCA-Driven Adaptive Sensor Triage for Edge AI Inference

arXiv:2604.05045v1 Announce Type: new
Abstract: 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).