PCA-Driven Adaptive Sensor Triage for Edge AI Inference
This work addresses bandwidth limitations for edge AI inference in industrial IoT, presenting an incremental improvement with strong specific gains.
The paper tackles the problem of bandwidth constraints in multi-channel sensor networks for industrial IoT by proposing PCA-Triage, a streaming algorithm that adaptively allocates sampling rates per channel under a bandwidth budget, achieving within 0.1% of full-data performance on one dataset and maintaining high F1 scores at reduced budgets.
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).