[2604.05045] PCA-Driven Adaptive Sensor Triage for Edge AI Inference

Computer Science > Machine Learning arXiv:2604.05045 (cs) [Submitted on 6 Apr 2026] Title:PCA-Driven Adaptive Sensor Triage for Edge AI Inference Authors:Ankit Hemant Lade, Sai Krishna Jasti, Nikhil Sinha, Indar Kumar, Akanksha Tiwari View a PDF of the paper titled PCA-Driven Adaptive Sensor Triage for Edge AI Inference, by Ankit Hemant Lade and 4 other authors View PDF HTML (experimental) 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). Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) ACM classes: I.2.6; C.3 Cite as: arXiv:2604.05045 [cs.LG]   (or arXiv:2604.05045v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.05045 Focus to lea...