PCA-Driven Adaptive Sensor Triage for Edge AI Inference
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913057521795072 |
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| author | Lade, Ankit Hemant Jasti, Sai Krishna Sinha, Nikhil Kumar, Indar Tiwari, Akanksha |
| author_facet | Lade, Ankit Hemant Jasti, Sai Krishna Sinha, Nikhil Kumar, Indar Tiwari, Akanksha |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05045 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PCA-Driven Adaptive Sensor Triage for Edge AI Inference Lade, Ankit Hemant Jasti, Sai Krishna Sinha, Nikhil Kumar, Indar Tiwari, Akanksha Machine Learning Artificial Intelligence Systems and Control I.2.6; C.3 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). |
| title | PCA-Driven Adaptive Sensor Triage for Edge AI Inference |
| topic | Machine Learning Artificial Intelligence Systems and Control I.2.6; C.3 |
| url | https://arxiv.org/abs/2604.05045 |