A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.
A TinyML Approach to Non -Repudiable Anomaly Detection in Extreme Industrial Environments
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data
A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.