ARotting/edge-sentinel-telemetry

Edge Sentinel

Edge Sentinel is a classical machine-learning benchmark for industrial telemetry. It detects sensor drift, actuator mismatch, vibration faults, pressure spikes, and network floods across simulated devices.

The evaluation split holds out entire devices, not random rows, reducing leakage from device-specific operating patterns.

Verified results

The final threshold and model weighting were selected on devices 8 and 9. Devices 10 and 11 were used once for the held-out test:

Model	ROC-AUC	Average precision	F1	Recall	False-positive rate
Isolation Forest	0.9259	0.6954	0.6889	0.7000	0.0444
Gradient boosting	0.9775	0.9597	0.9308	0.9288	0.0090

The validation search assigned the supervised model a weight of 1.0, so the final artifact is not described as an ensemble improvement. The Isolation Forest remains useful as a label-free baseline.

The test confusion matrix was [[8741, 79], [84, 1096]] across 10,000 observations.

Dataset

• 60,000 simulated timestamped observations across 12 devices;
• 26 raw and engineered numeric features;
• five anomaly families: drift, mismatch, vibration fault, pressure spike, and network flood;
• train devices 0-7, validation devices 8-9, test devices 10-11.

The generated Parquet files are directly loadable with Hugging Face Datasets.

Reproduce

uv run python projects/edge-sentinel-ml/generate_data.py
uv run python projects/edge-sentinel-ml/train.py

The fitted artifact and complete metric report are written to artifacts/edge-sentinel-ensemble/.