IoT Sensor Dataset Generator

What it does

The IoT Sensor Dataset Generator produces synthetic telemetry from temperature, humidity, and pressure sensors — up to 100,000 readings per run. Values follow realistic distributions (Gaussian noise around sensible baselines) and timestamps increment chronologically from your start date, so the output behaves like a real time series. Seeded runs regenerate the identical stream.

Common use cases

• IoT platform testing — device data for ingestion pipelines, rules engines, and alerting thresholds.
• Time-series databases — bulk loads for InfluxDB, TimescaleDB, or Prometheus-adjacent tooling; NDJSON export streams straight in.
• Monitoring dashboards — Grafana-style panels with data that has realistic jitter instead of sine waves.
• Analytics prototypes — rolling averages, anomaly detection, and downsampling logic against a known-shape signal.

Settings

• Sensor type — temperature, humidity, or pressure; each has its own realistic baseline and noise profile.
• Readings — 1 to 100,000 data points.
• Start date — timestamps advance from here at regular intervals.
• Seed — identical seed + settings = identical readings, for reproducible load tests and demos.

Privacy note

Readings are generated locally in your browser and never uploaded. No devices exist — the values are statistical simulations, not measurements — so the data carries no operational or personal information.

FAQ

Do the values look like real sensors? They wobble like real sensors: values cluster around a plausible baseline with Gaussian noise, rather than sweeping unrealistically. Good enough for dashboards, thresholds, and aggregation logic.

Are timestamps evenly spaced? Yes — readings increment at regular intervals from the start date, the common case for polling-based telemetry.

Can I simulate an anomaly? Not directly — the signal is well-behaved by design. A practical pattern: generate the baseline here, then splice in hand-crafted spikes where your alerting should fire.