Is this real sensor data?

No. Every reading is generated locally in your browser using a random walk around realistic baselines. Nothing is captured from a real device and no data leaves your browser.

What metrics are included?

Each row has a device ID, an ISO timestamp, temperature in Celsius, relative humidity as a percentage, and atmospheric pressure in hectopascals. Values stay within physically plausible ranges.

How realistic is the time series?

Each metric uses a random walk: the next value drifts a small amount from the previous one rather than jumping randomly. This produces smooth, correlated trends that look like real sensor telemetry rather than noise.

How are timestamps spaced?

Readings are evenly spaced by the interval you set, counting backward from now. With a 60-second interval and 100 readings, the series spans the last 100 minutes ending at the current time.

Can I simulate multiple devices?

Yes. Set the device count and the tool emits a full series for each device, each with its own ID and independent random walk, interleaved by device in the output.

Sample IoT Sensor Dataset Generator

This generator produces realistic time-series telemetry for fake IoT sensors so you can build and test dashboards, time-series databases, and ingestion pipelines without wiring up real hardware. Everything runs locally in your browser.

How it works

Each device emits a series of readings using a random walk so the values trend smoothly instead of jumping around:

next = clamp(previous + random(-step, +step), min, max)

Temperature starts near 21 °C, humidity near 45%, pressure near 1013 hPa.
Each new reading drifts a small amount from the previous one, then is clamped to a physically plausible range.
Timestamps are evenly spaced by your chosen interval, counting backward from the current time so the latest reading is now.

Output is CSV with a header or pretty-printed JSON, with each device’s series generated independently.

Tips and notes

Because the random walk produces correlated trends, the data charts well as smooth lines rather than scatter. Use a small interval and many readings for a dense, high-resolution series, or a large interval for a sparse long-range view. Each generation is independently random — save the output if you need a fixed dataset.