| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - time-series-forecasting |
| - tabular-classification |
| language: |
| - en |
| tags: |
| - synthetic-data |
| - iot |
| - sensor |
| - telemetry |
| - time-series |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
| # FreeSyntheticIoTTelemetry50M |
|
|
| A free dataset of 50 million fully synthetic IoT sensor readings, built for developers and researchers who need realistic device telemetry at scale — for testing IoT platforms, device-monitoring dashboards, edge-computing pipelines, or anomaly detection on sensor streams. No real devices or deployments are represented in this data. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | reading_id | string | Unique reading identifier | |
| | device_id | string | Synthetic device identifier | |
| | sensor_type | string | temperature, humidity, pressure, motion, voltage, co2, light, vibration | |
| | reading_value | float | Sensor reading, range depends on sensor type | |
| | unit | string | Measurement unit (C, %, hPa, V, ppm, lux, g, bool) | |
| | location_zone | string | Deployment zone (zone-a, warehouse-1, rooftop, etc.) | |
| | battery_level | int | Device battery level, 1-100 | |
| | signal_strength_dbm | int | Signal strength in dBm (negative) | |
| | reading_timestamp | string | Reading time (YYYY-MM-DD HH:MM:SS) | |
| | status | string | ok, warning, error, or offline | |
| |
| ## Format |
| |
| Single Parquet file, Snappy compression, ~1.3 GB, 50,000,000 rows. |
| |
| ## Quick Start |
| |
| **pandas** |
| ```python |
| import pandas as pd |
| df = pd.read_parquet("telemetry_50M.parquet") |
| ``` |
| |
| **datasets** |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("ziadatalabs/FreeSyntheticIoTTelemetry50M") |
| ``` |
| |
| **duckdb** |
| ```python |
| import duckdb |
| duckdb.sql("SELECT * FROM 'telemetry_50M.parquet' LIMIT 10").show() |
| ``` |
| |
| ## Notes |
| |
| Each sensor type produces readings within a physically plausible range — temperatures look like temperatures, voltage like a battery cell, pressure like real atmospheric readings — rather than uniform-random numbers, so the data is usable for anomaly-detection and time-series work. Battery level, signal strength, and status distributions reflect typical device-fleet behavior. All entirely synthetic. |
| |
| ## License & Usage |
| |
| Released under CC BY-NC 4.0 — personal, research, and educational use permitted, attribution required, no commercial use. |
| |
| --- |
| |
| Created by Zia Data Labs. Questions or feedback: zia.data.team@protonmail.com |
| |
