Context
Environmental sensor telemetry data, detailed in the blog post, Getting Started with IoT Analytics on AWS, published on Towards Data Science.
Content
The data was generated from a series of three identical, custom-built, breadboard-based sensor arrays. Each array was connected to a Raspberry Pi devices. Each of the three IoT devices was placed in a physical location with varied environmental conditions.
| device | environmental conditions |
|-------------------|------------------------------------------|
| 00:0f:00:70:91:0a | stable conditions, cooler and more humid |
| 1c:bf:ce:15:ec:4d | highly variable temperature and humidity |
| b8:27:eb:bf:9d:51 | stable conditions, warmer and dryer |
Each IoT device collected a total of seven different readings from the four sensors on a regular interval. Sensor readings include temperature, humidity, carbon monoxide (CO), liquid petroleum gas (LPG), smoke, light, and motion. The data spans the period from 07/12/2020 00:00:00 UTC – 07/19/2020 23:59:59 UTC. There is a total of 405,184 rows of data.
The sensor readings, along with a unique device ID and timestamp, were published as a single message, using the ISO standard Message Queuing Telemetry Transport (MQTT) network protocol. Below is an example of an MQTT message payload.
{
"data": {
"co": 0.006104480269226063,
"humidity": 55.099998474121094,
"light": true,
"lpg": 0.008895956948783413,
"motion": false,
"smoke": 0.023978358312270912,
"temp": 31.799999237060547
},
"device_id": "6e:81:c9:d4:9e:58",
"ts": 1594419195.292461
}
Columns
There are nine columns in the dataset.
| column | description | units |
|----------|----------------------|------------|
| ts | timestamp of event | epoch |
| device | unique device name | string |
| co | carbon monoxide | ppm (%) |
| humidity | humidity | percentage |
| light | light detected? | boolean |
| lpg | liquid petroleum gas | ppm (%) |
| motion | motion detected? | boolean |
| smoke | smoke | ppm (%) |
| temp | temperature | Fahrenheit |
Detail Image of Sensors