Objective of this repository is to provide code and instruction on how to turn your Raspberry Pi into an air measuring device. Given the correlation of Covid-19 particale density due to breathing and CO2, this can be be used (without warranty) as an indicator when the room needs ventilation.
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| Visualization of air quality |
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| Photo of setup with LCD screen |
Disclaimer: This tutorial is in parts based on the Unravel Github Repository (hence GPL 3.0)
- Raspberry Pi 4
- SCD30 Air Quality Sensor or the Seed Studio version (with Grove connector)
- 5" LCD Display
Optional:
- In case you use the Seed Studio Sensor: Grove - 4 pin Female Jumper to Grove 4 pin Conversion Cable
- HDMI/USB cables for connecting the screen to the Raspberry Pi
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Illustration of how to connect the SCD30 (Seed Studio) with Grove connector to Raspberry Pi. Image source in parts: link
Once you have connected your SCD30 to the Raspberry Pi, you can verify if it can be found:
i2cdetect -y 1 0x61 0x61If everything was wired correctly, this should appear in the terminal:
0 1 2 3 4 5 6 7 8 9 a b c d e f
00:
10:
20:
30:
40:
50:
60: 61
70: The SDC30 is connected to the Raspberry via the GPIO IC2 bus. To control the GPIO we need the pigpio library. To install the library:
wget https://github.com/joan2937/pigpio/archive/master.zip
unzip master.zip
cd pigpio-master
make
sudo make installIn order to launch the pigpio daemon launch:
sudo pigpiod
If everything is installed and working properly, the following command should return some integer value:
pigs hwver
In I2C communication, the master device determines the clock speed. However, there are situations where an I2C slave is not able to co-operate at the clock speed determined by the master device. To faciliate communication under these circumstances the clock speed needs to slow down. The mechanism behind this slow-down is referred to as clock stretching. For communicating with the SDC30, we need the clock cycles to be stretched to 200ms. To do so, we need a little tool.
First, clone the resposity from Github:
https://github.com/raspihats/raspihats/tree/master/clk_stretch
Second, compile the source code:
gcc -o i2c1_set_clkt_tout i2c1_set_clkt_tout.c
gcc -o i2c1_get_clkt_tout i2c1_get_clkt_tout.c
In order to stretch the clock cycle to 200 ms, run the following command:
./i2c1_set_clkt_tout 20000
In order to store the measurement, we leverage a open source database for time series - influxdb. Doing so allows for a quite easy to use way to handle the data and discard old data. Discarding old data is very important because time series data by can pile up pretty quickly and fill up your memory. To this end, influxdb offers what is called Retention policies. Leveraging an retention policy, essentially adds an expiration date to your data. Upon expiration the database will automatically take care of removing the data. In the following case, we create a database called "mydb" with a retention policy of 30 days. Consequentely, data that is older than 30 days, will be discarded.
More detailed instructions with different options can be found here.
First, we need to add the repositories to apt:
wget -qO- https://repos.influxdata.com/influxdb.key | gpg --dearmor > /etc/apt/trusted.gpg.d/influxdb.gpg
export DISTRIB_ID=$(lsb_release -si); export DISTRIB_CODENAME=$(lsb_release -sc)
echo "deb [signed-by=/etc/apt/trusted.gpg.d/influxdb.gpg] https://repos.influxdata.com/${DISTRIB_ID,,} ${DISTRIB_CODENAME} stable" > /etc/apt/sources.list.d/influxdb.list
Second, updating the packages and installation from influxDB:
sudo apt-get update && sudo apt-get install influxdb
Third, starting the influxDB service and configure it to launch at boot time:
sudo systemctl unmask influxdb.service
sudo systemctl start influxdb
sudo systemctl enable influxdb.service
First, enter the influxdb CLI in your shell:
influx
Second, we create the database with the desired retention policy:
CREATE DATABASE mydb WITH DURATION 30d
Let's verify that everything is set-up correctly:
SHOW RETENTION POLICIES ON mydb
which should show:
name duration shardGroupDuration replicaN default
---- -------- ------------------ -------- -------
autogen 720h0m0s 168h0m0s 1 trueIn order to visualize the air quality measurement data we use Grafana. Grafana is an open source analytics and interactive visualization.
Detailed instructions on how to install Grafana on a Raspberry Pi can be found here
First, we need add an authentication key to apt as well as adding the path to it:
wget -q -O - https://packages.grafana.com/gpg.key | sudo apt-key add -
echo "deb https://packages.grafana.com/oss/deb stable main" | sudo tee -a /etc/apt/sources.list.d/grafana.list
Second, we install Grafana. In case you followed the steps above, you don't need to update the apt packages again (so you can omit the first command), and directly install:
sudo apt-get update
sudo apt-get install -y grafana
Third, we configure the Grafana service to launch at boot time and start the service:
sudo /bin/systemctl enable grafana-server
sudo /bin/systemctl start grafana-server
Now, we opening the browser and enter http://localhost:3000, you should see the Grafana login page.
To visualize data we use Grafana. However, before we can visualize something we need to define data source. In our case, the datasource is the InfluxDB database. Simply add it, in our case with the default settings:
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| Grafana configured to have InfluxDB as datasource |
Once you have configured InfluxDB, you can query values from it and visualize it in any way. Just select InfluxDB as datasource and then select the data field you want to visualize, e.g., CO_2 level.
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| Grafana dashboard connected with InfluxDB to read the CO_2 values. |
In the directory './Grafana' you will find pre-configured dashboards that visualize the all sensor readings. Of course, these can be adapted easily and arbitrarily to change the style. To import them, simply click on import and select the JSON files one by one. Here we have three dashboards. One for temperature, one for CO_2 level and an overview dashboard.
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| Grafana dashboard import from JSON file. |
Next, you have to create a playlist. This is sort of an autopilot for visualizing the different dashboards. In this case, we have each dashboard display for 10 seconds before the next dashboard is displayed. In this case, we have created a new playlist called "1"
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| Grafana playlist creation |
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| Grafana playlist properties |
Once we have setup all the component we are ready to launch the visualization. The most simply way is to open several session, e.g. using tmux. One session for writing the sensor readings into the database. Another session for running the Grafana service.
In order to write the sensor scripts into InfluxDB, simply launch the Python script. If you have followed the instructions above, you can simply launch the script. Don't forget to launch the 'pigpio' and the 'clock stretching' as described above. Otherwise, the Python script will not be able to connect to the sensor.
python raspi_airdata.py
Depending on your geolocation, you might need to change the atmospheric pressure (default is: 944 mbar). There are numerous tutorials on the internet on how to convert altitude to atmospheric pressure, e.g., link.
python raspi_airdata.py --pressure <your geolocation pressure>
Alternatively, you might want to add a pressure sensor to your Raspberry Pi.
Last but not least, we want to start Grafana. You have to replace the , with the IP address your router or DNS-server has assigned to your Raspberry PI. Also you have to set the user name, e.g., admin and with the password. Then you are all set, and the Grafana dashboards should show up on your Raspberry Pi (or in your browser, if you connect to it via IP address):
sudo systemctl start grafana-server
sudo systemctl stop grafana-server
sudo systemctl restart grafana-server
DISPLAY=:0.0 /usr/bin/grafana-kiosk --playlists --URL http://<IP-Address>:3000/playlists/play/3 --ignore-certificate-errors --login-method local --kiosk-mode full --username <USER-NAME> --password <YOUR PASSWORD> --lxde --autofit
And this is it. I hope you enjoyed the tutorial.