Module 2: Install Azure IoT Edge on your Raspberry Pi

Index

Module 1: Create an Azure IoT Central application
Module 2: Set up your Raspberry Pi
Module 3: Set up your development environment
Module 4: Run the solution
Module 5: Dockerize the Air Quality Monitor solution
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Hardware requirements

Raspberry Pi 2 or better, SD Card, and Raspberry Pi power supply
Pimoroni Enviro+ pHAT
PMS5003 Particulate Matter Sensor with Cable available from Pimoroni and eBay.

Raspberry Pi 4 Tips and Tricks

This lab does not need a Raspberry Pi 4, but if you are using one then here are some tips and tricks I like to use for my Raspberry Pi.

Booting from high speed USB3 storage

As we will be building Docker images on the Raspberry Pi 4 so I would recommend a fast SD Card or a high speed USB3 Flash or SSD drive.

I use a Samsung USB 3.1 Flash Drive FIT Plus 128GB or USB3 SSD drive. See the 5 of the Fastest and Best USB 3.0 Flash Drives
For instruction on booting from USB3 see How to Boot Raspberry Pi 4 From a USB SSD or Flash Drive. Note, at the time of writing, set the FIRMWARE_RELEASE_STATUS to stable rather than beta.

Optionally overclocking a Raspberry Pi 4

Though not a requirement, the machine learning inference times will be improved by overclocking the Raspberry Pi 4. You will need a Raspberry Pi heat sink if you overclock. See the How to overclock Raspberry Pi 4 article for more information.

I use the following settings in the /boot/config.txt.

over_voltage=6
arm_freq=2000
gpu_freq=700

Raspberry Pi set up

Create the Raspberry Pi OS Image

I recommend using Raspberry Pi OS Lite as it takes fewer resources than the full Raspberry Pi Desktop version. If you’ve not set up a Raspberry Pi before then this is a great guide. “Setting up a Headless Pi”. Be sure to use the WiFi network as your development computer.

Start Raspberry Pi and update

Attach the Pimoroni Enviro+ pHAT and PMS5003 Particulate Matter sensor.
Insert SD Card or USB3 drive, and power on your Raspberry Pi.
Log into the Raspberry Pi over your network
```
 ssh pi@raspberrypi.local
```
or depending on your network settings try
```
 ssh pi@raspberrypi
```

Update and reboot

 sudo apt update && sudo apt install -y git python3-pip && sudo apt full-upgrade && sudo reboot

Install Docker on the Raspberry Pi

Log into your Raspberry Pi
```
 ssh pi@raspberrypi.local
```

From the SSH session run the following command.

 curl -sSL get.docker.com | sh && sudo usermod pi -aG docker && sudo reboot

Install the following Python Packages

From the SSH session you started in the previous step install the following required Python packages. Run the following command.

pip3 install ptvsd azure-iot-device psutil enviroplus RPi.GPIO pylint autopep8

Install the Pimoroni Enviro+ Python library

From the SSH session, run the following commands.

git clone https://github.com/pimoroni/enviroplus-python
cd enviroplus-python
sudo ./install.sh

You will also be prompted to install the samples. Select y as they will be useful for you to understand how to extend the solution.

Clone the Raspberry Pi Air Quality Monitor Solution

Log into your Raspberry Pi
```
 ssh pi@raspberrypi.local
```

From the SSH session, clone the solution repository to the Raspberry Pi

 git clone https://github.com/gloveboxes/Raspberry-Pi-Python-Environment-Monitor-with-the-Pimoroni-Enviro-Air-Quality-PMS5003-Sensor.git raspberry-pi-air-quality-monitor

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