Extending the AZ-220 Digital Twins hands-on lab with 3D visualization

Azure Digital Twins is advertised as a “platform as a service (PaaS) offering that enables the creation of twin graphs based on digital models of entire environments, which could be buildings, factories, farms, energy networks, railways, stadiums, and more—even entire cities”.

This sounds promising but it does not really ring a bell, does it?

Fortunately, besides the excellent documentation, Microsoft provides a great learning path in MS Learn as part of the AZ-220 Azure IoT developer exam preparations.

There, you will learn how Azure Digital Twins offers new opportunities for representing an Internet of Things solution via twin models, twin relations, and a runtime environment.

You finish the learning path with a hands-on lab where you build a model around a cheese factory and ingest sensor telemetry:

In the demo, the telemetry flows through the runtime and ends up in Time Series Insights.

Yes, the learning path is a good start and will prepare you for the exam or the assessment (you need to pass this assessment for a free one-year certification renewal).

On the other hand, many extra features could be added to turn this good start into a great start!

Think about propagating Azure Digital Twins events and twin property changes through the graph and visualizing live updates of twins in a 3D model, complete with alerts.

Let’s check out some of these additional features and see what you need to do to extend the ADT example.

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Getting started with Azure IoT Plug and Play

Microsoft is the founder of this concept called Azure IoT Plug and Play:

IoT Plug and Play enables solution builders to integrate smart devices with their solutions without any manual configuration.

The idea is that the device describes itself using some identification key. This key, the Model ID (or DTMI, Device Twin Model Identification), is bound to a complete model written in DTDL (Digital Twins Definition Language).

Using this model, the interface (or capabilities) of this device can be read:

• Properties (Azure IoT Device Twin desired properties and reported properties)
• Telemetry (the D2C messages)
• Command methods (based on Azure IoT Direct methods)

Once a device starts communicating using this deterministic interface, a User Interface can be provided dynamically.

This is the same principle of Plug and Play devices like a mouse or a webcam. If you plug it in, the device is identified as a mouse or webcam and the correct device driver is downloaded from the internet and installed. In the time before Plug&Play, each device came with a CD or floppy disk containing the driver. This was always a hassle, Plug&Play has taken away that pain.

The actual model is stored in a global Device Models Repository. You can create your own repository too.

Look at this Azure IoT Plug&Play architecture/flow:

Here are see the different steps needed to build a solution based on Azure IoT Plug&Play:

1. Devices exposing their Model ID
2. IoT Hub storing the Model ID as a reference in the IoT Hub registry
3. Consuming this Model ID by other Azure resources
4. Looking up the actual Model in a Device Models Repository
5. Building up a tree of device capabilities based on the device model

Once the device capabilities are known, an actual UI can be generated for this device so users can interact with it without any extra effort.

Let’s check out how this works.

Sending telemetry from IoT Edge to IoT Central

After writing my last post, I kept thinking about sending messages to the Azure IoT Central Bridge from Azure IoT Edge.

Eventually, I started building this IoT Edge module which passes incoming route messages to the IoT Central Bridge of your choice.

This module is now available in Docker Hub.

Use it in your IoT device with tag:

svelde/iot-edge-ic-bridge:1.1.0-amd64

Let’s check this module out.

When your clone is several commits behind the master

Once in a while, I clone a project just to check out the code, have it compiled or even to request a pull for an update.

That’s ok.

But what if my clone gets stale? What if it is several commits behind the original repository? You see a message like this:

How do you fix it? Is there a hidden button somewhere in the GitHub portal?