It only takes five simple steps to secure your secrets in Azure Functions

As seen in my last blog, we occasionally need to address secrets in Azure Function code. It’s too easy to just copy and paste for example connection strings or passwords in the source code but then you risk exposing your secrets to anyone who can access the code.

It takes only five steps to secure these secrets using an access policy. Let’s fix a common example using the Azure Key vault.

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Manipulate IoT Edge Module twin using an Azure Function

We use IoT Central a lot for demonstration purposes. It provides an IoT Dashboard for your IoT devices on a SaaS level. It brings speed into the projects and we can have good discussions about usability with customers.

Recently, I had to add some buttons in IoT Central to manipulate an IoT Edge device. At this moment, IoT Central is not supporting IoT Edge devices but it can be done with a simple trick. So displaying information is not that hard. But sending module twin changes back to the IoT edge is not simply done.

In this blog, I show how to program IoT Edge module twin updates using c#. I use Azure Functions to make this code reachable from other sources like IoT Central.

One Azure IoT accelerator to rule them all

The family of Azure IoT resources is very diverse. If you know what you are doing and have developers available you can have a great time with the many PaaS cloud resources.

If you have devices which need internet connectivity but you have no developers, you can check out IoT Central, the SaaS IoT solution.

Recently, Microsoft announced a very powerful integration with other leading IoT Platforms like SAP Leonardo and PTC Thingworx. Both can connect directly with the Azure IoT Hub, the cloud gateway. This opens a broad range of integration opportunities.

And last but not least, you can start with prebuild verticals, Azure IoT accelerators, formerly known as Azure IoT suites. If you have developers available but you do not want to start from scratch, check them out. You can deploy a typical accelerator in 15 minutes to see how they behave. And the smart thing is, all the code behind the logic is available for free on Github.

The most known accelerators are:

• Remote Monitoring (version two is based on microservices)
• Connected Factory (support OPC-UA protocol)
• Predictive Maintenance

But there are also third-party accelerators.

If you are a developer or architect, it’s time well spend checking them out!

Remote monitoring

The remote monitoring is a good starting point, it has a lot of out-of-the-box features:

In one of our current projects, we were looking for a rule engine. And while playing with the demo of the Remote Monitoring Accelerator, we stumbled on one.

The picture shown above is not really helping to explain how this rule engine works and you can try to read about it or check out the code on GitHub.

The features of this rules engine are both simple and powerful:

• Define rules for alarms or even actions as JSON files in blob storage
• Bind rules to groups of devices (defined as CSV file in blob storage)
• Rules can react to ‘instant’ messages using Javascript comparisons
• Rules can react to time windows aggregations using Javascript comparisons

And the best feature is that the rules engine is based on Azure Stream Analytics. Therefore it’s modular and it can be separated and reused completely in your own solution.

In this blog, we will see how it’s done.

Compare previous and current message in Stream Analytics

Last week I was testing the temporary storage in IoT Edge. I was interested in the stability so I wanted to know if messages were missing or maybe even coming in twice.

I have this heartbeat module which produces a counter. So I am able to generate messages which can be measured as a sequence.

One way is to check this using your eyes 🙂

But this can be seen as a more generic issue, comparing two messages after each other. So I was thinking about Azure Stream Analytics. This should be the perfect tool for this job.

Let’s check out how we can compare subsequent messages using Stream Analytics.

Custom IoT Hub assignment in Device Provisioning Service

In my previous blog, I have shown how to provision a device using a real TPM using the Device Provisioning Service (DPS).

Once you are able to provision your IoT devices to the Azure IoT Platform using a DPS, a whole new world of possibilities opens up for you.

Before, you registered your device to one IoTHub. To change it, you had to go to the device and fix it. But now you are able to make a choice between multiple IoT Hubs within the cloud, dynamically!

But what strategy are you going to use?

Microsoft provides three standard strategies out of the box:

1. Lowest latency (select the nearest IoT Hub)
2. Evenly weighted distribution (select the IoT Hub with the least amount of devices)
3. Static configuration (just select one yourself. This is the situation as before)

But there is a new strategy which is very flexible:

This fourth strategy makes use of a custom Azure Function which you can write yourself.

You could, for instance, access a database and read some data before you make the decision to which IoTHub you assign this device.

Let’s see how we can build a custom function ourselves and get the most out of it.

Prototype an IoT Dashboard with Adafruit IO

Last week I built this great demo with an Azure IoT Edge running on Industrial hardware, reading temperature, humidity, fan activity and led activity. But there was something missing…

I needed a simple dashboard to represent the values which were ingested by my Azure IoTHub and sent to an Azure Function.

Normally I build a basic website myself or I use tooling like PowerBI. It’s not that hard to get something sufficiently running for a demo.

But the last couple of weeks I was looking around for generic, off-the-shelf IoT Dashboards. And I had a couple of questions about their capabilities. What is on the market? What connectivity do they use? How many messages can I Ingest per time window? How do I configure the visual components? Etc.

I have reviewed a number of them and then I was checking out Adafruit IO.

This is what they see about themselves:

“Our simple client libraries work with the most popular devices such as the Adafruit Feather Huzzah, ESP8266, Raspberry Pi, Arduino, and more.”

I was triggered by the ‘more’ part. Does it also work with non-Adafruit devices? Because I know Adafruit from their DIY electronics shop, I was interested in what they are offering. And I was pleasantly surprised.

Let’s take a look at how we can integrate Adafruit IO in a generic demo with industrial hardware.

Azure IoT Client SDK now supports IoT device modules

Silently, Microsoft introduced modules in IoT Devices.

No, I’m not talking about IoT Edge modules, these are modules for IoT Devices which can connect to the IoT Hub directly.

Before, we used the device client to communicate with clients and the IoTHub. And we used the Device Twin to configure the device with desired properties.

This approach is still valid. But in addition, we can also separate the client logic in multiple modules. And each module can send messages and receive a Module Twin configuration.

Let’s see how this works.