Back in 2018, the Azure Stream Analytics team announced Anomaly detection for Azure Stream Analytics. And, it was also supported on the Azure IoT Edge. In November 2018, I was allowed to demonstrate this at the SPS IPC Drives in Nuremberg:
Since then, anomaly detection has become a first citizen of Azure Stream Analytics.
Azure Stream Analytics Anomaly Detection is able to ‘automatically’ detect spikes, dips, and trends in a stream of values. This is based on math and all you need to do is to specify how many values you expect and how sensitive the detection must be. It is in fact Machine Learning, in the end. So it is a prediction with a certain certainty…
And it’s just part of the Stream Analytics query language. So on the edge, we can deploy a Stream Analytics module (the engine of stream analytics). This is a fixed module from Microsoft.
All you have to do is feeding it a query, inputs, outputs and, user-defined functions if available:
We can define all this in the cloud, inside a stream analytics job and we can even test it. Those parts are then packed as a blob and put in blob storage. The Stream Analytics job can then download it and run it.
The inputs and outputs of the Stream Analytics job can be attached to the normal Azure IoT Edge routing mechanism.
But there were also smaller gems. On of them is “30 days to learn it“. You can complete the IoT Developer challenge and get 50 percent off the cost of a Microsoft Certification:
Implement and maintain the cloud and edge components of an IoT solution. Learn how to connect devices, move workloads to the edge, and build scalable, enterprise-grade, secured IoT solutions on Azure to increase performance, reduce costs, and optimize operations. It’ll only take about 15 hours to discover how to securely connect IoT devices to the cloud, build the intelligent edge, and develop solutions with Azure IoT Central.
There are more certificates to earn. See the site for all eight challenges!
All sessions are available now in the session catalog for you to watch. Below, I summerized all IoT related sessions.
Persisting incoming telemetry in a local database is useful for multiple purposes. One of them is creating a custom dashboard eg. written in Blazor.
In this blog, we explore how to deploy the popular InfluxDB. This open-source time-series database is specialized in Internet of Things usage. And on top of that, let’s explore how Grafana can be deployed on the edge too. Grafana is an open source analytics and interactive visualization web application.
We see how it can be connected to the Influx database:
As you see here, we need a custom Azure IoT Edge module (called ‘writer’) that is capable of writing incoming telemetry to the Influx database. There, the telemetry is picked up and displayed by Grafana.
If you have built an IoT Device yourself and are finally able to send telemetry to the cloud, you should be familiar with the scenario where you have to repeat the hard work of describing the messages all again on ingestion.
IoT Devices expose D2C telemetry and it can also support C2D communication. This interface is most of the time unique for that device. To be able to get insights from a device you have to be able to react to its interface.
Wouldn’t it be nice if a device was able to provide metadata about its interface once it connects to the cloud? This way, the incoming D2C telemetry could automatically result in e.g. a full user interface. And all C2D output could be represented by pre-configured input controls.
IoT Plug and Play enables solution builders to integrate smart devices with their solutions without any manual configuration. At the core of IoT Plug and Play, is a device model that a device uses to advertise its capabilities to an IoT Plug and Play-enabled application
Some of the original accelerators (it started with Azure IoT suites) like Remote Monitoring are now outdated or even archived. These are replaced by excellent Azure IoT Central apps which demonstrate the capabilities of the IoT platform for numerous markets and verticals:
There is still one original accelerator alive-and-kicking: the Connected Factory. This one demonstrates the use of OPC-UA protocols on the edge and in the cloud.
More than two years ago, I already wrote about this accelerator and the OPC publisher module, the backbone of this accelerator. Since then, a lot has changed. Some functionality is (temporarily) deprecated so I got a lot of questions based on the old blogs.
So it’s time to update it a little and see how the OPC Publisher is doing these days.
We are familiar with the Azure IoT Hub metrics which are offered. The Azure cloud tells us eg. how many messages are received or the number of devices that are connected.
If we look at Azure IoT Edge, you had to collect your own made metrics in the past.
Because IoT Edge modules are Docker containers and therefore sandboxed, you had to rely on the (third-party) logic to capture Host metrics. Regarding metrics about the edge agent and hub, these were not available.
With the most recent IoT Edge runtimes, agent, and hub, we have access to Edge metrics.
Both the Agent and Hub module expose the metrics over HTTP endpoints:
Within the Moby runtime, port 9600 is exposed on both individual modules. Outside the runtime, we have to assign individual host ports to prevent using the same host port.
Let’s see how this looks like and how we can harvest metrics in a custom container.
If you are looking for a way to manage and monitor your IoT devices outside the Azure Portal or are not able to build your own IoT platform, IoT Central is the place to be. And you can extend this portal with custom Azure resources using the export functionality.
All you need is to have browser access to Azure IoT Central. You can even run it for free for seven days to test it out. Also, the first two devices registered are free too.
Once you have worked with Azure IoT central, you have mastered it using the portal. If you want to scale up eg. the number of devices or users, automation of your tasks becomes necessary.