In this video, I'm going to show you how
to deploy Azure IoT Edge runtime onto a Linux
device, and in this demo I'll be using a
Raspberry Pi running raspbian. But first,
I'll illustrate to you what edge
computing is all about. In order to
understand what edge computing is all
about, we need to first understand the
role played by cloud computing in
industry. In a typical plant today you've
machines that are generating data and
sending it to the cloud via a gateway
that routes the data straight to a
storage service or to an analytic
software. The job of the analytic
software would be to convert the data
into something digestible by a real-time
dashboard or a machine learning
algorithm. In the case of machine
learning, the algorithm is trained using
the data and out of that comes a model,
a model that describes all things that
the machine has experienced in the past.
So, this is useful in the sense that you
can then use the model in real time
such that when the machine encounters an
undesired condition you can take action
before it causes damage by sending a
control signal to the machine at the
factory floor. So the idea of edge
computing is that, instead of sending all
the data upstream and have all this
activity happening at the cloud, why not
push all of these components to the edge
closer to the factory equipment so that
we can have these decisions made right
at the source of the data, in order to
reduce the cost of transferring data,
benefit from unlimited bandwidth, and be
able to employ these technologies in
mission-critical applications.
So to begin, I'll go to my Azure Portal
and create an IoT hub that will be used
for bi-directional communication between
the cloud and my edge device, so this
communication channel will be used to
download edge modules and perform some
basic configurations. Ok, and then when
the deployment of my IoT hub is complete,
I'll go to the IoT Hub to create an Edge
Device, an IoT edge device. Which is
essentially a reflection of the actual
device, otherwise known as the device
twin. Such that when we need to update
our edge device we update
this cloud version instead, and changes
will be automatically synced. So we copy
its connection string to be used for
configuring our raspberry pi. Okay, so
I've got my raspberry pi connected to
this monitor here, but I'll be using SSH
to remotely access its terminal on my PC
Now, since our Azure IoT Edge runtime and modules are run as Docker containers we
need to have Docker installed on our edge
device. So we'll begin by installing a docker
engine, called Moby, which is officially
supported with our Azure IoT Edge. When the
installation of the docker engine is
complete, we move on to the installation
of Azure IoT Edge runtime using the following
commands. And then when that is complete,
we move on to manually provision the
device. We do that by opening the Azure IoT Edge
config file, and updating the device
connection string with the one that we
copied earlier in our Azure Portal. And
then we save and the file. And then
after provisioning the device we
restart the Azure IoT Edge daemon. And then we can
check the status of the runtime, and here
we can see that our Azure IoT Edge runtime is
active and running. Okay, so the next
thing that I want to show you is how you
can deploy a module onto your as your
Azure IoT Edge runtime. And to do that I'm going
to use a prebuilt module that simulates
temperature and pressure. So, navigate to
your IoT hub, goto IoT edge. Go to the
device. Select Set Modules, and then click add
an IoT Edge Module. Enter the name and then we
need to provide the image URI for the
module container. So if you build your
images into a Docker container you then
supposed to provide the URI of that image
here. Next.
So this is our default route that sends
all messages to IoT Hub.
And then here you can see our
tempSensor module is now listed
as part of the modules on our IoT Edge
runtime. And then back here on our Raspberry
PI terminal, we can list the modules that
are currently running there and then you
see we've got our temperature sensor
also running. And then finally, we can
view the messages that are being
generated and sent from our temperature
sensor module. And then if we go back to
our IoT hub overview, we should see the
messages being sent from the device to
the cloud. Thanks for watching, if you've
got any thought at all on this video, I'd
love to hear about it in the comments
section below.
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