So a while back I posted about a little pet project I am working on along the lines of how hard can it be. To see the big picture, you can read the overview here. The first step is writing an Internet of Things (IoT) client app that runs on a device and sends sensor readings to the cloud. In the cloud, the Azure IoT Hub manages the client and also receives the data from the client.

PCB board

As the name implies, IoT devices connect to the internet. So not so different than your client app you might think at first. But when you start thinking about how IoT devices are deployed and run in the wild, there quite a few differences setting them apart. For one, the devices are usually not operated by a person. It's not a bring your own device (BYOD) scenario, the creator of the IoT device is generally in control of the software running on the device. Being in control also means that the device gets set up by the manufacturer and connects to the backend without or little human interaction. Since the device is out in the open and connected to the internet. It is generally a good idea if not mandatory to have a plan to update the device should a security breach such as Heartbleed ever occur. Though generally speaking the problems are often a lot more homegrown as this post from Troy Hunt nicely summarises. Another aspect that often arises with IoT solutions is the volume of data that has to be processed. While you can process data on the edge (on-site) - usually the desire is here to aggregate the data at a central point and act upon the live data or analyse the data in hindsight to find insights. It is often the data processing scenarios that bring the most significant business value and therefore are an essential part of the solution one tries to create with IoT solutions. Providing the developers with the challenge of creating a system that accomplishes to scale to meet the high data volumes running through the system. In short it is a different world than your traditional Xamarin, WPF, WinForms or Web client app.

The backend plays a significant role in IoT device scenarios. Therefore it comes at no surprise that you will find a lot of companies wanting to help you with your IoT endeavours. One of the solutions is the Azure IoT Hub which was created with the IoT challenges in mind. It provides many great features such as scalability to receive data from many million devices simultaneously, different messaging patterns to accommodate always connected vs IoT devices that may only have a connection now and then again. You can not only receive data from your devices with Azure IoT Hub but also send information to the device. It furthermore provides the means to inform your IoT devices that a new software/firmware update is available. And since it is running in the cloud, scalability is baked into the product.

For setting up the IoT Hub, I recommend you follow these instructions.

You can create one free IoT Hub instance per account, which is the one I will be using for this blog post.

With the IoT Hub setup, let's set up our local environment. You can manage your IoT Hub all from within PowerShell - oh yes feel that power of the shell (trying to improve my godfather jokes here...) all you will have to do is install the Azure CLI. Then install the IoT extension. If you did not have the tools already installed, be sure to first login to Azure on PowerShell with the following command:

az login

After being logged in and having the extensions you can do all sorts of stuff right from within PowerShell. For instance, we can create a new device like this:

az iot hub device-identity create --hub-name IoTEndpoint --device-id test-device-01

Note that the --hub-name must equal the name you gave your IoT Hub on Azure. You are free to choose a different name to register your device after --device-id. Once you have a device registered. You can send messages as the registered device:

az iot device send-d2c-message -n IoTEndpoint -d test-device-01 --data 'Hello IoTHub'

No error means success but, another handy command is seeing which messages the IoT Hub is receiving:

az iot hub monitor-events -n IoTEndpoint

If you now open a second PowerShell to send a message as a registered device, you will see IoT Hub receiving the message.

Showing received message

With all the tools installed and having a device registered, we are ready to implement our client. There are more commands which you can use with Azure CLI, you can find the full list of commands here.

Implementing an IoT Client

The Azure IoT Hub provides an SDK which clients can use to communicate. The SDK is available for .Net, Node.js, Python, C and Java. However can't or don't want to use the SDK you can manually connect to the IoT Hub over HTTP, MQTT or AMQP.

For my first endeavour, I used the Azure IoT Dev Kit. It is a fantastic value for money - perhaps you even have received one as a gift at an event you attended? The dev kit comes with many sensors, and an RGB LED, display, AUX, a USB- and WiFi-connection. That being said if you do not own a device, you can still get started with the SDK for .Net. The SDK runs on .Net Standard. So you can write a .Net Core client which using the NuGet package.

So we could implement a .Net Core Console app to implement our solution. Note since the library can be installed in a .Net Standard project you could extract all the IoT logic code into a .Net Standard library. Being lazy aka keeping the solution of this blog simple I will implement all of the code directly in the .Net Core project. The first thing we will want to do with our app is to connect to the backend. There are different ways how we can achieve this with the IoT hub. For getting started, we will take the easiest route, which is usually not what you want to deploy to production - i.e. using a connection string with an API key as an authentication mechanism:

var device = DeviceClient.CreateFromConnectionString("HostName=IoTEndpoint.azure-devices.net;DeviceId=test-device-01;SharedAccessKey=THIS-IS-WHERE-THE-SHARED-KEY-WOULD-BE-DISPLAYED");

You can retrieve this connection string from within the Azure Portal under your IoT Hub, IoT devices, in my case test-device-01 and then select either the Primary or Secondary Connection String.

Once connected, we can start to send sensor readings serialized to JSON:

var json = JsonConvert.SerializeObject(measurement);
var message = new Message(Encoding.UTF8.GetBytes(json));

await device.SendEventAsync(message);

Since .Net Standard runs on nearly every OS you can think of, you could extract the code above to run on a Raspberry Pie or Android or INSERT-YOUR-TARGET-HERE using actual sensors. And since the SDK is also available for other languages such as C version, we can use it for writing apps for the Azure Dev Kit.

Can I have the rest of the client code? Yes - if you stick around until the end you will find a link to the full source code of the client on GitHub.

Azure IoT Dev Kit Client

I have to recommend following the official docs by Microsoft on how to set up the Azure IoT Dev Kit with Visual Studio Code, which will show you how to initially set up the device and connect it to the IoT Hub. Word of advice make sure you follow all the steps of the documentation and do not leave out any parts such as installing the USB driver on your Windows machine. Talking out of experience from a friend here of course

Microsoft offers quite a few samples for the Dev Kit. The samples can be loaded directly onto the device using Visual Studio Code. I took some inspiration for the .Net Core Client above from the remote monitoring tutorial - I skipped the Azure stuff since I will want to process the data differently. Only changes I made to the C code was the changing the unit of the atmospheric pressure (atm) to hPa. Oh and of course removing the conversion from Celsius to Fahrenheit- my brain will not compute the imperial system

Note that a lot of IoT devices do not come with an operating system installed due to the restricted resources available on the device. These restrictions are often the reason why many programs are written in C. If you are a .Net developer like myself, you will probably miss a lot of conveniences. Then again you might find some consolidation in the knowledge that your code will be very efficient

Another device that I am looking forward to checking out soon is the Meadow by Wilderness Labs. The great thing about the Meadow is that you can write your IoT client code with C# for the device. Good news is they are open for pre-orders, but you might have to wait in line until all of the Kickstarter campaign supporters have received theirs.

Once you have installed the app on the dev kit and connected it to WiFi, you will be able to see the messages arrive on the IoT Hub dashboard.

Graph showing the messages being received by the Azure IoT Hub.

And by using the CLI tools command from before we can start a listener that will receive every message sent to the Azure IoT Hub:


You will be able to see the raw JSON coming in on the IoT hub.


Setting up the first IoT device can be a bit of a daunting task since there are a few moving parts. First getting to know the client or even just knowing how to implement a client in .Net Core, then the Azure IoT Hub part. For me, it was good to notice that it is okay to start small and gradually learn the possibilities provided by Azure IoT Hub. But there is still so much more packed into the Azure IoT Hub. For instance updating a device, device twins or communicating from the cloud to a device - if you are interested in a detailed list, be sure to check out the official page here.

In the next blog post of the series, we will look at how to process the data in the cloud. And how to forward it to clients. Where we will visualise the data for the user. So stay tuned and check out all of the client code on GitHub.



Black Metal Padlock

Update: So after posting this my colleague and friend Danielapproached me and showed me the Azure Artifacts Credentials Provider by Microsoft which automates the steps bellow. Be sure to check it out. Thanks, Daniel for showing me this and making my life easier 😃

So lately I was playing around with one of Azure DevOps many features. Namely pushing freshly created NuGet packages to your private feed. Bringing up the question how can I access the feed and authenticate during a NuGet restore process via dotnet restore?

While this blog post shows steps to be taken for Azure DevOps - the same actions are required in the NuGet.config for other sources.

While I knew how to click my way around Visual Studio to do this. Under my Ubuntu Shell, this was not an option. Luckily adding a NuGet feed is quite common knowledge, while the paths differ under Windows and Unix systems you will find it in your home directory under:


Or for Windows that would be:


You can add the feed to your NuGet.config file:

<?xml version="1.0" encoding="utf-8"?>
        <add key="nuget.org" value="https://api.nuget.org/v3/index.json" protocolVersion="3" />
        <add key="NameOfYourFeed" value="path to your nuget/index.json" />

Now for accessing a private NuGet feed, you will have to provide a username and password. You can add them to the config file:

<?xml version="1.0" encoding="utf-8"?>
    <!-- packageSources -->
            <add key="Username" value="gnabber"/>
            <add key="ClearTextPassword" value="YourPassword"/>

The only issue being you probably do not want to store your Azure DevOps password in plain text on a computer. And you shouldn't do that either. So let's head back over to Azure DevOps, click on your profile picture and select "Security". Now generate a new token. Be sure to select "Show all scopes" then under Packaging choose the "Read" permissions.

Image showing the dialog to generate a token for readonly access to your package feed

Copy the generated token and store it in the NuGet.config within the PlainTextPassword field. You can now dotnet restore your packages from the private Package feed.




Whether writing a mobile app with Xamarin Forms or native Xamarin.iOS/Xamarin.Android sometimes the requirements demand that your app updates as quickly as possible when something changes on the server. This might be a chat application, stock ticker or any monitoring app showing live data to a user of a process currently running in the backend.

Let's say we wanted to create a simple chat app. If we would go down the path of a traditional Create Read Update Delete (CRUD) app over HTTP, we might choose to poll the server every few seconds or so to read the latest value. While this approach delivers the results, it comes with some drawbacks. To name a few: making requests even though nothing has changed, climbing the leader board on the battery usage category plus your server gets hammered with requests only to tell them: "Sorry no news yet..." - so if not poll lets push. And this is where WebSockets come in.

The only problem with WebSockets is that the implementation in .Net is close to the metal. This results in an additional implementation effort having to be done by the developers. Luckily for .Net developers, there is SignalR which comes with all the boilerplate code you want around WebSockets. A web developer will also tell you about fall back options backed into SingalR. As a Xamarin developer, you will most probably never use those features. But the chances are good that you will be delighted by the ease of handling connections, channels or writing to specific connected clients.

SignalR is around for quite a while; it has been ported over to .Net Core and is .Net Standard compatible. This is excellent news since we can add the SignalR client directly into our Xamarin Forms app - no platform-specific/wrapper code required. But before we can start implementing our client, we will first have to create a SignalR enabled backend.

If you are new to SignalR, be aware that there is quite a big difference between SignalR and SignalR Core. If you are writing a new app today using ASP.NET Core or Azure Functions. You will want to use SignalR Core or else you will go on a nasty error hunt ending with your palm slapping against your forehead ‍🤦‍♂️

The Server

When implementing the backend, we can choose between two options. Either we implement SignalR using ASP.NET Core, or we decide to go with Azures SignalR Core Service. The later can be integrated into ASP.NET Core or an Azure Function app. The Azure option also comes with scaling capabilities - in other words, you get up to 1 Million simultaneous connections using SignalR out of the box.

For more detail on how to set up the Azure Functions and SignalR combo, you will find instructions in the official documentation.

For our simple chat application, we will want a way for our clients to send and receive messages. To achieve this, we will have to create a SignalR Hub that provides a method for sending messages:

public static Task SendMessage(
    [HttpTrigger(AuthorizationLevel.Anonymous, "post")]string message,
    [SignalR(HubName = "SignalRDemo")]IAsyncCollector<SignalRMessage> signalRMessages)
    return signalRMessages.AddAsync(
        new SignalRMessage
            Target = "NewMessage",
            Arguments = new[] { message }

On invocation, the method will send the message to all connected clients. Which brings us to our next point. Every chat participant will first have to connect to the hub so that messages can be received. So let's implement that registration method:

public static SignalRConnectionInfo Negotiate(
[HttpTrigger(AuthorizationLevel.Anonymous)]HttpRequest req,
[SignalRConnectionInfo(HubName = "SignalRDemo")] SignalRConnectionInfo connectionInfo)
    // connectionInfo contains an access key token with a name identifier claim set to the authenticated user
    return connectionInfo;

The naming of the method is a convention from SignalR. In other words, you must name the method Negotiate, or your code will not work. No, I do not want to elaborate on how I found this one out the hard way 😉

With the function and SignalR Service in place, we can now turn our focus to the client.

The mobile client

On the mobile client, we want to be able to receive messages and type responses to the group. Our simple app will have to live with the limitation of only receiving messages while being connected. At least for the moment. But here is the chat running in all of its glory.


Now let's have a look at the ChatService which connects us to the backend and receives messages:

public async Task Connect()
    if (_connection.State == HubConnectionState.Connected) return;

    _connection.On<string>("NewMessage", (messageString) =>
        var message = JsonConvert.DeserializeObject<Message>(messageString);

    await _connection.StartAsync();

Note that we register the receiver method before we connect to the backend. This way, we start receiving updates as soon as being connected to the SignalR Service. Now when implementing a receiver method, you must ensure that the type signature matches the method we defined earlier on the server. If the types or the name do not match, you will never receive any messages.

Since reading is only half the fun, let's implement the send message:

public async Task Send(Message message)
    var serializedPayload = JsonConvert.SerializeObject(message);

    var response = await _httpClient.PostAsync("https://gnabbersignalr.azurewebsites.net/api/SendMessage", new StringContent(serializedPayload));
    Debug.WriteLine(await response.Content.ReadAsStringAsync());

And if you ever had enough from the stream of messages but want to give your eyes the joy of staring at a bare app here is the disconnect method:

public async Task Disconnect()
    await _connection.DisposeAsync();

    _connection = new HubConnectionBuilder()

I hope you could see that using SignalR it is a breeze to implement a bidirectional communication layer to your server. Which will allow your (mobile) clients to send and receive data in near real-time. Another side effect of using SignalR is that you could easily extend the app with a web client. Since your favourite JavaScript framework will allow you to use the SignalR client. If you are ready to get started with SignalR, be sure to check out the docs.

You can find the entire sample, including all the UI code on GitHub.

This blog is part of the October Xamarin Challenge. So be sure to check out the other posts for more best practices when writing Xamarin apps.


  •   Posted in: 
  • F#

A picture containing colour crayons

I recently was faced with the task to render RAL Colours on an app that I was developing. RAL Colours are used mainly in industrial colour appliances - i.e. powder coating. So while well known in the powder coating industry it was quite an exciting read on Wikipedia to find out how RAL colours came to life. The good thing is that there is a finite set of classic RAL colours. Even better there is a table on Wikipedia which contains a good enough approximation for the classic RAL colours.

So instead of copy & pasting the table into an editor and slashing away at the data. I was wondering if there would be a better way to extract the information from the website. And store the data in a more handy form such as a JSON file.

In the last couple of months, I have been dabbling with F# in my free time. Data providers are a powerful tool which is available in the F# language. In short, you can point a data provider at a data source, and during compilation, the types used in that source get generated for you. So for your typical JSON response from a website. You can use the JSON type provider to create a type based on that stream which you then can use throughout your F# program. Now, this is a more than your "create a C# POCO from JSON" Visual Studio feature. You also get methods to slice and dice through your data. In other words, it is an excellent tool for exploring new data sources or just parsing new data sources and processing that data.

As with LINQ extensions it is is possible to write your type providers. But for most general use cases the type provider already exists and can be added to your project as a NuGet package. The NuGet package we will be using is the FSharp.Data which provides type providers for JSON, XML, CSV and HTML (plus the World Bank ‍🤷‍♂️).

Using an F# script, we will first have to reference the type provider:

#I "./packages"
#r "FSharp.Data/lib/netstandard2.0/FSharp.Data.dll"

open FSharp.Data

Side note: I am using paket for my dependency management because it installs the package right into the project folder. You do not need to use paket, but you will have to make sure that the #r ... line points to the dll.

Now type providers create a type based on a data source. In our case I can point it at the Wikipedia website listing all the RAL colours:

type wikipedia = HtmlProvider<"https://en.wikipedia.org/wiki/List_of_RAL_colors">

We could have also provided a local file:

type wikipedia = HtmlProvider<"list_of_ral_colors.html">

The local file is excellent if you do not always want to hit the remote site. But you run the risk of having an older version locally than on the server which can lead to ugly problems. As far as we are concerned for the script. I will point it at Wikipedia and be sure to make my again annual donation at the end of year

In the JSON file, we will want to store the RAL, RGB and colour name. So let's create a record type for that quickly:

type ralColor = { ral: string; hex: string; name: string}

Now that we have our types, we are all set to extract that data. By looking at the website, we can see in which section the table is located:

Picture showing part of the Wikipedia RalColor List

Knowing this location, we can scan the site and hone in on the data we are looking for:

let ralColorSection = wikipedia.Load("https://en.wikipedia.org/wiki/List_of_RAL_colors")
                                .``All RAL Colours in a single listing``

If you have never used type providers, you will be probably reading the lines above and go: "Okay... I guess..." - so let's just quickly look at what happened underneath those lines of code. As the name suggests, type providers provide a type based on a data source. This is where we nod. So what do we get with the lines above? We get a type which represents the table of RAL colours. We can access all of the rows via ralColorSection.Rows. When iterating over each row we can read the value in a column by using its name. So we could print out all colour names as follows:

ralColorSection.Rows |> Seq.iter (fun r -> printfn "%s" r.``Colour name``)

I know this is freaking cool right! So if we wanted to extract the RAL, RGB and name from the table we could use our previously defined type and the values as follows:

let ralColors = ralColorSection.Rows
                    |> Seq.map((fun r -> 
                        {ral = r.``RAL Number``; 
                            hex = r.``HEX Triplet``; 
                            name = r.``Colour name``}))

Note the two ticks are how F# variables with spaces in them can be accessed. Now we have all the data we wanted. So now all that is left to do is the boring bit of storing it into a JSON file:

#I "./packages"
#r "Newtonsoft.Json/lib/netstandard2.0/Newtonsoft.Json.dll"

// ...

open Newtonsoft.Json

// ...

let writeToJsonFile ralColors =
    let filePath = Path.Combine(__SOURCE_DIRECTORY__, "ral_colour_map.json")
    let jsonString = JsonConvert.SerializeObject(ralColors)
    File.WriteAllText(filePath, jsonString, Encoding.UTF8) |> ignore

And that wraps up this blog post. I hope you have seen that F# 's type providers can be a great way to scan through data sources and extract the information you need. One thing to be aware of when using type providers: You can't directly share the generated type with other .Net languages such as C#. You would have to wrap the data in a record type - by the way: the type we created to hold the subset of data is a record type. So while there might be some additional effort up ahead when writing fully-fledged enterprise applications, they are a no brainer for scripting. And will provide you with a significant productivity boost when exploring new datasets.

Be sure to check out the official documentation on the HTML provider used in this post. As always you can find the entire code on GitHub.



Ever since I have received my Azure IoT Devkit, I wanted to create a small app with it. The app would let me play around with streaming data from the device and view it on my phone. Also sending data to the device and many other exciting aspects of creating an IoT application. So what better way than to create a system that would stream the sensor data from my devkit to my mobile phone. Once that is implemented, how about creating an alerting system. Should a value increase over a certain threshold, the system will notify me. Many exciting ideas and paired with the capabilities of Azure all in my grasp. So before implementing, let me show you my high-level system design:

System Overview showing the IoT Kit, the different Azure services and the Xamarin mobile app.

However, first we will need to create an app on the device or emulate a device that sends up a stream of data to the Azure cloud over the IoT Hub. Then we will probably want to stream the data to the mobile client using SignalR. And finally visualise all of the data using some nice Xamarin Forms code - perhaps even a fancy charting library.

Once the data is getting streamed to the client let's have a look at alarming scenarios. What if the value e.g. the humidity rises quickly over a short timeframe. We should be able to detect such an event using Stream Analytics and then send a push notification to a client.

SignalR vs IoT Hub

Before diving into the implementation I showed this overview to a friend of mine - to make sure I am not overdoing it with the Azure-candy store. And promptly I got the question: "Hey Mark, so what you are basically doing is a Publish and Subscribe system - why do you need SignalR and IoT Hub. Wouldn't IoT Hub or SignalR offer all you need to send messages at scale to clients?". What a great question - and simplification of the problem. While it is right that what we are doing is nothing else then publishing data and subscribing to it. They are two worlds we are combining here. On the one side we have the IoT world. Here we the creator/company/enterprise produce the software and often also distribute the hardware. So we own the device and software. On the other hand when we release an app we often do not own the device on which the app is running on. So while we generally tend to trust our IoT devices because we create them, harden them and equip them with certificates for communication with our backend - in our case Azure IoT Hub. We generally do not have the same trust in mobile apps. Mobile apps are often deployed to the mobile store and therefore have no guarantee that the app does not end up on a rooted Android or Jailbreaked iOS phone. Once on such a device is often just a cat and mouse game to make it as difficult as possible for an attacker to access the secrets that got installed with the app. In short IoT apps and mobile apps come from a different starting position and therefore we tend to use different approaches for implementing security. And that is why we use two pub/sub systems, because both of them were designed with the two different aspects in mind.

In the next blog post we will get started with writing the IoT client and also see how we can write an IoT client without requiring a devkit.