Quickly generating a dataset of fictitious Users using Randomised Real Data and PowerShell

Introduction

I’ve lost count of the number of times I’ve had the need to generate a representative dataset of users. Of course I have access to many production datasets but for many reasons they can’t be used. Finding previous datasets I’ve randomly generated always seems to take longer than it should, so with my most recent iteration of having to generate a fictitious list of users with Australian addresses, I’ve documented how I went about it, along with the source data I used and the script to create it.

Source Data

For my data sources to base my dataset off, I wanted representative data for Australia for both people names and locations. After a few quick searches I found;

  • that Data South Australia has lists of baby names for both male and female babies in SA. I downloaded the 2017 lists as CSV’s.
  • for Surname, also from Data South Australia I borrowed the 19th Century Arrivals list and manipulated the Fullname column to separate it on “,” then used the Excel Function to remove duplicates. I deleted all other columns so that I was left with just over 13,000 surnames in a CSV file.
  • Matthew Proctor’s list of Australian Postcodes as a CSV. This provides Postcode, Suburb and State.
  • Brisbane City Council (Australia’s largest Council) has a dataset with all bus locations that includes Street names as a CSV. Like I did for Surname I used the Excel Function to remove duplicates, removed the blanks and the other columns and then had just over 1600 street names.

The Script

The script is pretty simple. It imports each of the CSV’s listed above and generates a random number based on the number of records in each file.

The GitHub Repo contains the PowerShell script along with the source files. Change line 3 for the location where you store the CSV files and change line 66 for the number of users to generate. I’ve left the end of the script empty. I either insert the API call to create the users, or the PowerShell cmdlet with the data to do the creation depending on where I’m creating the users.

darrenjrobinson/Generate-Random-Users

Generate-Random-Users – Using real data, randomise it to create realistic users with Australian addresses

The Output

Here is a sample output in JSON format.

{
"Street": "370 Miskin St",
"Surname": "Burne",
"Suburb": "WOODBROOK",
"Postcode": "3451",
"State": "VIC",
"GivenName": "Miro"
}
{
"Street": "293 Preston Rd",
"Surname": "Partingale",
"Suburb": "MARRARA",
"Postcode": "812",
"State": "NT",
"GivenName": "Daniella"
}
{
"Street": "409 Orchard St",
"Surname": "Liaseyer",
"Suburb": "THURGOONA",
"Postcode": "2640",
"State": "NSW",
"GivenName": "Ariana"
}
{
"Street": "775 Station Rd",
"Surname": "Nevin",
"Suburb": "AVON DOWNS",
"Postcode": "862",
"State": "NT",
"GivenName": "Naria"
}

Summary

Using data publicly available and PowerShell it is possible to quickly generate a dataset of representative users and addresses. Generating other attributes is as easy as extrapolating from the existing data or supplementing it with additional source data files.

A Voice Assistant for Microsoft Identity Manager

This is the third and final post in my series around using your voice to query/search Microsoft Identity Manager or as I’m now calling it, the Voice Assistant for Microsoft Identity Manager.

The two previous posts in this series detail some of my steps and processes in developing and fleshing out this concept. The first post detailed the majority of the base functionality whilst the second post detailed the auditing and reporting aspects into Table Storage and Power BI.

My final architecture is depicted below.

Identity Manager integration with Cognitive Services and IoT Hub 4x3

I’ve put together more of an overview in a presentation format embedded here.

GitPitch Presents: github/darrenjrobinson/MIM-VoiceAssistant/presentation

The Markdown Presentation Service on Git.

If you’re interested in building the solution checkout the Github Repo here which includes the Respeaker Python Script, Azure Function etc.

Let me know how you go @darrenjrobinson

Using your Voice to Search Microsoft Identity Manager – Part 2

Introduction

Last month I wrote this post that detailed using your voice to search/query Microsoft Identity Manager. That post demonstrated a working solution (GitHub repository coming next month) but was still incomplete if it was to be used in production within an Enterprise. I hinted then that there were additional enhancements I was looking to make. One is an Auditing/Reporting aspect and that is what I cover in this post.

Overview

The one element of the solution that has visibility of each search scenario is the IoT Device. As a potential future enhancement this could also be a Bot. For each request I wanted to log/audit;

  • Device the query was initiated from (it is possible to have many IoT devices; physical or bot leveraging this function)
  • The query
  • The response
  • Date and Time of the event
  • User the query targeted

To achieve this my solution is to;

  • On my IoT Device the query, target user and date/time is held during the query event
  • At the completion of the query the response along with the earlier information is sent to the IoT Hub using the IoT Hub REST API
  • The event is consumed from the IoT Hub by an Azure Event Hub
  • The message containing the information is processed by Stream Analytics and put into Azure Table Storage and Power BI.

Azure Table Storage provides the logging/auditing trail of what requests have been made and the responses.  Power BI provides the reporting aspect. These two services provide visibility into what requests have been made, against who, when etc. The graphic below shows this in the bottom portion of the image.

Auditing Reporting Searching MIM with Speech.png

Sending IoT Device Events to IoT Hub

I covered this piece in a previous post here in PowerShell. I converted it from PowerShell to Python to run on my device. In PowerShell though for initial end-to-end testing when developing the solution the body of the message being sent and sending it looks like this;

[string]$datetime = get-date
$datetime = $datetime.Replace("/","-")
$body = @{
 deviceId = $deviceID
 messageId = $datetime
 messageString = "$($deviceID)-to-Cloud-$($datetime)"
 MIMQuery = "Does the user Jerry Seinfeld have an Active Directory Account"
 MIMResponse = "Yes. Their LoginID is jerry.seinfeld"
 User = "Jerry Seinfeld"
}

$body = $body | ConvertTo-Json
Invoke-RestMethod -Uri $iotHubRestURI -Headers $Headers -Method Post -Body $body

Event Hub and IoT Hub Configuration

First I created an Event Hub. Then on my IoT Hub I added an Event Subscription and pointed it to my Event Hub.

IoTHub Event Hub.PNG

Streaming Analytics

I then created a Stream Analytics Job. I configured two Inputs. One each from my IoT Hub and from my Event Hub.

Stream Analytics Inputs.PNG

I then created two Outputs. One for Table Storage for which I used an existing Storage Group for my solution, and the other for Power BI using an existing Workspace but creating a new Dataset. For the Table storage I specified deviceId for Partition key and messageId for Row key.

Stream Analytics Outputs.PNG

Finally as I’m keeping all the data simple in what I’m sending, my query is basically copying from the Inputs to the Outputs. One is to get the events to Table Storage and the other to get it to Power BI. Therefore the query looks like this.

Stream Analytics Query.PNG

Events in Table Storage

After sending through some events I could see rows being added to Table Storage. When I added an additional column to the data the schema-less Table Storage obliged and dynamically added another column to the table.

Table Storage.PNG

A full record looks like this.

Full Record.PNG

Events in Power BI

Just like in Table Storage, in Power BI I could see the dataset and the table with the event data. I could create a report with some nice visuals just as you would with any other dataset. When I added an additional field to the event being sent from the IoT Device it magically showed up in the Power BI Dataset Table.

PowerBI.PNG

Summary

Using the Azure IoT Hub REST API I can easily send information from my IoT Device and then have it processed through Stream Analytics into Table Storage and Power BI. Instant auditing and reporting functionality.

Let me know what you think on twitter @darrenjrobinson

Using your Voice to Search Microsoft Identity Manager – Part 1

Introduction

Yes, you’ve read the title correctly. Speaking to Microsoft Identity Manager. The concept behind this was born off the back of some other work I was doing with Microsoft Cognitive Services. I figured it shouldn’t be that difficult if I just break down the concept into individual elements of functionality and put together a proof of concept to validate the idea. That’s what I did and this is the first post of the solution as an overview.

Here’s a quick demo.

 

Overview

The diagram below details the basis of the solution. There are a few extra elements I’m still working on that I’ll cover in a future post if there is any interest in this.

Searching MIM with Speech Overview

The solution works like this;

  1. You speak to a microphone connected to a single board computer with the query for Microsoft Identity Manager
  2. The spoken phrase is converted to text using Cognitive Speech to Text (Bing Speech API)
  3. The text phrase is;
    1. sent to Cognitive Services Language Understanding Intelligent Service (LUIS) to identify the target of the query (firstname lastname) and the query entity (e.g. Mailbox)
    2. Microsoft Identity Manager is queried via API Management and the Lithnet REST API for the MIM Service
  4. The result is returned to the single board computer as a text result phase which it then uses Cognitive Services Text to Speech to convert the response to audio
  5. The result is spoken back

Key Functional Elements

  • The microphone array I’m using is a ReSpeaker Core v1 with a ReSpeaker Mic Array
  • All credentials are stored in an Azure Key Vault
  • An Azure Function App (PowerShell) interfaces with the majority of the Cognitive Services being used
  • Azure API Management is used to front end the Lithnet MIM Webservice
  • The Lithnet REST API for the MIM Service provides easy integration with the MIM Service

 

Summary

Leveraging a lot of Serverless (PaaS) Services, a bunch of scripting (Python on the ReSpeaker and PowerShell in the Azure Function) and the Lithnet REST API it was pretty simple to integrate the ReSpeaker with Microsoft Identity Manager. An alternative to MIM could be any other service you have an API interface into. MIM is obviously a great choice as it can aggregate from many other applications/services.

Why a female voice? From a small response it was the popular majority.

Let me know what you think on twitter @darrenjrobinson

Sending Events from IoT Devices to Azure IoT Hub using HTTPS and REST

Overview

Different IoT Devices have different capabilities. Whether it is a Micro-controller or Single Board Computer your options will vary. In this post I detailed using MQTT to send messages from an IoT Device to an Azure IoT Hub as well as using the AzureIoT PowerShell Module.

For a current project I needed to send the events from an IoT Device that runs Linux and had Python support. The Azure IoT Hub includes an HTTPS REST endpoint. For this particular application using the HTTPS REST endpoint is going to be much easier than compiling the Azure SDK for the particular flavour of Linux running on my device.

Python isn’t my language of choice so first I got it working in PowerShell then converted it to Python. I detail both scripts here as a guide for anyone else trying to do something similar but also for myself as I know I’m going to need these snippets in the future.

Prerequisites

You’ll need to have configured an;

Follow this post to get started.

PowerShell Device to Cloud Events using HTTPS and REST Script

Here is the PowerShell version of the script. Update Line 3 for your DeviceID, Line 5 for your IoT Hub Name and LIne 11 for your SAS Token.

Using Device Explorer to Monitor the Device on the associated IoT Hub I can see that the message is received.

Device Explorer

 

Python Device to Cloud Events using HTTPS and REST Script

Here is my Python version of the same script. Again update Line 5 for your IoT DeviceID, Line 7 for your IoT Hub and Line 12 for the SAS Token.

And in Device Explorer we can see the message is received.

Device Explorer Python

Summary

When you have a device that has the ability to run Python you can use the IoT Hub HTTPS REST API to send messages from the Client to Cloud negating the need to build and compile the Azure IoT SDK to generate client libraries.

Utilising Azure Speech to Text Cognitive Services with PowerShell

Introduction

Yesterday I posted about using Azure Cognitive Services to convert text to speech. I also eluded that I’ve been leveraging Cognitive Services to do the conversion from Speech to Text. I detail that in this post.

Just as with the Text to Speech we will need an API key to use Cognitive Services. You can get one from Azure Cognitive Services here.

Source Audio File

I created an audio file in Audacity  for testing purposes. In my real application it is direct spoken text, but that’s a topic for another time.  I set the project rate to 16000hz for the conversion source file then exported the file as a .wav file.

Capture Audio

The Script

The Script below needs to be updated for your input file (line 2) and your API Key (line 7). Run it liine by line in VSCode or PowerShell ISE.

Summary

That’s it. Pretty simple once you have a reference script to work with. Enjoy.

Converted

 

 

Utilising Azure Text to Speech Cognitive Services with PowerShell

Introduction

Recently I’ve been building an IoT Project that leverages Azure Cognitive Services. A couple of the services I needed to use were for converting Text to Speech and Speech to Text. The guides were pretty good from Microsoft, but not obvious for use with native PowerShell. I’ve got it all working, so am documenting it for myself for the future but also to help anyone else trying to work it out.

Accessing the Cognitive Services Text to Speech API

Azure Cognitive Services Text to Speech is a great service that provides the ability as the name suggests, convert text to speech.

First you’ll need to get an API key. Head to the Cognitive Services Getting Started page and select Try Text to Speech and Get API Key. It will give you a trial key and 5000 transactions limited to 20 per minute. If you want to use it longer, provision a Speech to Text service using the Azure Portal.

The Script

I’m using a female voice in English for my output format. All the available output languages and genders are available here.

There are also 8 audio output formats. The two I’ve used most are raw 16khz pcm for .wav format and 16khz mp3 for MP3 output as highlighted below. The script further below is configured for MP3.

  • ssml-16khz-16bit-mono-tts
  • raw-16khz-16bit-mono-pcm
  • audio-16khz-16kbps-mono-siren
  • riff-16khz-16kbps-mono-siren
  • riff-16khz-16bit-mono-pcm
  • audio-16khz-128kbitrate-mono-mp3
  • audio-16khz-64kbitrate-mono-mp3
  • audio-16khz-32kbitrate-mono-mp3

The script below is pretty self-explanatory. Update Line 5 for your API Key, and Lines 11 and 13 if you want the output audio file to go to a different directory or filename.  The text to be converted is in line 59.

Step through it using VSCode or PowerShell ISE.

Summary

Using Azure Cognitive Services you can quickly convert text to audio. Enjoy.

 

Getting started with the Lithnet REST API for the Microsoft Identity Manager Service

Introduction

A common theme with my posts on Microsoft Identity is the extensibility of it particularly with the Lithnet tools that Ryan has released.

One such tool that I’ve used but never written about is the Lithnet REST API for the Microsoft Identity Manger Service. For a small proof of concept I’m working on I was again using this REST API and I needed to update it as Ryan has recently added some new functionality. I realised I hadn’t set it up in a while and while Ryan’s documentation is very good it was written some time ago when IIS Manager looked a little different. So here is a couple of screenshots and a little extra info to get you started if you haven’t used it before to supplement Ryan’s documentation located here.

Configuring the Lithnet REST API for the Microsoft Identity Manager Service

You can download the Lithnet REST API for the FIM/MIM Service from here

If you are using the latest version of the Lithnet Rest API you will need to make sure you have .NET 4.6.1 installed. If you are running Windows Server 2012 R2 you can get it from here.

When configuring your WebSite make sure you choose .NET v4.5 Classic for the Application Pool.

WebSite AppPool Settings.PNG

The web.config must match your MIM version. Currently the latest is 4.4.1749.0 as detailed here. That therefore looks like this.

WebConfig Resource Management Version.PNG

Finally you’ll need an SSL Certificate. For development environments a Self-Signed Certificate is fine. Personally I use this Cert Generator. Make sure you put the certificate in the cert store on the machine you will be testing access with. Here’s an example of my command line for generating a cert.

Cert Generation.PNG

You could also use Lets Encrypt.

In your bindings in IIS have the Host Name match your certificate.

Bindings.PNG

If you’ve done everything right you will be able to hit the v2 endpoint help. By default with Basic Auth enabled you’ll be prompted for a username and password.

v2 EndPoint.PNG

Using PowerShell to query MIM via the Lithnet Rest API

Here is an example script to query MIM via the Lithnet MIM Rest API. Update for your credentials (Lines 2 and 3), the URL of the server running the API Endpoint (Line 11) and what you are querying for (Line 14). My script takes into account Self Signed Certs in a Development environment.

Example output from a query is shown below.

Example Output.PNG

Summary

Hopefully that helps you quickly get started with the Lithnet REST API for the FIM/MIM Service. I showed an example using PowerShell directly, but using an Azure Function is also a valid pattern. I’ve covered similar functionality in the past.
 

Exporting IoT Device Information from Azure IoT Hub(s) using PowerShell

Introduction

I have a number of Azure IoT Hubs each with a number of devices configured on them. I wanted to export the details for each IoT Device. This can’t be done via the Azure Portal (May 2018) so I looked to leverage the Azure.IoTHub New-AzureRmIotHubExportDevices cmdlet.

Now the documentation for New-AzureRmIotHubExportDevices is a little light on. When I was running the New-AzureRmIotHubExportDevices I kept getting the error ‘Operation returned an invalid status code ‘InternalServerError’.

After many attempts (over weeks) I finally was able to export my IoT devices using PowerShell. The key was to generate the SAS Storage Token for the Container rather than creating a blob file to export to and generating a SAS Token for the file. Simply specify the Storage Container to export too.

Overview

My sample script below uses the latest (as of May 2018) version of the Azure.IoTHub Module (v3.1.3). It;

  • enumerates all Resource Groups in an Azure Subscription and looks for IoT Hubs and puts them into a collection
  • then iterates through each IoT Hub, creates an associated Storage Account (if one doesn’t exist)
  • Exports the IoT Devices associated with the IoT Hub to Azure Storage
  • Downloads the IoT Devices Blob File, opens it and displays through PowerShell console output the IoT Device Names and Status

Export IoT Devices 640px

To use the script you will just need to;

  • change your Subscription Name in Line 4
  • The location where you want to download the blob files too in Line 31
  • if you want to display additional info on each device or do something else with the info change line 71 accordingly

The exported file can be found using Azure Storage Explorer as shown below.

Output Blob File.PNG

And the script outputs the status to the PowerShell console as shown below.

Exported IoT Devices.PNG

The exported object contains all the details for each IoT Device as shown below in the IoT Device PSObject.

IoT Device PSObj.PNG

Summary

It is obvious once you work out how the cmdlet works. Hopefully this working example will save someone else a few hours of head scratching.

Building a Teenager Notification Service using Azure IoT an Azure Function, Microsoft Flow, Mongoose OS and a Micro Controller

Introduction

This is the third and final post on my recent experiments integrating small micro controllers (ESP8266) running Mongoose OS integrated with Azure IoT Services.

In the first post in this series I detailed creating the Azure IoT Hub and registering a NodeMCU (ESP8266 based) micro controller with it. The post detailing that can be found here. Automating the creation of Azure IoT Hubs and the registration of IoT Devices with PowerShell and VS Code

In the second post I detailed communicating with the micro controller (IoT device) using MQTT and PowerShell. That post can be found here. Integrating Azure IoT Devices with MongooseOS MQTT and PowerShell

Now that we have end to end functionality it’s time to do something with it.

I have two teenagers who’ve been trained well to use headphones. Whilst this is great at not having to hear the popular teen bands of today, and numerous Facetime, Skype, Snapchat and similar communications it does come with the downside of them not hearing us when we require their attention and they are at the other end of the house. I figured to avoid the need to shout to get attention, a simple visual notification could be built to achieve the desired result. Different colours for different requests? Sure why not. This is that project, and the end device looks like this.

IoT Notifier using Neopixel
IoT Notifier using Neopixel

Overview

Quite simply the solution goes like this;

  • With the Microsoft Flow App on our phones we can select the Flow that will send a notification
2018-03-25 18.56.38 500px.png
Send IoT Notification Message
  • Choose the Notification intent which will drive the color displayed on the Teenager Notifier.
2018-03-25 18.56.54 500px
IoT Notifier Task Message
  • The IoT Device will then display the color in a revolving pattern as shown below.

The Architecture

The end to end architecture of the solution looks like this.

IoT Cloud to Device - NeoPixel - 640px
IoT Message Cloud to Device

Using the Microsoft Flow App on a mobile device gives a nice way of having a simple interface that can be used to trigger the notification. Microsoft Flow sends the desired message and details of the device to send it to, to an Azure Function that puts a message into an MQTT queue associated with the Mongoose OS driven Azure IoT Device (ESP8266 based NodeMCU micro controller) connected to an Azure IoT Hub. The Mongoose OS driven Azure IoT Device takes the message and displays the visual notification in the color associated with the notification type chosen in Microsoft Flow at the beginning of the process.

The benefits of this architecture are;

  • the majority of the orchestration happens in Azure, yet thanks to Azure IoT and MQTT no inbound connection is required where the IoT device resides. No port forwarding / inbound rules to configure on your home router. The micro controller is registered with our Azure IoT Hub and makes an outbound connection to subscribe to its MQTT topic. As soon as there is a message for the device it triggers its logic and does what we’ve configured
  • You can initiate a notification from anywhere in the world (most simply using the Flow mobile app as shown above)
  • And using Mongoose OS allows for the device to be managed remote via the Mongoose OS Dashboard. This means that if I want to add an additional notification (color) I can update Flow for a new option to select and update the configuration on the Notifier device to display the new color if it receives such a command.

Solution Prerequisites

This post builds on the previous two. As such the prerequisites are;

  • you have an Azure account and have set up an IoT Hub, and registered an IoT Device with it
  • your IoT device (micro controller) can run Mongoose OS on. I’m using a NodeMCU ESP8266 that I purchased from Amazon here.
  • the RGB LED Light Ring (generic Neopixel) I used I purchased from Amazon here.
  • 3D printer if you want to print an enclosure for the IoT device

With those sorted we can;

  • Install and configure my Mongoose OS Application. It includes all the necessary libraries and sample config to integrate with a Neopixel, Azure IoT, Mongoose Dashboard etc.
  • Create the Azure PowerShell Function App that will publish the MQTT message the IoT Device will consume
  • Create the Microsoft Flow that will kick off the notifications and give use a nice interface to send what we want
  • Build an enclosure for our IoT device

How to build this project

The order I’ve detailed the elements of the architecture here is how I’d recommend approaching this project. I’d also recommend working through the previous two blog posts linked at the beginning of this one as that will get you up to speed with Mongoose OS, Azure IoT Hub, Azure IoT Devices, MQTT etc.

Installing the AzureIoT-Neopixel-js Application

I’ve made the installation of my solution easy by creating a Mongoose OS Application. It includes all the libraries required and sample code for the functionality I detail in this post.

Clone it from Github here and put it into your .mos directory that should be in the root of your Windows profile directory. e.g C:\Users\Darren\.mos\apps-1.26 then from the MOS Configuration page select Projects, select AzureIoT-Neopixel-JS then select the Rebuild App spanner icon from the toolbar. When it completes select the Flash icon from the toolbar.  When your micro controller restarts select the Device Setup from the top menu bar and configure it for your WiFi network. Finally configure your device for Azure MQTT as per the details in my first post in this series (which will also require you to create an Azure IoT Hub if you don’t already have one and register your micro controller with it as an Azure IoT Device). You can then test sending a message to the device using PowerShell or Device Explorer as shown in post two in this series.

I have the Neopixel connected to D1 (GPIO 5) on the NodeMCU. If you use a different micro controller and a different GPIO then update the init.js configuration accordingly.

Creating the Azure Function App

Now that you have the micro controller configured and working with Azure IoT, lets abstract the sending of the MQTT messages into an Azure Function. We can’t send MQTT messages from Microsoft Flow, so I’ve created an Azure Function that uses the AzureIoT Powershell module to do that.

Note: You can send HTTP messages to an Azure IoT device but … 

Under current HTTPS guidelines, each device should poll for messages every 25 minutes or more. MQTT and AMQP support server push when receiving cloud-to-device messages.

….. that doesn’t suit my requirements 

I’m using the Managed Service Identity functionality to access the Azure Key Vault where credentials for the identity that can interact with my Azure IoT Hub is stored. To enable and use that (which I highly recommend) follow the instructions in my blog post here to configure MSI on an Azure Function App. If you don’t already have an Azure Key Vault then follow my blog post here to quickly set one up using PowerShell.

Azure PowerShell Function App

The Function App is an HTTP Trigger Based one using PowerShell. In order to interact with Azure IoT Hub and integrate with the IoT Device via Azure I’m using the same modules as in the previous posts. So they need to be located within the Function App.

Specifically they are;

  • AzureIoT v1.0.0.5
  • AzureRM v5.5.0
  • AzureRM.IotHub v3.1.0
  • AzureRM.profile v4.2.0

I’ve put them in a bin directory (which I created) under my Function App. Even though AzureRM.EventHub is shown below, it isn’t required for this project. I uploaded the modules from my development laptop (C:\Program Files\WindowsPowerShell\Modules) using WinSCP after configuring Deployment Credentials under Platform Features for my Azure Function App. Note the path relative to mine as you will need to update the Function App script to reflect this path so the modules can be loaded.

Azure Function PS Modules.PNG
Azure Function PS Modules

The configuration in WinSCP to upload to the Function App for me is

WinSCP Configuration
WinSCP Configuration

Edit the AzureRM.IotHub.psm1 file

The AzureRM.IotHub.psm1 will locate an older version of the AzureRM.IotHub PowerShell module from within Azure Functions. As we’ve uploaded the version we need, we need to comment out the following lines in AzureRM.IotHub.psm1 so that it doesn’t do a version check. See below the lines to remark out (put a # in front of the lines indicated below) that are near the start of the module. The AzureRM.IotHub.psm1 file can be edited via WinSCP & notepad.

#$module = Get-Module AzureRM.Profile
#if ($module -ne $null -and $module.Version.ToString().CompareTo("4.2.0") -lt 0)
#{
# Write-Error "This module requires AzureRM.Profile version 4.2.0. An earlier version of AzureRM.Profile is imported in the current PowerShell session. Please open a new session before importing this module. This error could indicate that multiple incompatible versions of the Azure PowerShell cmdlets are installed on your system. Please see https://aka.ms/azps-version-error for troubleshooting information." -ErrorAction Stop
#}
#elseif ($module -eq $null)
#{
# Import-Module AzureRM.Profile -MinimumVersion 4.2.0 -Scope Global
#}

HTTP Trigger Azure PowerShell Function App

Here is my Function App Script. You’ll need to update it for the location of your PowerShell Modules (I created a bin directory under my Function App D:\home\site\wwwroot\myFunctionApp\bin), your Key Vault details and the user account you will be using. The User account will need permissions to your Key Vault to retrieve the password (credential) for the account you will run the process as and to your Azure IoT Hub.

You can test the Function App from within the Azure Portal where you created the Function App as shown below. Update for the names of the IoT Hub, IoT Device and the Resource Group in your associated environment.

Testing Function App.PNG
Test Function App

Microsoft Flow Configuration

The Flow is very simple. A manual button and a resulting HTTP Post.

Microsoft Flow Config 1
Microsoft Flow Configuration

For the message I have configured a list. This is where you can choose the color of the notification.

Manual Trigger.PNG
Microsoft Flow Manual Trigger

The Action is an HTTP Post to the Azure Function URL. The body has the configuration for the IoTHub, IoTDevice, Resource Group Name, IoTKeyName and the Message selected from the manual button above. You will have the details for those settings from your initial testing via the Function App (or PowerShell).

The Azure Function URL you get from the top of the Azure Portal screen where you configure your Function App. Look for “Get Function URL”.

HTTP Post
Microsoft Flow HTTP Post

Testing

Now you have all the elements configured, install the Microsoft Flow App on your mobile if you don’t already have it for Apple iOS Appstore and Android Google Play Log in with the account you created the Flow as, select the Flow, the message and done. Depending on your internet connectivity you should see the notification in < 10 seconds displayed on the Notifier device.

Case 3D Printer Files

Lastly, we need to make it look all pretty and make the notification really pop. I’ve created a housing for the neopixel that sits on top of a little case for the NodeMCU.

As you can see from the final unit, I’ve printed the neopixel holder in a white PLA that allows the RGB LED light to be diffused nicely and display prominently even in brightly lit conditions.

Neopixel Enclosure
Neopixel Enclosure

I’ve printed the base that holds the micro controller in a different color. The top fits snugly through the hole in the micro controller case. The wires from the neopixel to connect it to the micro controller slide through the shaft of the top housing. It also has a backplate that attaches to the back of the enclosure that I secure with a little hot glue.

Here is a link to the Neopixel (WS2812) 16 RGB LED light holder I created on Thingiverse.

NodeMCU Enclosure.PNG
NodeMCU Enclosure

Depending on your micro controller you will also need an appropriately sized case for that. I’ve designed the neopixel light holder top assembly to sit on top of my micro controller case. Also available on Thingiverse here.

Summary

Using a combination of Azure IoT, Azure PaaS Services, Mongoose OS and a cheap micro controller with an RGB LED light ring we have a very versatile Internet of Things device. The application here is a simple visual notifier. A change of output device or even in conjunction with an input device could change the application, whilst still re-using all the elements of the solution that glues it all together (micro-controller, Mongoose OS, Azure IoT, Azure PaaS). Did you build one? Did you use this as inspiration to build something else? Let me know.