Azure Sphere – Initial Setup, Configuration and First Impressions

In April this year, Microsoft announced Azure Sphere. This was the same week as I’d be preparing for a presentation I was giving on Azure IoT at the Sydney location for the Global Azure Bootcamp. When pre-orders became available from Seeed Studio I naturally signed up as I’ve previously bought many IoT related pieces of hardware from Seeed Studio.

Fast forward to this week and the Azure Sphere MT3620 device shipped. It’s a long weekend here in Sydney Australia and delivery wasn’t due until after the long weekend, but by some miracle the packaged was delivered on the Friday by DHL after only leaving China 3-4 days earlier.

What a great opportunity then to un-box it, get it configured and build the sample “Hello World” (Blinky) project.

Getting Started

Following the “Get Started Guide” here I was straight away perplexed as to why Visual Studio was required, when I’ve made the complete transition to Visual Studio Code.

It seems there isn’t support in the IoT Workbench Extension in VS Code for the MT3620 yet.

Azure IoT Workbench.PNG

After patching and updating my now out-of-date Visual Studio installation I was finally able to install the VS Tools for Azure Sphere.

Azure Sphere VS Tools.PNG

which also comes with the TAP Driver for communicating with the device via the USB port, which is necessary for setup.

TAP Driver.PNG

With that all done it needs to be connected to Azure Active Directory. For that I created a new user for use with Azure Sphere in my Azure AD Tenant and then proceeded to login to Azure AD with that account.

azsphere login

azsphere login.PNG

Permissions.PNG

Successfully logged in (if you try with a Microsoft Account you’ll get a message indicating Azure AD is required), it prompts you to create an Azure Sphere Tenant.

Create Tenant

NOTE: Claiming the Device

Claiming the Device.PNG

With the Azure Sphere Device connected the Windows 10 computer you are executing the command from, as this is the first time setup an Azure Sphere Tenant needs to be created and the device claimed.

azsphere tenant create --name 
azsphere device claim

Claim Device.PNG

Connecting to Wifi

With the Azure Sphere Tenant created and the device claimed its time to connect it to Wifi.

azsphere device wifi show-status
azsphere device wifi add --ssid  --key

Connect Azure Sphere to Wifi.PNG

Checking the Wifi Connection Status after connecting provides the device connection status.

azsphere device wifi show-status

Azure Sphere Wifi Status.PNG

Checking the Azure Sphere OS Version against what is available shows it’s on the latest.

azsphere device show-ota-status

Azure Sphere OS Version.PNG

Blink Example Project

With the device now configured it was time to try out the sample project. Again following the instructions I first Enabled Debugging.

azsphere device prep-debug

Enable Azure Sphere Debugging.PNG

Following the example as per the Getting Started Guide I built the Blink Example project.

New Project Azure Sphere Blink Example.PNG

and ran it. It all worked as per the instructions. Pressing the A button with debugging enabled allow the state of the device (button) to be read and output.

Blink Example with Debugging.PNG

Summary

The setup was very quick, completely painless and just worked. So initial impressions are positive. My only gripe is that the Azure IoT Workbench Extension for VS Code doesn’t support the hardware. I’m hoping that comes soon.

Now to build something with it. What to build ……..

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
Voice Assistant for Microsoft Identity Manager Architecture

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
Voice Search for Microsoft Identity Manager Auditing and Reporting

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
Azure IoT Hub Events

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
Azure Stream Analytics Inputs

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
Azure Stream Analytics Outputs

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
Azure Stream Analytics Query

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
Table Storage Events

A full record looks like this.

Full Record.PNG
Voice Search Table Storage Audit Record

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
PowerBI Voice Search Analytics

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.

Adding a Display to the Teenager Notification Service Azure IoT Device

Overview

A couple of weeks back I wrote this post that detailed Building a Teenager Notification Service using Azure IoT an Azure Function, Microsoft Flow, Mongoose OS and a Micro Controller. 

Over the Easter break I enhanced it with the inclusion of a display. I was rummaging around in a box of parts when I found a few LCD displays I’d purchased on speculation some time ago. They are SSD1306 LCD driven units that can be found on Amazon here. A quick upgrade later and …

… scrolling text to go with rotating lights. The addition of the display requires the following changes to the previous project which are detailed in this post;

  • inclusion of the SSD1306 library
  • configure your micro controller for the display
  • a few changes in the Mongoose OS Init.JS file to have the appropriate text displayed for the notification
  • change to the Notifier Base case to integrate the display
    • it is available in the Thingiverse Project for this thing here and named NodeMCU with Display Window.stl

Incorporating the SSD1306 Library

Before starting, with your micro controller connected and using the MOS UI, take a copy of your Init.js configuration file by selecting Device Files, then Init.js and copying the content to somewhere safe. Also the Device Config by choosing Device Config, Expert View and Save Configuration.

From the MOS UI select Projects, select the AzureIoT-Neopixel-js project then from the drop down menu select mos.yml.

Add the line  – origin: https://github.com/mongoose-os-libs/arduino-adafruit-ssd1306 then select the Spanner icon to Rebuild the App. Once completed select the Flash icon to update your micro controller.

Include SSD1306 Library.PNG

Once written to your micro controller check your Init.js and copy back your backup. Check your Configuration and make sure your MQTT settings are still present. Copy your previous config back if required.

Configure your Micro Controller for the SSD1306 Display

We need to tell your micro controller which GPIO Pins we have attached the display too. I actually also moved the GPIO Pin I attached for the Neopixel as part of this. The configuration is;

    • Neopixel connected to GPIO 12
    • SSD1306 SDA connected to GPIO 4
    • SSD1306 SCL connected to GPIO 5

 

In the Expert Device Config mode update the I2C section as shown below. Save the configuration.

 "i2c": {
 "enable": true,
 "freq": 100000,
 "debug": false,
 "sda_gpio": 4,
 "scl_gpio": 5
 },

Wiring the SSD1306 to the Micro Controller

Looking at the NodeMCU diagram you can see where the connections need to be made for the NeoPixel and SSD1306 display. SSD1306 SCL to D1, SDA to D2. The Neopixel data connection is now on D6. Power and GND using the PWR and GND pins. I’m using them all on the same side of the NodeMCU to make it fit cleanly into the case later.

NodeMCU.png

Init.js code additions

Incorporate the display library in your Init.js by including the line below.

load('api_arduino_ssd1306.js');

With that done we to initialize the display also in the Init.js. The following lines initialize the display address, SCL pin the display is connected to, the size of the text we are going to display and color. Put them before or after the initialization for the Neopixel.

//------------ Setting up Display ----------------
let oled_addr = 0x3C; // I2C Address for SSD1306let 
oled = Adafruit_SSD1306.create_i2c(5 /* RST GPIO */, Adafruit_SSD1306.RES_128_32);

// Initialize the display. 
oled.clearDisplay();
oled.setTextSize(2);
oled.setTextColor(Adafruit_SSD1306.WHITE);

In the MQTT Subscriber section where you are looking at the MQTT message being sent from the Microsoft Flow and displaying a color on the Neopixel add the following lines to send output to the display. The following below outputs Pink to the display. If Pink indicates some task then change oled.write(‘PINK’); to oled.write(‘TASK’); or similar.

 if (msg === "Pink"){
 // PINK 
 oled.clearDisplay();
 oled.setTextSize(2);
 oled.setCursor(1, 10);
 oled.write('PINK');
 oled.display();
 oled.startScrollLeft(0x00, 0x0F);

Following the Neopixel loop after

 strip.clear();
 strip.show(strip);

add the following to clear the display as the the Neopixel has finished displaying its color notification.

 oled.clearDisplay();
 oled.display();

Repeat for the differing colors and their tasks/meanings.

Summary

Now the notifier includes both a visual color notification AND the text associated with the notification. No confusion here, or does it need a buzzer as well?

Evaluating the migration of Azure Functions to Microsoft Flow – Twitter IoT Integration

 

Introduction

Almost 18 months ago I wrote this post on integrating Twitter with Azure Functions to Tweet IoT data. A derivative of that solution has been successfully running for about the same period. Azure Functions have been bullet proof for me.

After recently implementing Microsoft Flow as detailed in my Teenager Notification Device post here I started looking at a number of the Azure Functions I have running and looked at what would be better suited to being implemented with Flow. What could I simplify by migrating to Microsoft Flow?

The IoT Twitter Function linked above was one the simpler Functions I had running that I’ve transposed and it has been running seamlessly. I chose this particular function to migrate as the functions it was performing were actions that Microsoft Flow supported. Keep in mind (see the Summary), that there isn’t a one size fits all. Flow and Functions each have their place and often work even better together.

Comparison

Transposing the IoT Twitter Function App to Microsoft Flow provided me with the same outcome, however the effort to get to that outcome is considerably less. As a quick comparison I’ve compared the key steps I needed to perform with the Azure Function to enable the integration vs what it took to implement with Microsoft Flow.

Function vs Flow.PNG

That’s pretty compelling. For the Azure Function I needed to register an App with Twitter and I needed to create an Azure Function App Plan to host my Azure Function. With Microsoft Flow I just created a Flow.

To setup and configure the Azure Function I needed to set up Deployment Options to upload the Twitter PowerShell Module (this is the third-party module), and I needed to store the two credential sets associated with the Twitter Account/App. In Microsoft Flow I just chose Twitter as an Action and provided conscent to the oAuth2 challenge.

Finally for the logic of the Azure Function I had to write the script to retrieve the data, manipulate it, and then post it to Twitter. In Microsoft Flow it was simply a case of configuring the workflow logic.

Microsoft Flow

As detailed above, the logic is still the same. On a schedule, get the data from the IoT Devices via a RestAPI, manipulate/parse the response and output a Tweet with the environment info. Doing that in Flow though means selection of an action and configuring it. No code, no modules, no keys.

Below is a resultant Flow (overview) to achieve the same result as my Azure Function that I originally implemented as an Azure Function as detailed here.

MS Flow - Twitter.PNG

The schedule part is triggered hourly. Using Recurrence it is easy to set the schedule (much easier than a CRON format in Azure Functions) complete with timezone (within the advanced section). I then get the Current time to allow me to acquire the Date and Time in a format that I will use in the resulting tweet.

Schedule

Next is to perform the first RestAPI call to get the data from the first of the IoT devices. Parse the JSON response to get the temperature value.

GET

Repeat the above step for the other IoT Device located in a different environment and parse that. Formulate the Tweet using elements of information from the Flow.

Repeat and Tweet

Looking at Twitter we see a resultant Tweet from the Flow.

Tweet.PNG

Summary

This is a relatively simple flow. Bare in mind I haven’t included any logic to validate what is returned or perform any conditional operations during processing. But very quickly it is possible to retrieve, manipulate and output to a different medium.

So why don’t I used Flow for everything? The recent post I mentioned at the beginning for the Teenager Notification Device that also used a Flow, also uses an Azure Function. For that use case the integration of the IoT Device with Azure IoT is via MQTT. There isn’t currently that capability in Flow. But Flow was used to initiate an Action of initiating a trigger for an Azure Function that in turn sent an MQTT message to an IoT Device. The combination of Flow with Functions provides a lot of flexibility and power.

 

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.

Automating the creation of Azure IoT Hubs and the registration of IoT Devices with PowerShell and VS Code

The creation of an Azure IoT Hub is quick and simple, either through the Azure Portal or using PowerShell. But what can get more time-consuming is the registration of IoT Devices with the IoT Hub and generation of SAS Tokens for them for authentication.

In my experiments with micro-controllers and their integration with Azure IoT Services I often find I keep having to manually do tasks that should have just been automated. So I did. In this post I’ll cover using PowerShell to;

  • create an Azure IoT Hub
  • register an Azure IoT Device
  • generate a SAS Token for the IoT Device to use for authentication to an Azure IoT Hub from a Mongoose OS enabled ESP8266 micro controller

IoT Integration

Prerequisites

In order to fully test this, ideally you will have a micro-controller. I’m using an ESP8266 based micro-controller like this one. If you want to test this out without physical hardware, you could generate your own DeviceID (any text string) and use the AzureIoT Library detailed further on to send MQTT messages.

You will also require an Azure Subscription. I detail using a Free Tier Azure IoT Hub which is limited to 8000 messages per day. And instead of using PowerShell/PowerShell ISE get/use Visual Studio Code.

Finally you will need the AzureRM and AzureIoT PowerShell modules. With WinRM 5.x you can get them from the PowerShell Gallery with;

install-module AzureRM
install-module AzureIoT

Create an Azure IoT Hub

The script below will create a Free Tier Azure IoT Hub. Change the location (line 15) for which Azure Region you will use (the commands on the lines above will list what regions are available), the Resource Group Name that will be created to hold it (line 18) and the name of the IoT Hub (line 23) and let it rip.

From your micro-controller we will need the DeviceID. I’m using the ID generated by the device which I obtained from the Device Configuration => Expert View of my Mongoose OS enabled ESP8266.

Device Config.PNG

Register the IoT Device with our Azure IoT Hub

Using the AzureIoT PowerShell module we can automate the creation/registration of the IoT Device. Update the script below for the name of your IoTHub and the Resource Group that contains it that you created earlier (lines 7 and 11). Update line 21 for the DeviceID or your new IoT Device. I’m using the AzureIoT module to do this. With WinRM 5.x you can install it quickly fromt the gallery with install-module AzureIoT

Looking at our IoTHub in the Azure Portal we can see the newly registered IoT Device.

DeviceCreated.png

Generate an IoT Device SAS Token

The final step is to create a SAS Token for our IoT Device to use to connect to the Azure IoTHub. Historically you would use the IoT Device Explorer to do that. Alternatively you can also use the code samples to implement the SAS Device Token generation via an Azure Function App. Examples exist for JavaScript and C#. However as of mid-January 2018 you can do it direct from VS Code or Azure Cloud Shell using the Azure CLI and the IOT Extension. I’m using this method here as it is the quickest and simplest method of generating the Device SAS Token.

The command to generate a token that would work for all Devices on an IoT Hub is

az iot hub generate-sas-token --hub-name

Here I show executing it via the Azure Cloud Shell after installing the IOT Extensions as detailed here. To open the Bash Cloud Shell select the >_ icon next to the notification bell in the right top menu list.

Generate IOT Device SAS Token.PNG

As we have done everything else via PowerShell and VS Code we can also do it easily from VS Code. Install the Azure CLI Tools (v0.4.0 or later in VS Code as detailed here. Then from within VS Code press Control + Shift + P to open the Command Palette and enter Azure: Sign In. Sign in to Azure. Then Control + Shift + P again and enter Azure: Open Bash in Cloud Shell to open a Bash Azure CLI Shell. You can check to see if you have the Azure CLI IOT Extension (if you’ve previously used the Azure CLI for IoT operations) by typing;

az extension show --name azure-cli-iot-ext

and install it if you don’t with;

az extension add --name azure-cli-iot-ext

Then run the same command from VS Code to generate the SAS Token

az iot hub generate-sas-token --hub-name

VSCode Generate SAS Token.PNG

NOTE: That token can then be used for any Device registered with that IOT Hub. Best practice is to have a token per device. To do that type

az iot hub generate-sas-token --hub-name  --device-id

Generate SAS Token VS Code Per Device.PNG

By default you will get a token valid for 1 hour. Use the –duration switch to specify the duration of the token you require for your environment.

We can now take the SAS Token and put it into our MQTT Config on our Mongoose OS IoT Device. Update the Device Configuration using Expert View and Save.

Mongoose SAS Config.PNG

We can then test our IoT Device sending updates to our Azure IoT Hub. Update Init.js using the telemetry sample code from Mongoose.

load('api_config.js');
 load('api_mqtt.js');
 load('api_sys.js');
 load('api_timer.js');

let topic = 'devices/' + Cfg.get('device.id') + '/messages/events/';

Timer.set(1000, true /* repeat */, function() {
 let msg = JSON.stringify({ ram: Sys.free_ram() });
 let ok = MQTT.pub(topic, msg, 1);
 print(ok, topic, '->', msg);
 }, null);

We can then see the telemetry being sent to our Azure IOT Hub using MQTT. In the Device Logs after the datestamp and before device/ if you see a 0 instead of 1 (as shown below) then your conenction information or SAS Token is not correct.

Mongoose IOT Events.png

On the Auzre IoT side we can then check the metrics and see the incoming telemetry using the counter Telemetry Metrics Sent as shown below.

Telemetry Metrics Sent.PNG

If you don’t have an IoT Device you can simulate one using PowerShell. The following example shows sending a message to our IoT Hub (using variables from previous scripts).

$deviceParams = @{
 iotConnString = $IoTConnectionString
 deviceId = $deviceID
}
$deviceKeys = Get-IoTDeviceKey @deviceParams 
# Get Device 
$device = Get-IoTDeviceClient -iotHubUri $IOTHubDeviceURI -deviceId $deviceID -deviceKey $deviceKeys.DevicePrimaryKey

# Send Message
$deviceMessageParams = @{
 deviceClient = $device
 messageString = "Azure IOT Hub"
}
Send-IoTDeviceMessage -deviceClient $deviceMessageParams

Summary

Using PowerShell we have quickly been able to;

  • Create an Azure IoT Hub
  • Register an IoT Device
  • Generate the SAS Token for the IoT Device to authenticate to our IoT Hub with
  • Configure our IoT Device to send telemetry to our Azure IoT Hub and verify integration/connectivity

We are now ready to implement logic onto our IoT Device for whatever it is you are looking to achieve.

Getting started developing Custom Actions for the Google Assistant (Home)

Introduction

Whilst I was in the USA recently I bought myself a Google Home. My home already had Hue Lights, Chromecast on a couple of TV’s and I’m a big user of Spotify (Premium). It was very quick to get it up and running and doing simple tasks, but I started thinking about what custom things I could get it to do. Could I get it to call custom/private API to get some information and let me know the result? The answer is yes, and that is what this post will cover. I have a few temperature sensors that have a RestAPI that can be used to query the temperature. I got it working pretty quickly (one evening) for not having messed with the Google App Engine for many years. But there were a number of steps required, and as this functionality will form the basis for more complex functionatlity I’ve documented the step by step process I used.

But here is the result. A command, a query and custom response.

Prerequisites

  • First up you are going to need to have a Google Account. Seeing as you are probably reading this because you want to do something similar, you will have a Google Home and thereby a Google account, so you’re already covered on that point. The account associated with your Google Home is the one you NEED to use with the additional services, as it is what will tie everything together
  • You will need to register for an api.ai account. Sign in with the Google Account you have linked to your Google Home
  • You will need to also register for a Google Cloud account. Sign in with the Google Account you have linked to your Google Home
  • Download and install the Google Cloud CLI. Even though I did this on a Windows machine, I actually installed the CLI in Ubuntu via the Windows Subsystem for Linux. If you want that option, here is how to get started with the Windows Subsystem for Linux
    • change into your homedir eg cd ~/ and then run curl https://sdk.cloud.google.com | bash

Getting Started

Now it is time to start our Project. In your gCloud CLI window create a project directory. I named mine insidetemp as I will be calling an IOT Temperature sensor to get the temperature inside one of my beer brewing sheds.

inside temp.PNG

We will now create a small Javascript script that will call a RestAPI to retrieve the current temperature from an IOT Temperature Sensor. The script below does that. Modify it for whatever unauthenticated API call you want to make. You’ll need to change lines 6, 7 and 24 for building the URL you will call to get data. Line 8 contains the Function name. Mine is called insideTemp. If you name yours different, then everywhere I use ‘insideTemp’ (not case) substitute what you changed your function name to.

With your modified script (I’ve changed the api key so it won’t work as is), in your application directory create a new file named index.js and paste in your script. e.g in Linux run nano index.js 

After pasting in your script, use Cntrl + O to save as index.js and Cntrl + X to quit.

custom action function
custom action function

Run gcloud auth login and go through the authentication process using the same Google Account you used for api.ai and Google Cloud. The CLI will give you a URL you need to paste into your browser and authorize access to Google Cloud for your Google Account. Once completed get the code generated from the authorization and paste that back into the CLI

gcloud authentication
gcloud authentication

Now we will create a Google Cloud Storage Bucket for our script. I named mine the same as the Function in the script (line 8) but all in lowercase (as lower case is required).
gsutil mb gs://insidetemp

GCloud Storage Bucket.PNG

You should then be able to browse to your storage. My URL is below. Updated for your project name.

https://console.cloud.google.com/storage/browser/insidetemp?project=insidetemp

Google Cloud Storage
Google Cloud Storage

We will now upload our script and create the Function that we will call from Google Home. Change the command below for your project and script function name.

gcloud beta functions deploy insideTemp –stage-bucket insidetemp –trigger-http 

Upload and create function
Upload and create function

Once uploaded and the Function has been created you will get a HTTP Trigger URL. Copy this as you’ll need it shortly. If you need to update or change the script do that locally and run the gcloud beta functions deploy command again.

Function Created
Function Created

Validating the Function

Using a browser you should be able to browse to your Function. The URL should look something like this. Change for your project name. https://console.cloud.google.com/functions/details/us-central1/insideTemp?project=insidetemp&tab=general&duration=PT1H

If the Storage account is different (because you already had one) then navigate to https://console.cloud.google.com/functions and use select your project from the menu in the top left next to Google Cloud Platform.

Select the Testing menu then click on the Test the function button. If your script is all good it will execute and get the value back from the API. You can see mine returned 21 degrees from my IOT Temp Sensor.

Test the Function
Test the Function

Wiring up our Project to Google Home

Now that we have an HTTP Function that can retrieve info from a RestAPI let’s have that as an action in Google Home. Head on over to the Actions API here;

https://console.cloud.google.com/apis/api/actions.googleapis.com/overview

At the top of the middle pane click enable if it isn’t already enabled.

Enable Google Actions API
Enable Google Actions API

Now head over to the api.ai Agents Console https://console.api.ai/api-client/#/agents and select Create Agent.

Create Agent
Create Agent

Give it a name, choose your timezone and language and select Save.

New Agent Details
New Agent Details

Select Create Intent

Create Intent
Create Intent

Provide a couple of phrases you will speak to Google Home. Don’t worry about any other settings for now. Select Save.

Create Intent Detail
Create Intent Detail

From the Left Menu select Fulfillment.

Fulfillment
Fulfillment

Enable Webhook and paste in the URL that you got after uploading the Function Script via the gCloud cli. Select Save from the bottom of the page.

Fulfillment Webhook
Fulfillment Webhook

Back in Intents, scroll to the bottom and click Fulfillment and enable Use webhook. Select Save

Intent Webhook
Intent Webhook

From the left menu select Integrations 

Integrations
Integrations

Enable Actions on Google

Actions on Google
Actions on Google

Open Settings and select  Update Draft.

Actions Trigger
Actions Trigger

then select Test 

Actions Test
Actions Test

Update: I did also remove the Default Welcome Intent and use one of my Intent phrases. 

Test will new be active. Select Close

Actions Test
Actions Test

Head back to api.ai and select Intents. Under Response choose the Actions on Google menu and enable it. Select Save.

Intent Actions on Google
Intent Actions on Google

Testing the Custom Action via the API Console

Within the api.ai console in the right hand pane in the Try it now box type in your Intent. One of mine is what is the inside temp. This tests our integration and successfully returns the result from querying the API via our Function.

Test Success
Test Success

If you select Show JSON from the bottom right you can see the processing that went on. We can see that the Agent got the query, used the Webhook to go to Fulfillment to use the Function to call the API to get our information and provide the response.

Success JSON.PNG

Testing the Custom Action via Google Home

Go to Actions on Google https://console.actions.google.com/u/0/ and select Add/Import Project. You should see a project named NewAgent. Select it

New Actions Project
New Actions Project

Then Select Import Project

Import Project
Import Project

Step through to provide info for the Agent, including images etc as if you are going to publically publish this function. Select Save.

App Info
App Info

Finally select Test Draft. DO NOT SELECT SUBMIT DRAFT FOR REVIEW.

Test Draft
Test Draft

As we are in ‘test’ mode we follow the  Ok Google wake command with Talk to . So for my project it is Ok Google, Talk to Inside Temperature.

And we are done.

Summary

Very quickly we’ve created a custom command that queries our own API to get data and have the response spoken to us. This can then be expanded to do any number of different tasks as long as you have something to query or update and the desire to do it from your Google Home.