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.
After patching and updating my now out-of-date Visual Studio installation I was finally able to install the VS Tools for Azure Sphere.
which also comes with the TAP Driver for communicating with the device via the USB port, which is necessary for setup.
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
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.
NOTE: Claiming the Device
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.
Checking the Wifi Connection Status after connecting provides the device connection status.
azsphere device wifi show-status
Checking the Azure Sphere OS Version against what is available shows it’s on the latest.
azsphere device show-ota-status
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
Following the example as per the Getting Started Guide I built the Blink Example project.
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.
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 ……..
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.
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.
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.
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.
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.
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 Events
A full record looks like this.
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 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.
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.
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.
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.
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.
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
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.
And the script outputs the status to the PowerShell console as shown below.
The exported object contains all the details for each IoT Device as shown below in the IoT Device PSObject.
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.
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;
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
Looking at Twitter we see a resultant Tweet from the Flow.
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.
This is the third and final post on my recent experiments integrating small micro controllers (ESP8266) running Mongoose OS integrated with Azure IoT Services.
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
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
Send IoT Notification Message
Choose the Notification intent which will drive the color displayed on the Teenager Notifier.
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 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.
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 …
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
The configuration in WinSCP to upload to the Function App for me is
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.
Test Function App
Microsoft Flow Configuration
The Flow is very simple. A manual button and a resulting HTTP Post.
Microsoft Flow Configuration
For the message I have configured a list. This is where you can choose the color of the notification.
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”.
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
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.
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.
In my last post here on IoT I detailed getting started with Azure IoT Hubs and registering an IoT device and sending telemetry from the IoT Device to the Azure IoT Hub. And doing all that using PowerShell.
If you haven’t read that post or worked through those steps, stop here, work through that and then come back. This post details configuring MongooseOS to receive MQTT messages from Azure IoT which is the last mile to making the IoT Device flexible for integration with anything you can think of.
Prerequisites
The only change to my setup from the previous post is I installed the Mongoose Demo App onto my ESP8266 device. Specifically the demo-js App detailed in the application list here. Install is quick and simple on Windows using the MOS Tool. Details are here. I also enabled the Mongoose Dashboard on my Mongoose IoT Device so that I don’t have to have the IoT Device connected to my laptop when configuring and experimenting with it. Essentially check the checkbox for Dashboard when configuring the IoT Device when connected locally via a USB cable.
The rest of the configuration is using the defaults in Azure IoT with respect to MQTT.
MongooseOS MQTT Subscribe Configuration – Init.js
On your IoT Device in the MongooseOS init.js we need to configure the ability to subscribe to a MQTT topic. In the first post we were publishing to send telemetry. Now we want to receive messages from Azure IoT.
Include the following lines in your init.js configuration file and restart your IoT Device. The devices//messages/devicebound/# path for the MQTT Subscription will allow the IoT device to subscribe to messages from the Azure IoT Hub.
In order to test the configuration of the IoT Device I initially use the Device Explorer. It is available from GitHub here. The screenshot below shows me successfully sending a message to my IoT Device.
From the Mongoose OS Dashboard we can inspect the Console Log and see the telemetry we are sending to the IoT Hub, but also the message we have received. Success.
Sending MQTT Messages from Azure IoT to MongooseOS using PowerShell
Now that we’ve verified that we have everything setup correctly let’s get to the end goal of sending messages to the IoT Device using PowerShell. Here is a little script that uses the AzureIoT Module that we used previously to assist with configuration automation, to send a message from Cloud to Device.
Update it for your Resource Group, IoTHub, DeviceID and IoTKeyName. Also the message if you feel the need (line 40).
Hello from the Cloud via PowerShell MQTT Message Received.
Summary
Through the two blog posts I’ve detailed the creation of an Azure IoT Hub, registration of an IoT Device, sending telemetry from MongooseOS on the IoT Device to Azure IoT and now sending messages to the IoT Device from Azure, all via PowerShell. Now we have end to end connectivity bi-directionally, what can we do with it? Stay tuned for future posts.
A couple of years ago I bought a number of Philips Hue bulbs and put them in the living areas of my house. Typically we control them via the Hue App on our phones, or via the Google Assistant. This all works very well, but of course I’m a techie and have a bunch of other Internet of Things devices and it would be great to integrate the Hue lights with those.
This post is the first in doing that integration. Setting up access to the Philips Hue Bridge and manipulating the lights. For ease of initial experimentation I’m using PowerShell to perform the orchestration. The API calls can easily be transposed to any other language as they are simple web requests.
Prerequisites
First you will need to have your Philips Hue lights setup with your Philips Hue Bridge. Test the lights are all working via the Philips Hue mobile app.
Locate the IP address of your Philips Hue Bridge. I found mine easily via my Unifi console and you should be able to get it via your home router.
Getting Started
Navigate to your Philips Hue Bridge using a browser and its IP Address. You will see a splash screen with a list of the open source modules that it utilises. Now append the IP Address with /debug/clip.html For me that is;
http://192.168.1.124/debug/clip.html
Create an Account
The Rest API takes a JSON payload. We can quickly create that in the API Debugger. See my example body below and change the URL to /api. Whilst pressing the button on the top of your Philips Hue Bridge select the POST button. This will create an account that you can then use to orchestrate your hue lights.
{“devicetype”:”AzureFunction#iphone Darren”}
Via the API we’ve just created an account. Copy the username from the response. We’ll need this for the API calls.
Test Connection
Change the URL in the debugger as shown below and clear the Message Body. Select GET and you should get returned the light(s) connected to your Philip Hue Bridge.
http:///api//lights
Controlling a Light
I have many lights. In our kitchen we have three pendant lights in a row that are all Philips Hue lights. I’m going to start by testing with one of them. Choosing one from the list from the response above Light 5 should be the middle light. The command is:
http://<yourHueBridge/api//lights//state
In the body put On and True to turn on. False would be to turn it off. Select PUT. My light turned on. Updating the message body to false and pressing PUT turned it off.
Using PowerShell to Manage a Philips Hue Light
Now lets manipulate the Hue Light using PowerShell now that we have an account and know the light we want to manage.
Update the following test script for the IP address of your Philips Hue Bridge, the light number you wish to control and the username you got when you performed the enablement steps earlier. The script will then get the current status of the light and reverse it (turn OFF if it was ON and ON if it was OFF).
Flipping the state of a light
If you have configured everything correctly your light will change and you will get a success reply and the state it transitioned too.
Controlling Multiple Lights
Now let’s do that for multiple lights. In my kitchen we have 3 drop lights over the counter bench. Lets control all three of those. I created a collection of the light numbers, then iterate through each one and flip its state. NOTE: you can also control multiple lights through the Groups method. I won’t be covering that though
I had one set in an inverse state to the other two before I started, to show each is individually updated.
Controlling Multiple Lights and Changing Colors
Now lets change the color. Turn the lights on if they aren’t already and make the color PINK.
As you can see, iterating through the lights the script turns them on and makes them Pink.
Finally, effects for multiple lights
Now lets turn on all the lights, and set them to use the color loop effect (transition through the color spectrum) for 15 seconds then make them all pink.
The lights transition through the color spectrum for 15 seconds then the effect is turned off and the color is set to pink.
Summary
We created an account on the Philips Hue Bridge, were able to enumerate the lights that we have and then orchestrate them via PowerShell.
Here is a short video showing three lights being turned on, changing color and iterating through the color spectrum then setting them Pink.
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
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.
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.
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.
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
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
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.
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.
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.
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).
