Skip to main content

[IoT Home Project] Part 9 - Extending Azure Function to support Thieves Alarm

In this post we will discover how to
  • Crunch distance (sonar) information produced by a GrovePI sensor and send using Raspberry PI and Azure IoT Hub to backend
  • Add filters on top of Service Bus Topic Subscriptions to receive only information related to distance and temperature on each subscription
  • Add new functionality to the current portal to be able to display alarm and notify our user
Story
In this moment we have a system that is able to collect metrics from Raspberry and process some of the data. We already have from GrovePI a sensor that calculate the distance from the sensor to an object. Why not to use it to detect is something is moving in front of it and create a simple alarm system.
Yes, this is not the best sensor that we could use, although this scenario is a great method to learn something new.

Previous post: [IoT Home Project] Part 8 - Connecting to Azure Function and to a virtual heat pump
GitHub source code: https://github.com/vunvulear/IoTHomeProject

What we have until now
We already developed a system that sends data from our sonar (distance sensor) to Azure IoT Hub. From there there is a Stream Analytic job query that push our data to Service Bus Topic. It means that we already have all that we need in Azur and we just need to interpret data and create a notification system.
Below is an overview of the components that are involved, where green border marks a component that needs to be developed.


Filter messages on Service Bus Topic per Subscription
The base concept is to add a filter on each subscription, that would allow only specific messages to pass through. The filtering concept is very powerful and allows us to remove message filtering on the consumer side - being sure all the time that a subscriptions has only specific types of messages.
Unfortunately, there is no support in this moment on Stream Analytics that allow us to set the message properties. When we are sending messages from Stream Analytics to Service Bus, we can specify only the body of the message.
We could find workarounds to it, like using Service Bus Queues or adding an Azure Function between Stream Analytics and Service Bus Topic, that would add the required properties.

Crunch sonar information
This step is done in a similar way as we already done for heating system. The only difference is that in his case we compare the value that is coming from Raspberry PI and compare it with a predefined value.
When we detect that the measured distance is lower than our threshold value we trigger the alarm, by updating a field in Azure Table.
module.exports = function (context, mySbMsg) {
     context.log('Message:', mySbMsg);
    var azure = require('azure-storage');
    var tableSvc = azure.createTableService();
    tableSvc.createTableIfNotExists('systemstatus', function(error, result, response){
        if(!error){
                var motiondetectoron = "false";
                if(mySbMsg.avgdistance < 10)
                {
                    motiondetectoron = "true";
                }
                var entityToUpdate = new Object();
                entityToUpdate.PartitionKey= "system";
                entityToUpdate.RowKey = "motiondetectoron";
                entityToUpdate.Status = motiondetectoron;

                tableSvc.insertOrReplaceEntity ('systemstatus', entityToUpdate, function(error, result, response){
                    if(!error) {
                        context.log('motiondetectoron: ', mySbMsg.avgdistance);
                    }
                });
                               
            };
    });
   
    context.done();
};

Add new functionality in current portal to support Alarms
Similar to heating system, we will use the existing REST API that read data from Azure Table to check if an alarm was triggered. If yes, we will display a custom message on the portal.
    <div @*class="col-md-3" *@>
        <h2>Thief Alarm</h2>
        <div>
            Alarm status: <b id="thiefAlarmStatus">@(Model.AlarmStatus? "ARMED" : "ACTIVE")</b>
        </div>
        <div>          
            <img id="thiefAlarm" src="~/images/@(Model.AlarmStatus?"house-alarm.jpg":"house-secure.png")" height="200" />
        </div>
    </div>

What to remember

  • There is no support to specify message properties for Azure Service Bus from Azure Stream Analytics

Comments

Popular posts from this blog

Windows Docker Containers can make WIN32 API calls, use COM and ASP.NET WebForms

After the last post , I received two interesting questions related to Docker and Windows. People were interested if we do Win32 API calls from a Docker container and if there is support for COM. WIN32 Support To test calls to WIN32 API, let’s try to populate SYSTEM_INFO class. [StructLayout(LayoutKind.Sequential)] public struct SYSTEM_INFO { public uint dwOemId; public uint dwPageSize; public uint lpMinimumApplicationAddress; public uint lpMaximumApplicationAddress; public uint dwActiveProcessorMask; public uint dwNumberOfProcessors; public uint dwProcessorType; public uint dwAllocationGranularity; public uint dwProcessorLevel; public uint dwProcessorRevision; } ... [DllImport("kernel32")] static extern void GetSystemInfo(ref SYSTEM_INFO pSI); ... SYSTEM_INFO pSI = new SYSTEM_INFO(

Azure AD and AWS Cognito side-by-side

In the last few weeks, I was involved in multiple opportunities on Microsoft Azure and Amazon, where we had to analyse AWS Cognito, Azure AD and other solutions that are available on the market. I decided to consolidate in one post all features and differences that I identified for both of them that we should need to take into account. Take into account that Azure AD is an identity and access management services well integrated with Microsoft stack. In comparison, AWS Cognito is just a user sign-up, sign-in and access control and nothing more. The focus is not on the main features, is more on small things that can make a difference when you want to decide where we want to store and manage our users.  This information might be useful in the future when we need to decide where we want to keep and manage our users.  Feature Azure AD (B2C, B2C) AWS Cognito Access token lifetime Default 1h – the value is configurable 1h – cannot be modified

What to do when you hit the throughput limits of Azure Storage (Blobs)

In this post we will talk about how we can detect when we hit a throughput limit of Azure Storage and what we can do in that moment. Context If we take a look on Scalability Targets of Azure Storage ( https://azure.microsoft.com/en-us/documentation/articles/storage-scalability-targets/ ) we will observe that the limits are prety high. But, based on our business logic we can end up at this limits. If you create a system that is hitted by a high number of device, you can hit easily the total number of requests rate that can be done on a Storage Account. This limits on Azure is 20.000 IOPS (entities or messages per second) where (and this is very important) the size of the request is 1KB. Normally, if you make a load tests where 20.000 clients will hit different blobs storages from the same Azure Storage Account, this limits can be reached. How we can detect this problem? From client, we can detect that this limits was reached based on the HTTP error code that is returned by HTTP