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SignalR is a Software Library or a Service provided by Microsoft that allows us to create Real-Time Applications over HTTP.

Real-Time Application means that when something happens on the Server, all the Clients are going to know immediately.

Examples of Real-Time Applications

• Chat Applications
• Online Gaming
• Video Conference Applications
• IoT Devices
• and Many More

How SignalR Works?

In between Client and a Server, SignalR works as a tunnel. Whenever a client wants data to the server, or send data to the server, or Vice Versa. SignalR tunnel allows the communication at the same time between Client and Server.

Why we use SignalR with IoT Devices Applications?

When we are talking about IoT Devices, they are always real-time or very close to real-time.

For example, if any device makes an alert so the Administrator or Device Managers should be known immediately because IoT Devices send billions of data per second and there is no time to click on the refresh again and again.

Technologies behind SignalR

SignalR uses four of the most popular technologies used to make real-time Applications.

• Web Sockets
• Event Source
• Forever Frame
• Long Pooling

Now the question is why we are not going to use the above technologies directly instead of SignalR?

Because, if above technologies get a new Feature, for E.g., your application is programmed using the Web Sockets, so you might have to go and change something for adding the updated Functionality but If you are using SignalR, all of the changes and updates are responsibility of the SignalR and it will give you the right results.

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To improve water management practices and maximize water productivity, In the current situation, models to predict future water requirements based on data mining techniques can be useful.

In this section we will be walking-through an experiment in Azure Machine Learning Studio that will help us to predict future water requirements based on data mining technique.

In order to achieve our goal, we will be using Two Class Decision Forest  for our solution that comes with Azure Machine Learning Studio. Please note that the data we collected for this experiment was through KAISPE Agriculture Remote Monitoring solution using Azure IoT.

So, in the first step we will upload the dataset on Azure Machine Learning Studio in the CSV file format, where it contains historical data composed of attributes on various weather parameters such as Temperature, Humidity, Soil Moisture, phLevel, Rainfall and Wind speed combined with Crop Type and Water Usage.

First, open Azure Machine Learning home page and Click +NEW at the bottom of the window

• Select DATASET .
• Select FROM LOCAL FILE.

In the Upload a new dataset dialog, click Browse, and find the KSP-Irrigation-Data.csv file you created.

Now in the next step we will create an experiment in Machine Learning Studio that uses the dataset you uploaded. So, click +NEW at the bottom of the window and Select EXPERIMENT, and then select “Blank Experiment”.

Select the default experiment name at the top and rename it to Predictive Water Irrigation Experiment and in the module palette to the left of the experiment page, expand Saved Datasets. Find the dataset you created under My Datasets and drag it onto the canvas.

Now let’s prepare the data by using Select Columns in Dataset which is useful if you want to reduce the size of the dataset by deleting unwanted columns.

• Search and drag Select Columns in Dataset, and in the Properties pane to the right page, click Launch column selector and select the following columns:

In addition, we will be using Filter Based Feature Selection which will help us to identifies the column with the strongest predictive power in the input dataset.

• Search and drag Filter Based Feature Selection and in the Properties pane to the right page, click Launch column selector and select the following target column and feature scoring method:

Now, we’ll use our data for both training the model and testing it by splitting the data into separate training and testing datasets.

• Search and drag Split Data onto the canvas and connect to the last Select Columns in Dataset.
• Click Split Data and in the Properties pane to the right of the canvas and set it to 0.75. In this way, we will use 75% of the data to train the model and 25% of the data for testing.

In the next step we will be applying Two Class Decision Forest machine learning algorithm which is most suitable for our Predictive Water Irrigation Experiment.

• Expand the Machine Learning category in the module palette on the left side of the canvas
• Expand Initialize Model. This shows several types of modules that can be used to initialize machine learning algorithms.
• For this experiment, we are selecting the Two Class Decision Forest under the Classification and drag it to the experimental canvas.

So, in the next step we will find and drag the Train Model module to the experiment canvas.

• Connect the output of the Two Class Decision Forest module to the left input of the Train Model module
• Connect the training data output (left port) of the Split Data module to the right input of the Train Model module.

Click the Train Model module, click Launch column selector in the Properties pane, and then select the waterUsage column. waterUsage is the value that our model is going to predict.

Now let’s Run the experiment so, we can have a trained model that can be used to score new Irrigation data to make Water Usage predictions.

Now that we have trained the model with 75% of the data, we can use it to score the other 25% of the data to understand the function of our model.

• Search and drag the Score Model module to the experiment canvas.
• Connect the output of the Train Model module to the left input port of Score Model.
• Connect the test data output (right port) of the Split Data module to the right input port of Score Model.

Let’s Run the experiment and view the output of the Score Model module by clicking on the output port of the Score Model, then select Visualize. The output shows the predicted value of the price and the known value in the test data.

Now as we are on the final stage to test the quality of the results.  We will select the Evaluate Model module and drag it to the experimental canvas, then connect the output of the Score Model module to the left input of the Evaluate Model. The final experiment should look like this:

In the next step we will be Running our experiment to Visualize the output of our Evaluate Model.

In addition, we will be using Cross-Validate Model which is an important technique commonly used in machine learning to assess the variability of data sets and the reliability of any model trained using that data.

• Search and drag Cross-Validate Model and in the Properties pane to the right page, click Launch column selector and select the following label column:

Now Run the experiment to See the results for a description of the report.

I hope you found this blog post helpful. If you have any questions, please feel free to contact me muhammad.ahmad@kaispe.com

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This year marks a huge milestone for KAISPE. Throughout this period, we have been really committed to learn the technology advancements and implement them to serve the customers. Our accomplishments and successes over the past year would not be possible without the competency and commitment of our phenomenal team.

For more information on KAISPE, please visit https://testing.kaispe.com

About KAISPE

With proven track record of delivering solutions to enterprise customers using Microsoft IoT platform to track, control and maintain their key business assets, we serve industries including Manufacturing, Logistics and Professional Services. Our solutions address end to end business scenarios for customers whether it is integrating devices with line of business applications like Microsoft Dynamics AX/CRM to automate the business process or having better insights into device data using Microsoft Power BI.

To learn more, visit https://testing.kaispe.com or call (315) 791-4472. Connect with us on Twitter, LinkedIn, and Facebook.

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First of all, we need to install the Visual Studio Code and Node JS to set up the Lab Environment. Then use the Azure subscription we have so we can create the Azure side of IoT components.

Afterward, we create a Resource Group and Azure IoT Hub service using Azure Portal.

Ok, now its time to set up the hardware and register it with IoT hub. For this post, we will be using Raspberry Pi 3. We need to install the OS Raspbian Jessie on our Raspberry Pi device.

To configure the connection string, I have used Visual Studio Code and make sure that my VS code uses the bash on Ubuntu (on Windows) shell in Integrated Terminal Panel. Below is the code snippet to get Raspberry Pi connected with IoT Hub:

After we build and run the code, we must be able to send messages from Raspberry Pi to Azure IoT Hub as you can see below:

Awesome! Now as you can see the IoT Hub shows messages and a connected device, which in this case is our Raspberry Pi.

Now let’s connect a Photocell Sensor with our IoT device, so we can get some real data from the device. For this scenario, we are using the Light Dependent Resistor (LDR).

We will give different manual inputs to the sensor, so we can have brightness data to analyze.

Finally, we get reading from the photocell sensor to the cloud. However, we receive continuous data in the cloud as the sensor keeps emitting brightness readings and we cannot just use it as is, so how to handle it? We will use Stream Analytics that makes it easier to set up real-time data computation.

In order to configure Stream Analytics, we need to configure Input, Output, and Query to get the required data. We will also create a Service bus queue for data storage and perform Stream Analytics on it.

Stream Analytics Input:

Events from a data source are called Input. So, we will create a Stream Analytics Job in which we can use one of the following sources as an input for the Job.

• Blob Storage
• IoT Hub
• Event Hub

In our Scenario, we are using IoT Hub. IoT Hub is used to collect data from connected devices and provide two-way communication between Cloud to Device and Device to Cloud.

Stream Analytics Output:

Stream Analytics job takes incoming data and converts it to a stream of Data Output events. The processed data can be used on real time dashboard, to generate alerts or for batch processing.

Now let’s create a Stream Analytics job. We can use one of the following sources as Output for the job.

• Azure Data Lake Store
• Azure SQL Database
• Blob Storage
• Event Hub
• Table Storage
• Service Bus Queue
• Service Bus Topic
• Azure Cosmos DB
• etc.

In our Scenario, we will use Service Bus Queue. It offers FIFO (First In First Out) method and scalable message structure.

Stream Analytics Query:

Now the final part of our Stream Analytics Job is Query where we will specify the logic of our Stream Analytics job. The good thing is Stream Analytics Query syntax is very similar to the Structured Query Language (SQL), so you can easily write the queries here.

Our Stream Analytics query is:

SELECT

    brightness

INTO

    [ServiceBusQueue]

FROM

    [Hub]

WHERE

    brightness > 5

If we now run the Stream Analytics job, it will only show the data where brightness is greater than 5 and filter out the rest of the output messages.

So, as you can see above, it shows 80 input events and 44 output events because of the filtering we have done using Stream Analytics query.

Next is we want to visualize the data using some kind of BI dashboard, so we can have better insights. For this purpose, let’s use Microsoft Power BI.

Following are the steps to create a Power BI dashboard:

• Create a dashboard using Power BI desktop application
• Add a Consumer Group to our IoT Hub. The consumer group is used by all applications to read data from Azure IoT hub
• Use the same Stream Analytics Job as we created above and have Power BI as the Output of job
• Once the above steps are completed, we can visualize Real-Time data into our dashboard

I hope you have found this blog post helpful. For any queries, please feel free to contact me farrukh.ahmed@kaispe.com.

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A quick snapshot of how the businesses will be transformed in next couple of years using IoT.

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https://academy.microsoft.com/en-us/professional-program/tracks/internet-of-things/

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