Connecting Raspberry Pi with Sense HAT to Azure IoT Hub Using Node-RED

Following up with A Simulated IoT Device with Node-RED, I replaced the simulated device with a Raspberry Pi with Sense HAT.

Node-RED Flow

The flow is much similar to that of a simulated device with as shown below,

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where

  • I initialize temperature, humidity and pressure as global variables and each is set to 0.
  • In the sensehat function, load the ambient data from Sense HAT, use mathjs to round the output to 2 decimal points and update the global variables with the rounded values before sending the data to Azure IoT Hub. Here I include the device connection string in the payload.
  • For the dashboard, the gauges and the charts are reading the data from the global variables. I noticed the Sense HAT temperature reading is generally about 10 degree Celsius higher than my room temperature.

Dashboard

Other than the ranges of data and minor cosmetic changes, the settings are the same with what I used with a simulated device. Here’s a snapshot.

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Next Step: Azure IoT Edge

Introduce Azure IoT Edge in opaque mode and connect the Raspberry Pi as an isolated device from Azure IoT Hub. Should be interesting. Stay tuned.

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A Simulated IoT device with Node-RED

In the last few months, I have gradually shifted to use Node-RED as the tool for demonstrating and prototyping Azure IoT solutions. In particular, I configure a dashboard to display the ambient information sent from the device and verify the data received by an Azure IoT Hub and stored in an Azure storage account using Azure Storage Explorer form my desktop. Ideally, I would configure all on an Arduino or a Raspberry Pi. To make it more portable, I also do it with a local Ubuntu VM, so no need to plug in anything and I can demo a simple IoT setup anytime and anywhere on demand with Internet connectivity. Briefly, here’s an outline of what I did.

1. Installing & Starting Node-RED

On my Ubuntu (16.04 LTS) VM, update and upgrade everything, followed by install Node-Red.

If you need to make a required node module globally available in Node_RED, edit the file, ~/.node-red/settings.js accordingly. Here, I made the module, math.js, globally available and used it to round the ambient data to two decimal points.

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Now, start Node-RED, as the below.

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As activities being carried out in Node-RED, this session displays the log with diagnostics in real-time.

In Ubuntu, when close out the terminal session running Node-RED, somehow it also stops the Node-RED service. This is different than how it behaves in Raspberry Pi where closing a Node-RED terminal session will not stop the service.

2. Accessing Node-RED IDE

The default port for Node-RED IDE is 1880, as shown below accessing the service from localhost. If preferred, authentication can be enabled and port changed by following what is stated in documentation and the above-mentioned settings.js file.

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By default, there are a number of nodes installed as shown on the left panel. And you may install addition nodes to better fit the needs.

3. Install additional Nodes

There are ample node and flow examples in Node-RED website which you may leverage. In addition to installing these node modules with a command line interface, doing it interactively is also an option. In the IDE, click the upper right waffle within the Node-RED session and click ‘Manage palette’. If you do not see the option, update npm to the latest should make this option appear. As shown below, the Nodes tab presents the nodes installable directly or already installed currently. The

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and on the install tab, you may keyword-search the Node-RED repository for relevant modules. Below, I search the modules relevant to Azure.

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A few modules, I frequently install including:

4. Develop & Deploy a Node-RED Process Flow

To create a flow, start dragging selected nodes from the left panel to the canvas and construct flows by connecting the nodes. There is a copious amount of contents with how-to instructions on Node-RED in Internet already. Or if you like to do it in an old-fashioned way, like me, by reading the document. The following flow is for a simulated device to send ambient data to an Azure IoT Hub called thisiothub, while displaying the data on the local node-red dashboard.

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Global Variables

Here, I added a config node to set the global variable to set the baseline temperature, humidity and pressure for a simulation run. Node-RED will always initialize a config node prior to executing all flows presented on the canvas.

Timestamp

The timestamp node sets the time interval for sending data. When developing and troubleshooting, I set it to a long period between messages to minimize the noises. When demoing, I will then set it based on a customer’s requirements. Each time, the timestamp triggers, the connected nodes are consequently executing the programmed the logic, respectively.

The Functions

In this setting, each emission by the timestamp node has the following effects.

  • This IoT Device function prepares the message payload and updates current ambient data which are global variables.
  • The temperature, humidity and pressure functions pipe the data stored in the global variables to a configured Node-RED dashboard.

This IoT Hub

This node has the host name of a target Azure IoT hub, here thisiothub, and the device connection information is provided in the function, This IoT Device.

msg.payload

This is a debug node. Once dragged to the canvas, it will automatically rename itself to msg.payload. Once connected, this node becomes a standardout of Node-RED. And you can examine the output in the debug tab in the right panel. In the screen capture above, you will find that I rounded the data to two decimal points and send it with mqtt.

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Gauges and Charts

A main reason motivating me to use Node-RED is the simplicity to configure and deploy a dashboard directly on an IoT device. An IoT solution is really about data and data visualization plays a critical part. The ability to deploy a dashboard right there and when and on demand is a significant time saver and a noticeable advantage. It did however took me some practices to correctly place those gauges and charts the way I wanted. Once configured, the dashboard is published automatically at http://node-red-instance/ui and here is what I got.

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Verifying the Data Sent to Azure IoT Hub

There are two tools I use for managing and examining the activities between an IoT device and Azure IoT Hub. They are iothub-explorer for Linux and Device Explorer for Windows. The latter is a great tool for Windows users to examine the data received by an IoT Hub.

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And Device Explorer also provides a convenient way to verify device properties, acquire the connection string or change the state of a device as shown below.

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I also deployed a sample web app which plots the temperature and the humidity data received from thisiotdevice in real-time. Here’s a snapshot.

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So either from Azure IoT Hub using a web app or directly on the device with a Node-RED dashboard, we may present the data visually.

Some Gotcha

Ubuntu frequently stopped Node-RED when a deployment had failed to connect to Azure IoT Hub, and the node will also lose the configured hostname data. And I had to frequently restart the services and re-enter the Azure IoT Hub hostname in the node configuration. Therefore it is better to leave the terminal session where you started the Node-RED service visible at all time to have a clear indication of the state. I once spent hours troubleshooting a flow, researching material and was not able to figure out why, and only to later find out the Node-RED service exited its session upon a failed deployment behind the scene.

Closing Thoughts

Node-RED is a great learning and prototyping tool. And once learned, you can create process logic based on data flows relatively easily. It is visual and a picture is always worth a thousand words

Azure IoT Hub is the Swiss army knife for formulating an IoT solution. It does the heavy lifting for registering, securing and managing devices with interfaces to integrate other Azure or 3rd-party services. The recent announcement of Azure IoT Edge opens up many scenarios and opportunities to increase ROI by processing data right where they are collected. Which is what I plan to include to the next version of my Node-RED flow. Stay tuned.