Applying AI and Edge prediction to IoT data
Implement an edge computing architecture using Node-RED, Watson IoT Platform, and Watson Studio
In this tutorial, I present the high-level steps from my hands-on workshop that shows you how to implement an edge computing architecture. You learn how to integrate sensors, wireless connectivity, a multiprocessor edge device, a low-power microcontroller, and sensor libraries into this IoT edge architecture. You learn how to connect your edge device to the IBM Cloud and to Watson IoT Platform to create a new Node-RED application. Using the Watson Studio data science portal, you learn how to create a machine learning model that will be deployed to your edge device. Most importantly, you learn how to run a machine learning prediction model on the edge device.
If this tutorial looks a bit daunting, perhaps you’d like to start with another workshop, “Configuring mesh networking for the IoT Edge.”
I hope that even if you cannot follow along in the steps in this workshop exactly that you can use them as a model for building your own IoT edge solution using similar hardware and software.
These steps are the main steps for this tutorial:
The journey follows this series of steps.
You can complete this tutorial using an IBM Cloud account:
Step 1. Set up the edge device
After setting up the development board and connecting the sensor expansion board, you’ll learn how to power up OpenSTLinux on the edge device, detect the wifi IP address, and connect to the edge device. Then, you’ll run the environmental sensor programs to observe that environmental sensor data. Lastly, after connecting your browser to Node-RED that is running on the edge device, you’ll send the sensor data to Watson IoT Quickstart.
Step 2. Create the Node-RED apps
Before you can start creating your Node-RED apps, you need to create an IoT starter application running in IBM Cloud, open Watson IoT Platform to confirm that you can send/receive data from your edge device, and register the edge device in Watson IoT Platform. Next, you’ll configure Node-RED, install additional nodes, and import a pre-built flow, and then you’ll create your first Node-RED app that outputs the sensor data.
Next, you’ll create your second Node-RED app – a Node-RED dashboard – in IBM Cloud, which lets you experiment with chart types and plot real-time sensor data. You’ll also trigger alerts when that sensor data exceeds a threshold value.
Finally, you’ll create your third Node-RED app that stores the sensor data in a Cloudant database, format the time series database record, read datasets from the Cloudant database, and create a chart of the historical data.
Step 3. Create, deploy, and run a machine learning model
In this final step, you’ll use Watson Studio to create a machine learning model. Next, you’ll use Node-RED in IBM Cloud to send the model to the Node-RED running on the edge device. Finally, you’ll run the model on the edge device against incoming sensor data in order to make a prediction as to if the sensor is being held.
Summary and next steps
With this tutorial, I tried to show how you can go from sensors to insights, from the edge to the cloud. If you’d like to try your hand at other edge computing solutions, check out the Edge Computing and IBM Edge Application Manager hubs.