IoT analytics using predictive models and integration with edge devices to send commands based on prediction outcomes

Internet of Things (IoT) have evolved tremendously in all spheres of our lives like Industrial applications, Social interactions, Remote management of facilities and equipment to name a few. In general application areas, IoT data collected by Sensors can be used for monitoring as well as predicting the outcomes. If any deviation from the norm is detected, corrective action can be prescribed either manually or by an automated process. Such actions may come out of rule based anomaly detection or a Statistical Change point detection or a Predictive model that predicts a faulty condition ahead of time. This approach goes a long way in implementing Predictive maintenance which is more prudent approach than Scheduled Preventive maintenance which is periodic in nature.

The end to end process steps for applying Analytics on IoT data are listed below:

Collect IoT data from sensor
Change point detection using IoT Sensor data. Refer code pattern Detect change points in IoT sensor data for more details.
Predicting equipment failure using IoT Sensor data. Refer code pattern Predict equipment failure using IoT sensor data for more details.
Sending decisions based on Analytics insights to the edge for automated action

This IBM Code Pattern is a composite pattern that demostrates the building of complete IoT analytics solution. When you complete this code pattern, you will learn how to:

Send events from an edge device (we use Raspberry Pi for demonstration) to the Watson IoT Platform
Store the events in a DB2 database on IBM Cloud
Invoke a predictive model on Watson Studio for IoT events using the below code patterns:
- Predict equipment failure using IoT sensor data
- Orchestrate data science workflows using Node-RED
Send a command back to the edge (we use Raspberry Pi for demonstration) based on the outcome of the predictive model

This pattern uses a sample equipment sensors data. This data is sent to the Watson IoT platform and stored in a DB2 database. A predictive model is built using the data in the DB2 database. The predictive model then takes the sensor events from Watson IoT platform as input and returns the state of the equipment as Running or Failing. If the equipment is failing, then a shutdown command is sent back to the edge device which is a Raspberry Pi.

This pattern uses Node-RED at both device and cloud for building the solution:

Implementing device client on Raspberry Pi to send events to Watson IoT platform
Consuming events from Watson IoT platform on IBM Cloud and storing the events in a DB2 database
Invoke predictive model on Watson Studio and get a response back for an IoT event
Send a command back to the Raspberry Pi through the Watson IoT platform

Prerequisites:

Flow

The Raspberry Pi gets events from the sensors. In the absence of sensors, the sensor events are read from a file.
The Node-RED flows are invoked on the Raspberry Pi.
The sensor events are sent to the Watson IoT platform.
The Watson IoT platform receives the events and sends it to all subscribing applications.
The Node-RED flows on IBM Cloud are triggered. The sensor events are recieved and stored into a database.
The predictive model on Watson Studio is triggered. The outcome of the model execution is sent back to the Node-RED through websockets.
Based on the outcome, the Node-RED flow sends a command with the action to be taken to the edge device(Raspberry Pi) through the Watson IoT platform
The Node-RED flow on Raspberry Pi recieve the command

Included Components

IBM Cloud: IBM's innovative cloud computing platform or IBM Cloud (formerly Bluemix) combines
platform as a service (PaaS) with infrastructure as a service (IaaS) and includes a rich catalog of
cloud services that can be easily integrated with PaaS and IaaS to build business applications rapidly.
IBM Watson IoT Platform: IBM Watson™ IoT Platform for IBM Cloud gives you a versatile
toolkit that includes gateway devices, device management, and powerful application access. By using
Watson IoT Platform, you can collect connected device data and perform analytics on real-time data
from your organization.
IBM Watson Studio: Analyze data using Python, Jupyter Notebook
and RStudio in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.
DB2 Warehouse: IBM Db2 Warehouse on Cloud is a fully-managed, enterprise-class, cloud data warehouse service.

Featured Technologies

Analytics: Finding patterns in data to derive information.
Data Science: Systems and scientific methods to analyze structured and unstructured data in
order to extract knowledge and insights.
Internet of Things

Watch the Video

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Steps

Create IBM Cloud services and configure
Configure Raspberry Pi
Trigger the Node-RED flow on Raspberry Pi
Run the notebook
Analyze results

1. Create IBM Cloud services and configure

Internet of Things Platform

Click on Internet of Things Platform and create an instance of Internet of Things Platform.
Click on Launch to launch the Dashboard
Create a device type Equipment and device Sensors. Refer documentation and article.
Note down the device credentials. They cannot be retrieved later.

The device credentials will be used later to configure Node-RED.

Click on Apps on the menu.
Click on Generate API Key. Click Next.
Select the role as Data processor application.
Make a note of the API Key and Authentication Token. This will be needed in the Node-RED flow configuration in the subsequent steps.

DB2 Warehouse

Create a DB2 Warehouse instance.

Make a note of the service name. This needs to be bound to Node-RED that is created in the next step.

Click on Service Credentials. Click on New Credential. Click on View Credentials.

Make a note of the database credentials to be entered into Watson Studio notebook later.

Click on Manage
Click on Open to launch the Dashboard
Click on Explore.
Click on the schema starting with DASH.

Make a note of the schema name to be configured later on Watson Studio.

Click on New Table and create a table EQUIPMENT_DATA with the configuration as shown.

Node-RED on IBM Cloud

Create the Node-RED Starter application.
Choose an appropriate name for the Node-RED application - App name:.
Click on Create.
On the newly created Node-RED application page, click on Connections.
Click on Create Connection and select the DB2 Warehouse service that was created in the previous step. Click on Connect. This binds the DB2 Warehouse service to Node-RED.
Click on Getting Started.
Click on Visit App URL to launch the Node-RED editor once the application is in Running state.
On the Welcome to your new Node-RED instance on IBM Cloud screen, Click on Next.
On the Secure your Node-RED editor screen, enter a username and password to secure the Node-RED editor and click on Next.
On the Browse available IBM Cloud nodes screen, click on Next.
On the Finish the install screen, click on Finish.
Click on Go to your Node-RED flow editor.

Import the Node-RED flow

Clone this repo.
Navigate to the orchestrate_dsx_workflow.json.
Open the file with a text editor and copy the contents to Clipboard.
On the Node-RED flow editor, click the Menu and select Import -> Clipboard and paste the contents.

The imported Node-RED flow appears on the editor.

On the two DB2 nodes named EQUIPMENT_DATA. Select the DB2 Warehouse service.
Configure the two IoT nodes with the API Key and Authentication Token. Click on Edit icon shown in the image.
Enter the API Key and Authentication Token noted earlier.

Deploy the Node-RED flow by clicking on the `Deploy` button

Note the websocket URL

The websocket URL is ws://<NODERED_BASE_URL>/ws/orchestrate where the NODERED_BASE_URL is the marked portion of the URL in the above image.

Note:

An example websocket URL for a Node-RED app with name myApp is ws://myApp.mybluemix.net/ws/orchestrate, where myApp.mybluemix.net is the NODERED_BASE_URL.

The NODERED_BASE_URL may have additional region information i.e. eu-gb for the UK region. In this case NODERED_BASE_URL would be: myApp.eu-gb.mybluemix.net.

Watson Studio

Sign up for IBM's Watson Studio.
Create a project if necessary, provisioning an object storage service if required.
In the Assets tab, select the Create notebook option.
Select the From URL tab.
Enter a name for the notebook.
Optionally, enter a description for the notebook.
Enter this Notebook URL: https://github.com/IBM/iot-edge-predictive-models-dsx/blob/master/notebooks/watson_iotfailure_prediction_integrated.ipynb
Select the free runtime.
Click the Create button.
In Section 7. of the notebook, enter the websocket URL noted earlier.
In Section 4. of the notebook, enter the database credentials for DB2 Warehouse noted earlier.

2. Configure Raspberry Pi

Copy the data file to Raspberry Pi and start Node-RED

The data file can be found at the location - https://github.com/IBM/iot-edge-predictive-models-dsx/blob/master/data. Using ftp the file iot_sensor_dataset.csv is transferred to the Pi. The file is stored at the location /home/pi. After that Node-RED is started by running the command node-red.

Copy data file

Start Node-RED

Configure Node-RED on the Raspberry Pi

Navigate to the pi_flow.json.
Open the file with a text editor and copy the contents to Clipboard.
Access Node-RED using the IP address of the RaspberryPi as shown below.
On the Node-RED flow editor, click the Menu and select Import -> Clipboard and paste the contents.

The below flow will be imported into the Node-RED editor.

Click on the event node.

Configure the device credentials noted earlier.
Click on the all commands node. Select the credentials configured in the previous step.
Click on Deploy to deploy the Node-RED flow.

3. Trigger the Node-RED flow on Raspberry Pi

Click on the inject node Sensor event trigger. This will send sensor events to the Watson IoT Platform. These events will get stored in the DB2 Warehouse.

4. Run the notebook

Note: Only after the previous step (3. Trigger the Node-RED flow on Raspberry Pi) is complete and all events are stored into the DB2 Warehouse, run the cells in the notebook. The data in the DB2 Warehouse is then used for building the model.

When a notebook is executed, what is actually happening is that each code cell in the notebook is executed, in order, from top to bottom.

Each code cell is selectable and is preceded by a tag in the left margin. The tag format is In [x]:. Depending on the state of the notebook, the x can be:

A blank, this indicates that the cell has never been executed.
A number, this number represents the relative order this code step was executed.
A *, this indicates that the cell is currently executing.

There are several ways to execute the code cells in your notebook:

One cell at a time.
- Select the cell, and then press the Play button in the toolbar.
Batch mode, in sequential order.
- From the Cell menu bar, there are several options available. For example, you can Run All cells in your notebook, or you can Run All Below, that will start executing from the first cell under the currently selected cell, and then continue executing all cells that follow.
At a scheduled time.
- Press the Schedule button located in the top right section of your notebook panel. Here you can schedule your notebook to be executed once at some future time, or repeatedly at your specified interval.

For this Notebook, you can simply Run All cells. The websocket client will be started when you run the cell under 7. Start websocket client. This will start the communication between the UI and the Notebook.

5. Analyze results

Go to the Node-RED flow on the Raspberry Pi. Click on the inject node Event - Running. This sends an event with values indicating a good health to the Watson IoT Platform. Click on the inject node Event - Failing. This sends an event with values indicating a failing health to the Watson IoT Platform. A shutdown command is received from the Watson IoT platform after running of the predictive model.

6. Troubleshooting

See Debugging.md

7. License

This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 (DCO) and the Apache Software License, Version 2.

Apache Software License (ASL) FAQ

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