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Developing an IoT Application Powered with Analytics

Learn how to develop an Internet of Things (IoT) application on Oracle IoT Cloud Service that processes your sensor data, predicts future events from the historical sensor data, and persists the analyzed results

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advanced Advanced
duration 150 Min
modules 6 Modules

Learning Path Contents

Module Sections Topics and more

  • About the Learning Path
      duration 0 Min
    In this learning path, you create an IoT application with analytics components and analytics processors. You develop the analytics processors that analyze sensor data and predict results. You deploy the IoT application in Oracle IoT Cloud Service, trigger the processors and monitor their results.
    • This learning path is based on a use case derived from a scenario:


      Vision Heavy Machines manages and maintains heavy equipment like bulldozers, excavators, cranes, and loaders used by the construction companies. For each equipment, they would like to monitor the hourly average engine temperature. They want a supervisor to be alerted if the average engine temperature of the equipment exceeds a specific value within the last one hour. In addition, they would also like to predict the average engine temperature of an equipment for the next day. This prediction data will help them to manage and maintain their equipment effectively.

      Use Case

      Develop an IoT application powered with analytics on Oracle IoT Cloud Service. The IoT application fetches sensor data from an equipment, calculates the hourly average engine temperature, and sends an alert to Oracle IoT Cloud Service if this value exceeds the standard engine temperature of that equipment. The IoT application analyzes the historical sensor data and predicts the average engine temperature for the next day.

      To implement the use case, you perform four major tasks, and in each task you complete the steps sequentially:

      1. Create and configure an IoT application on Oracle IoT Cloud Service
        1. Create a device model (DM) that maps to the attributes of a heavy machine equipment.
        2. Create an IoT application and associate the device model with it.
        3. Create an analytics object (AO) for a NoSQL link that has the same attributes as that of the DM and two additional attributes, namely deviceId and eventTime.
        4. Create a device message link (DML), a NoSQL link and an Oracle Database as a Service (DBaaS) link. The standard engine temperature for the equipment are stored in a table in DBaaS.
      2. Develop and deploy a streaming analytics processor and a batch analytics processor that persists and processes sensor data respectively
        1. Create a streaming analytics processor that will persist the sensor data to a NoSQL link.
        2. Create an analyzed message link (AML) for raising alerts in IoT Cloud Service.
        3. Create a batch analytics processor (BAP) to calculate the hourly average engine temperature from the sensor data, and raise an alert if the hourly average engine temperature is greater than the standard engine temperature.
      3. Develop and deploy two batch analytics processors that perform training and scoring on the historical sensor data
        1. Create analytics objects and links for the two BAPs and obtain Oracle Storage Cloud Service (OSCS) container details.
        2. Develop a BAP in an IDE that does training and stores the training/learning model in the OSCS container.
        3. Develop another BAP that fetches the training model from the OSCS container, predicts the hourly average engine temperature for the next day, and then stores the predicted value in a table in Oracle NoSQL Database.
      4. Deploy and monitor the IoT Application powered with analytics
        1. Ensure that your IoT application has access to at-least two days of  historical sensor data.
        2. Deploy the IoT application, trigger the processors, observe the prediction data and monitor the alerts.
    • The following diagram illustrates a design of the use case. It displays the sequential steps in which the various components interact with each other to implement the use case.

  • Set Up
      duration 0 Min
    • Perform these steps to set up your environment:

      1. Get access to Oracle Cloud Services

      To complete the tutorials of this learning path, you need access to Oracle IoT Cloud Service, Oracle Storage Cloud Service, and Oracle Database as a Service. To request for access, you can do one of the following:

      2. Enable Analytics in your Oracle IoT Cloud Service instance

      Perform the steps listed in Creating an Oracle Internet of Things Cloud Service Instance with Analytics

  • Creating an IoT Application Powered with Analytics
      duration 25 Min
    In this module, you create the IoT application, configure the device model, and create the analytic components in the IoT application.
  • Developing Analytics Processors for Processing Sensor Data
      duration 60 Min
    In this module, you create a link to an external RDBMS , create and deploy a streaming analytics processor, integrate an IDE with Oracle IoT Cloud Service, develop and deploy a batch analytics processor in an IDE and then invoke the batch analytics processor.
  • Developing Batch Analytics Processors for Training and Scoring
      duration 40 Min
    In this module, you create and configure the analytics components required for the batch analytics processors, create and develop the batch analytics processors that apply linear regression algorithm for prediction.
  • Monitoring an IoT Application Powered with Analytics
      duration 25 Min
    In this module, you invoke the batch analytics processors, monitor the IoT application, and view the prediction results.