Industrial IoT Training

a course outline for an Industrial IoT training program that covers OPC UA, MQTT, and AWS integration:

Industrial IoT Training Course Content

• Module 1: Introduction to Industrial IoT and Connectivity Protocols 1.1 Overview of Industrial Internet of Things (IIoT) and its applications 1.2 Importance of data connectivity in industrial environments 1.3 Introduction to OPC UA (Unified Architecture) and MQTT (Message Queuing Telemetry Transport)

  Module 2: Understanding OPC UA 2.1 Exploring the OPC Foundation and its standards 2.2 OPC UA architecture and components 2.3 OPC UA information modeling and addressing

  Module 3: OPC UA Communication 3.1 OPC UA data modeling and namespaces 3.2 OPC UA communication layers: client-server and publisher-subscriber 3.3 Securing OPC UA communications and certificates

  Module 4: MQTT Protocol for IIoT 4.1 Overview of MQTT and its role in IoT communication 4.2 MQTT architecture: publishers, brokers, and subscribers 4.3 Quality of Service (QoS) levels and message retention

  Module 5: Setting Up MQTT Infrastructure 5.1 Configuring MQTT brokers and clients 5.2 Publish and subscribe mechanisms using MQTT 5.3 Implementing topic structures for efficient data routing

  Module 6: Integrating OPC UA and MQTT 6.1 Benefits of using OPC UA and MQTT together 6.2 Bridging OPC UA data to MQTT and vice versa 6.3 Hands-on exercises: creating OPC UA to MQTT gateways

  Module 7: Cloud Computing and AWS IoT 7.1 Introduction to cloud computing in IIoT 7.2 Overview of Amazon Web Services (AWS) and AWS IoT 7.3 Setting up AWS IoT services for data ingestion and processing

  Module 8: Sending Data to AWS using MQTT 8.1 Establishing MQTT connections to AWS IoT Core 8.2 Device registration and secure communication in AWS IoT 8.3 Data ingestion and rule-based processing in AWS IoT

• Module 9: Data Storage and Analysis in AWS 9.1 Storing data in AWS services like Amazon S3 and Amazon DynamoDB 9.2 Real-time data analytics using Amazon Kinesis and AWS Lambda 9.3 Visualizing data using Amazon QuickSight

  Module 10: Security and Authentication 10.1 Ensuring end-to-end security in IIoT systems 10.2 Using mutual authentication and authorization mechanisms 10.3 Implementing security best practices for OPC UA, MQTT, and AWS

  Module 11: Edge Computing and IoT Gateways 11.1 Introduction to edge computing in IIoT 11.2 Role of IoT gateways in data preprocessing 11.3 Deploying edge devices and gateways in IIoT architecture

  Module 12: Hands-on IoT Implementation 12.1 Practical exercises: setting up an end-to-end IIoT system 12.2 Configuring OPC UA communication, MQTT messaging, and AWS integration 12.3 Troubleshooting common challenges in IIoT deployments

  Module 13: Advanced Topics and Future Trends 13.1 AI and machine learning integration in IIoT 13.2 Fog computing and hybrid cloud-edge architectures 13.3 Exploring emerging IIoT standards and technologies

  Module 14: IIoT Project Development 14.1 Collaborative project: students design and develop an IIoT solution 14.2 Presenting the project, demonstrating functionality, and discussing design decisions

  Module 15: Best Practices and Maintenance 15.1 Continuous monitoring and maintenance of IIoT systems 15.2 Data lifecycle management and data retention strategies 15.3 Adapting to evolving technologies and industry trends