Chapter 1, Introduction to Industrial IoT, provides some background to the industrial IoT, the story, use cases, and the contrast with the home internet of things.
Chapter 2, Understanding the Industrial Process and Devices, defines the factory processes. This chapter describes the concept of distributed control system (DCS), programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA), Historian, manufacturing execution system (MES), enterprise resources planning (ERP), and fieldbus. It introduces the International Electrotechnical Commission (IEC)-61131 and the CIM pyramid. Finally, it designs a big picture, from equipment through to the cloud.
Chapter 3, Industrial Data Flow and Devices, details which equipment, devices, network protocols, and software layers managing the industrial IoT data flow along its path, from the sensors in the factory floor to the edge that is the external boundary of the industrial IoT data flow inside the factory.
Chapter 4, Implementing the Industrial IoT Data Flow, explains how to implement the industrial IoT data flow in a complex industrial plant. This journey starts with an understanding of how to select the industrial data source to connect to for the purpose of gathering the data and ends providing five network scenarios for edge deployment in industrial plants.
Chapter 5, Applying Cybersecurity, explores the industrial IoT data flow from the cybersecurity perspective, outlining the goals of the DiD strategy, and the most common network architecture to secure the industrial control systems, including the five network scenarios for edge deployment discussed in the previous chapter.
Chapter 6, Performing an Exercise Based on Industrial Protocols and Standards, discovers how to implement a basic data flow from the edge to the cloud by means of OPC UA and Node-RED.
Chapter 7, Developing Industrial IoT and Architecture, outlines the basic concepts regarding industrial IoT data processing, providing the key principles for storing time series data, handling the asset data model, processing the data with analytics, and building digital twins.
Chapter 8, Implementing a Custom Industrial IoT Platform, shows how to implement a custom platform leveraging on the most popular open source technologies: Apache Kafka, Node.js, Docker, Cassandra, KairosDB, Neo4J, Apache Airflow, Mosquitto, and Docker.
Chapter 9, Understanding Industrial OEM Platforms, explores the most common industrial IoT platforms developed by OEM vendors, from Siemens to BOSCH to General Electric.
Chapter 10, Implementing a Cloud Industrial IoT Solution with AWS, explores the solutions proposed by Amazon Web Services (AWS) and the capabilities of the AWS IoT platform. This chapter introduces the Edge IoT of AWS (Greengrass), the IoT Core, DynamoDB, AWS analytics, and QuickSight, for the purpose of showing data. We will learn these technologies by performing a practical exercise.
Chapter 11, Implementing a Cloud Industrial IoT Solution with Google Cloud, explores the solutions proposed by the Google Cloud Platform (GCP) and the capabilities of the GCP IoT platform, the GCP Bigtable, and GCP analytics.
Chapter 12, Performing a Practical Industrial IoT Solution with Azure, develops a wing-to-wing industrial IoT solution leveraging on Azure, Azure Edge, and the Azure IoT platform.
Chapter 13, Understanding Diagnostics, Maintenance, and Predictive Analytics, introduces the reader to the basic concepts of analytics and data consumption. It also develops basic analytics for anomaly detection and prediction.
Chapter 14, Implementing a Digital Twin – Advanced Analytics, develops a physics-based and data-driven digital equipment model to monitor assets and systems.
Chapter 15, Deploying Analytics on an IoT Platform, shows how to develop maintenance and predictive analytics on Azure ML and AWS SageMaker. Finally, the chapter explores other common technologies.
