Book Image

Hands-On Industrial Internet of Things

By : Giacomo Veneri, Antonio Capasso
Book Image

Hands-On Industrial Internet of Things

By: Giacomo Veneri, Antonio Capasso

Overview of this book

We live in an era where advanced automation is used to achieve accurate results. To set up an automation environment, you need to first configure a network that can be accessed anywhere and by any device. This book is a practical guide that helps you discover the technologies and use cases for Industrial Internet of Things (IIOT). Hands-On Industrial Internet of Things takes you through the implementation of industrial processes and specialized control devices and protocols. You’ll study the process of identifying and connecting to different industrial data sources gathered from different sensors. Furthermore, you’ll be able to connect these sensors to cloud network, such as AWS IoT, Azure IoT, Google IoT, and OEM IoT platforms, and extract data from the cloud to your devices. As you progress through the chapters, you’ll gain hands-on experience in using open source Node-Red, Kafka, Cassandra, and Python. You will also learn how to develop streaming and batch-based Machine Learning algorithms. By the end of this book, you will have mastered the features of Industry 4.0 and be able to build stronger, faster, and more reliable IoT infrastructure in your Industry.
Table of Contents (18 chapters)

Azure IoT

Azure IoT is a platform proposed by Microsoft to connect multiple devices, enable telemetry, store measures, run and develop analytics, and visualize results. The key components of Azure IoT are the following:

  • IoT Hub
  • Stream Analytics
  • Azure Data Lake
  • Data Lake Analytics
  • Time Series Insights

The general purpose components of Azure are the following:

  • Machine Learning (ML) Analytics
  • Power BI

The following diagram shows the architecture proposed by Azure. Data is acquired through the Azure IoT Edge and sent to the Azure IoT Hub. Data can be processed with low latency Stream Analytics, stored in a time series database called Time Series Insight, or stored in Azure Data Lake. It can then processed by Azure ML Analytics or Data Lake Analytics. Finally, we can use Power BI for fast visualization of the data:

The Azure IoT architecture
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