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)

Platforms for digital twins

A digital twins platform is not that much different from the analytics platforms studied in the previous chapters. All it needs is sufficient storage support to save the current status of the model and to manage the life cycle of the digital twin. In other words, digital twins require the model to be tuned initially. They also require the real asset and the digital asset to be synchronized periodically:

The components of digital twins

The platforms that can support digital twins today include AWS, Predix, and Google Cloud Platform (GCP).

AWS

AWS recommends that we use SageMaker as the main platform for ML. With SageMaker, we can define our model to train parameters and hyperparameters. We can also...