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)

Questions

  1. Which of the following technologies is not a neural network technology?
    1. CNN
    2. RNN
    3. LSTNN
    4. LSTM
  2. Which of the following is the best definition of digital twins?
    1. A 3D model of a piece of equipment
    2. A digital copy of the design project of a piece of equipment
    3. A digital representation of a piece of equipment
  3. What is the difference between a physics-based and a data-driven model?
    1. A physics-based model is based on mathematics, while a data-driven model is based on statistics
    2. A physics-based model is based on design knowledge, while a data-driven model is driven by data
    3. A physics-based model is based on rules, while a data-driven model is based on machine learning or deep learning