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. What is the most important benefit of using Kafka instead of RabbitMQ or AMQP?
    1. Performance and scalability
    2. Routing and protocols
    3. Reliability
  2. What are the most important differences between cold-path analytics and hot-path analytics?
    1. Cold-path analytics use real-time processing
    2. In hot-path analytics, data is processed before storage
    3. In cold-path analytics, data is stored and sent to the hot path
  3. Why would you use a TSDB database rather than a SQL/NoSQL standard database to store time-series?
    1. Data-sharding
    2. It has a specific API to interpolate data and aggregate data
    3. Scalability and reliability