Industrial IoT for Architects and Engineers: Architecting secure, robust, and scalable industrial IoT solutions with AWS
- Length: 344 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2023-01-20
- ISBN-10: 180324089X
- ISBN-13: 9781803240893
- Sales Rank: #5720784 (See Top 100 Books)
Go beyond connecting services to understand the unique challenges encountered in industrial environments by building Industrial IoT architectures using AWS
Purchase of the print or kindle book includes a free eBook in the PDF format
Key Features
- Understand the key components of IoT Architecture and how it applies to Industry 4.0
- Walk through extensive examples and solutions across multiple Industries
- Learn how to collect, process, store, and analyse Industrial IoT data
Book Description
When it comes to using the core and managed services available on AWS for making decisions about architectural environments for an enterprise, there are as many challenges as there are advantages. This Industrial IoT book follows the journey of data from the shop floor to the boardroom, identifying goals and aiding in strong architectural decision-making.
You’ll begin from the ground up, analyzing environment needs and understanding what is required from the captured data, applying industry standards and conventions throughout the process. This will help you realize why digital integration is crucial and how to approach an Industrial IoT project from a holistic perspective. As you advance, you’ll delve into the operational technology realm and consider integration patterns with common industrial protocols for data gathering and analysis with direct connectivity to data through sensors or systems. The book will equip you with the essentials for designing industrial IoT architectures while also covering intelligence at the edge and creating a greater awareness of the role of machine learning and artificial intelligence in overcoming architectural challenges.
By the end of this book, you’ll be ready to apply IoT directly to the industry while adapting the concepts covered to implement AWS IoT technologies.
What you will learn
- Discover Industrial IoT best practices and conventions
- Understand how to get started with edge computing
- Define and build IoT solution architectures from scratch
- Use AWS as the core of your solution platform
- Apply advanced analytics and machine learning to your data
- Deploy edge processing to react in near real time to events within your environment
Who this book is for
This book is for architects, engineers, developers, and technical professionals interested in building an edge and cloud-based Internet of Things ecosystem with a focus on industry solutions. Since the focus of this book is specifically on IoT, a solid understanding of core IoT technologies and how they work is necessary to get started. If you are someone with no hands-on experience, but are familiar with the subject, you’ll find the use cases useful to learn how architectural decisions are made.
Cover Title Page Copyright and Credits Contributors About the reviewer Table of Contents Preface Part 1: An Introduction to Industrial IoT and Moving Toward Industry 4.0 Chapter 1: Welcome to the IoT Revolution Technical requirements Industry 4.0 and the digitalization of industry A very brief history lesson The first Industrial Revolution The second Industrial Revolution The third Industrial Revolution Moving forward and the fourth Industrial Revolution How IoT can support Industry 4.0 at scale Sensor technology IT versus OT Industry 4.0 and organizational alignment You can get there from here Visibility is everything Business driving innovation The convergence – IT, OT, and management working together Reactive and preventative maintenance Condition-based maintenance Predictive maintenance Approach with caution Leveraging good architecture to drive progress Observability Repeatability lowers cost Summary Chapter 2: Anatomy of an IoT Architecture Technical requirements Architectural thinking Reference models Architectural components for IoT Designing in layers Defining the layers Bringing it all together Defining standards Basic project review recommendations Setting expectations Summary Chapter 3: In-Situ Environmental Monitoring Manufacturing in the bigger context What do we mean by environmental monitoring? Why is monitoring important? Integration patterns and protocols Getting data from the field Data drives everything – a common use case OEE for a legacy plastic manufacturer Adventures of a humble data packet Wired versus wireless Wireless network evaluation What network should you choose? Wireless gateways versus Intelligent edge devices Summary Chapter 4: Real-World Environmental Monitoring Technical requirements Exploring wireless networks and protocols Standardizing your approach Data capture architecture Understanding gateway and network setup Setting up a TTN IoT gateway Setting up Milesight UG65 Configuring and deploying sensors Connecting the AM319 multi-sensor Collecting data in the cloud Cloud data ingestion Data transformation and storage layer Setting up a basic dashboard Putting the layers together Summary Part 2: IoT Integration for Industrial Protocols and Systems Chapter 5: OT and Industrial Control Systems Technical requirements Revisiting the basics of manufacturing Understanding industrial control systems Exploring PLCs History and evolution of PLCs Deconstructing PLCs PLC programming PLCs versus microcontrollers The industrial communication of the future PROFINET and OP CUA Single Pair Ethernet Advanced Physical Layer Time-Sensitive Networking Industrial 5G Mapping your integration strategy Differentiating OT cybersecurity from IT Where IT meets OT in industrial systems Why do we need convergence? What drives IT/OT convergence? Revisiting the DIKW model Summary Chapter 6: Enabling Industrial IoT Technical requirements The opportunity of Industry 4.0 Integration complexity Understanding IT and OT convergence The emergence of the digital twin Architecture decisions for enablement AWS services for IoT Revisiting cloud data ingestion Data collection options Intelligence at the edge with AWS IoT GG Adopting technical innovations Which industries are leading the way? Summary Chapter 7: PLC Data Acquisition and Analysis Technical requirements PLC hardware and architecture Operating PLCs PLC programming – ladder diagrams Practical application of PLCs – a smart mocktail bar Data integration and mapping Data ingestion and data analysis Summary Chapter 8: Asset and Condition Monitoring Technical requirements Understanding APM and system monitoring What is APM? APM at scale Investigating data processing architecture Simulating data for processing Processing data with IoT Analytics Entering the channel Managing the data pipeline Cataloging and querying curated messages Making meaningful decisions with your data Summary Part 3: Building Scalable, Robust, and Secure Solutions Chapter 9: Taking It Up a Notch – Scalable, Robust, and Secure Architectures Technical requirements Understanding IIoT architectural principles Exploring IIoT reference architectures and how to use them The IIRA RAMI 4.0 Getting to know the AAS Getting to know the DSB System considerations for architectural design AWS reference architecture for IIoT Leveraging architecture for AI and ML Edge and cloud reference architecture for ML Common issues with AI for Industry 4.0 Summary Chapter 10: Intelligent Systems at the Edge Technical requirements Edge computing and AWS Greengrass Technology options for your edge approach Greengrass to the rescue Setting up our scenario Modbus data Greengrass permissions Communicating between Greengrass components Modbus Request component Component deployments The next component – ModbusTCP Sending data to the cloud Summary Chapter 11: Remote Monitoring Challenges Technical requirements Why do we need remote monitoring? Long-range data acquisition challenges Case study 1: remote monitoring of a solar farm The heavy edge configuration Central cloud-based monitoring platform Data communication Data analytics Concluding remarks AWS reference architecture for solar farm remote monitoring Architecting for sustainability Case study 2: remote monitoring of a CCS unit Summary Chapter 12: Advanced Analytics and Machine Learning Technical requirements ML and industrial IoT Interference versus rules Learning from the data Engineering your data Retrieving voltage and current data Decoding the values Building the model Transforming training data Setting up and training our model Deploying and testing the model Real-time inference Summary Appendix OT security strategies OT cybersecurity best practices Comprehensive security framework Orchestrating a robust security program OT cybersecurity areas of concern Summary Index Other Books You May Enjoy
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