Digital Twins in Action

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"Makes the theory tangible and the tools accessible, from a single sensor to a city-scale system."— Anthony Townsend, Cornell University

"Digital Twins in Action takes you from a five-dollar temperature sensor to an AI agent making autonomous decisions!"— Sanjay Basu, PhD, VP of Gen AI and GPU Infrastructure at Oracle

A digital twin is a virtual representation of a physical system that enables organizations to monitor, analyze, simulate, and optimize real-world assets without disrupting operations. From predictive maintenance and intelligent automation to smart factories and city-scale infrastructure, digital twins are transforming how businesses make data-driven decisions. Digital Twins in Action provides a practical, hands-on roadmap for building production-ready digital twins using modern software engineering, IoT, machine learning, visualization, and AI technologies.

Written by industry expert Greg Biegel, this comprehensive guide cuts through the hype surrounding digital twins and focuses on proven architectures, practical implementation techniques, and real-world engineering challenges. Whether you're a software developer, automation engineer, data engineer, or solutions architect, you'll learn how to design and deploy scalable digital twin platforms that bridge the physical and digital worlds.

Through step-by-step projects, you'll build a working digital twin suitable for an office or home environment while mastering key design concepts such as the Opportunity-to-Data Mapping Chain and the Minimum Viable Digital Representation (MVDR). These practical frameworks help ensure your digital twin delivers measurable business value instead of becoming another technology experiment.

The book demonstrates how to collect and integrate data from multiple sources, including IoT sensors, legacy documentation, databases, and cloud platforms. You'll learn how to deploy physical sensor networks using LoRaWAN and Wi-Fi, organize real-time messaging with standardized MQTT topic hierarchies, digitize engineering documents using OCR, and create a unified data pipeline capable of supporting intelligent decision-making.

Visualization is another essential component of successful digital twins. You'll discover how to model complex systems using semantic knowledge graphs and ontologies, create interactive dashboards with Grafana, and develop immersive 3D geospatial visualizations using CesiumJS and WebGL. These techniques enable both humans and intelligent systems to better understand complex environments.

Beyond visualization, the book explores advanced analytics and artificial intelligence techniques that make digital twins truly intelligent. You'll build supervised image classifiers that function as virtual sensors, detect anomalies using Isolation Forests, perform time-series forecasting with AutoGluon, and combine machine learning with continuous and discrete-event simulations to predict system behavior. You'll also learn how autonomous AI agents can reason, plan, and optimize physical operations while maintaining secure and reliable cyber-physical boundaries.

Security, scalability, and operational resilience receive equal attention. The book concludes by demonstrating how to deploy digital twins into production environments, monitor performance, protect critical infrastructure, and scale solutions confidently for enterprise applications.

What You'll Learn

• Understand the complete digital twin architecture from physical assets to intelligent software systems.
• Build practical digital twins using open-source and commercial technologies.
• Deploy hybrid IoT sensor networks using LoRaWAN and Wi-Fi.
• Collect, stream, and manage real-time data using MQTT.
• Integrate relational databases, Parquet files, Delta tables, and cloud data platforms.
• Create semantic models using ontologies and knowledge graphs with Memgraph.
• Build interactive dashboards using Grafana.
• Visualize digital twins in 3D with CesiumJS and WebGL.
• Apply machine learning for image classification, anomaly detection, and forecasting.
• Combine AI, simulations, and autonomous agents for intelligent decision-making.
• Secure cyber-physical systems for enterprise deployment.
• Evaluate projects using the Five-Level Digital Twin Maturity Model.

What's Inside

• Model physical environments using semantic knowledge graphs and ontologies.
• Deploy hybrid IoT sensor networks and stream real-time data.
• Integrate predictive machine learning, physics simulations, and AI agents.
• Learn the Five-Level Digital Twin Maturity Model.
• Build production-ready digital twin applications from the ground up.

Who Should Read This Book?

This book is ideal for:

• Software Developers
• Data Engineers
• Solutions Architects
• Automation Engineers
• IoT Developers
• Machine Learning Engineers
• Industry 4.0 Professionals
• Smart Manufacturing Engineers
• Industrial Automation Specialists
• Technical Consultants

Readers should have a basic understanding of machine learning. No prior experience with industrial automation or visual design is required.

Table of Contents

1. Bridging the Physical and Digital Worlds
2. Mapping Physical Systems to Digital Representations
3. Sensing the Real World
4. Data Integration and Management
5. Modeling Reality
6. 2D Visualization and Insight
7. Spatial Context and 3D Insight
8. Integrating Inference and Intelligence
9. Predicting Outcomes with Simulation
10. Digital Twins in Production

Appendices

• Building a LoRaWAN Network
• Building a Custom IoT Sensor
• Capturing a 3D Model Using Photogrammetry

About the Author

Greg Biegel has more than 20 years of experience designing and implementing production-scale digital twin systems. He spent six years developing a state-of-the-art industrial digital twin platform for one of Australia's leading energy producers and has extensive expertise in software engineering, industrial automation, IoT, artificial intelligence, and large-scale cyber-physical systems.

Whether you're developing smart factories, industrial automation systems, energy infrastructure, or next-generation AI-powered applications, Digital Twins in Action provides the practical knowledge, tools, and architectural guidance needed to build intelligent, scalable, and production-ready digital twin solutions.

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