Open Digital Twins at the Edge
Summary of Chapter 4 of the State of the Edge Report 2026 by Simon Bisson, Technology Columnist and Journalist.
This blog is part of a series highlighting key insights from the State of the Edge Report 2026. Each post explores one chapter from the report and provides a preview of the latest thinking from industry experts. To dive deeper into the trends, architectures, and recommendations shaping the future of edge computing, download the full report.
As edge deployments become larger and more intelligent, organizations need more than real-time data—they need a way to understand, predict, and optimize how physical systems behave. Chapter 4 of the State of the Edge Report 2026 explores how open digital twins combine edge devices, cloud-native technologies, and AI to create living models of physical systems that support monitoring, simulation, and predictive decision-making.
Digital Twins Bring the Physical and Digital Worlds Together
A digital twin is a virtual representation of a physical device, system, or environment that continuously updates using data collected from edge sensors.
By combining telemetry with simulations, digital twins help organizations monitor current conditions, predict future behavior, and test changes before applying them in the real world. While the concept dates back to NASA’s Apollo program, today’s digital twins are used across industries—from smart homes and manufacturing to aviation, renewable energy, and logistics.
As the chapter explains, digital twins are becoming a fundamental part of modern control systems, allowing organizations to move from simply reacting to events toward predicting and preventing them.
Open Technologies Make Digital Twins Scalable
Building digital twins at scale depends on an open ecosystem of tools, standards, and cloud-native platforms.
The chapter highlights technologies such as MQTT, CloudEvents, Eclipse Ditto, Kubernetes, PostgreSQL, Apache Parquet, and the W3C Web of Things standards as key building blocks for creating interoperable digital twin platforms. Rather than relying on proprietary infrastructure alone, these open technologies provide common ways to connect devices, exchange data, manage digital models, and scale deployments from small prototypes to enterprise-wide systems.
Common standards also simplify device management, making it easier to deploy updates, manage fleets of edge devices, and integrate new hardware as systems evolve.
Event-Driven Data and AI Improve Decision Making
Digital twins depend on timely, reliable data.
Instead of streaming every piece of sensor information, modern digital twin platforms use event-driven architectures to route relevant information where it’s needed. Publish-and-subscribe messaging frameworks, open event formats, and scalable data lakehouse architectures allow organizations to efficiently process both real-time events and historical data for simulations and analysis.
Machine learning further strengthens these capabilities by identifying anomalies, predicting equipment failures, and improving simulations over time. Running lightweight AI models directly on edge devices allows many decisions to happen locally, reducing latency while keeping centralized systems informed.
Building Intelligent Systems with Digital Twins
The chapter introduces Eclipse Ditto as an example of an open platform for managing digital twins. By creating standardized digital representations of physical devices, organizations can separate hardware from application logic, making systems easier to maintain, update, and scale. Combined with cloud-native orchestration tools like Kubernetes and projects such as Akri, digital twins become part of a flexible distributed system that can manage thousands of connected devices.
The chapter also emphasizes that effective digital twins go beyond device telemetry. External data sources—including weather forecasts, geospatial information, satellite imagery, and environmental data—provide additional context that improves predictions and supports applications such as precision agriculture, biodiversity monitoring, industrial automation, and renewable energy management.
Key Takeaway
Digital twins are evolving into a foundational technology for intelligent edge computing.
By combining edge sensors, open standards, event-driven architectures, AI, and cloud-native infrastructure, organizations can build systems that continuously monitor, simulate, and optimize real-world operations. As digital twins grow from individual devices to entire environments, they provide a scalable framework for creating more adaptive, resilient, and autonomous edge applications.
Read the Full Report
This chapter is just one part of the State of the Edge Report 2026, which brings together insights from industry leaders on the technologies, architectures, and trends shaping the future of edge computing.
Download the full report to explore all nine chapters and learn how organizations are building the next generation of edge infrastructure.
