Digital Twins and IoT: what you need to know

If you picture a finely tuned mirror of something in the physical world - whether that’s a machine, a device, a process or even an entire system - you’re on the right track with what a digital twin is.

As with many widely used terms, there’s more than one way to define it

Some people talk of a digital twin as simply a virtual representation of a physical asset where sensors feed data into a model from which you can monitor or visualize it. Others go further, viewing the twin as a live, continuous feedback-loop of physical to digital and back again, with simulation, prediction and optimization built in.

That flexibility in definition is part of its appeal (and occasional confusion). However we define them, digital twins are gaining in popularity.

Digital twins and IoT

For those working in the IoT space, the concept of a digital twin is particularly compelling.

At its heart, IoT is about connecting sensors, gathering data, and deriving insight. The digital twin builds on that foundation and adds a layer of meaning. It means we can move from reactive data (“What just happened?”) to insight (“What’s happening now?”) to foresight (“What’s likely to happen next?”). In some cases it allows us to go a step further still, and towards “what-if” scenario testing.

Imagine you’re deploying a fleet of connected devices.

With a digital twin, you can build a model of each unit, feed real-time sensor data into it, and compare actual performance against expected. If something that you are monitoring is straying from its normal pattern, the digital twin will not only alert you of the issue, but it would let you simulate what might happen if you delayed maintenance, or even explore what would happen under a different operating condition. This becomes a powerful way to add value through lower downtime, better performance, and earlier intervention.

For the IoT market more broadly, digital twins provide a bridge from “we are collecting data” to “we are acting on and optimising data”. They help tell the story to customers and stakeholders that the IoT device isn’t just live and reporting, but integrated into a richer digital environment through a model can provide insight about the future, not just the present

There are plenty of notable examples of digital twins deployed at the macro scale, for example, entire city-regions that have built digital models of their transport systems, utilities, and infrastructure.

But the digital twin concept isn’t reserved for big-budget, large-scale deployments. Thanks to the ubiquity of IoT, cloud and edge platforms (and open-source libraries), developers and smaller organisations can now build and deploy digital twins of single pieces of kit, small systems, or even prototypes of new devices.

That means you don’t need to wait for a multi-million-dollar infrastructure project to explore digital twins. You could spin up a simple virtual model of your device or subsystem, stream in its live sensor data, experiment with simulated behaviour under differing loads, or run “what-if” scenarios.

In doing so you are already practising the digital-twin mindset even if you don’t label it as such. Over time, as your system grows or as you build more complex interactions, the twin becomes a natural extension of your IoT architecture rather than a ‘plug in’ later in the project.

What’s next for digital twins?

The digital twin field is maturing.

Market-forecasts show strong growth with one recent report projecting the global digital twin market rising from $10.3 billion in 2023 to $140.9 billion by 2031.

Furthermore, it is expected that over 95% of IoT platforms will have some form of digital-twinning capability by 2029.

Several technological forces are accelerating this evolution:

• Standardisation and platforms: As more IoT platforms build twin-capabilities, integration becomes simpler and more accessible.
• Edge and cloud convergence: With sensors, edge computing and cloud analytics working together, real-time digital twin updates become feasible.
• Simulation, AI and machine learning: The twin is no longer just a passive mirror; it can simulate future states, apply machine learning to detect anomalies, and suggest optimisation strategies.
• Lower Entry Barriers: Tools, libraries, and platforms are lowering the time-to-value for smaller systems and pilots.
• Domain Expansion: Whereas initially digital twins were most prevalent in manufacturing and large assets, use cases are expanding into consumer goods, smaller systems, even personal or physiological twins.

What this means for developers

For the IoT developer, or organizations behind IoT systems, digital twins represent a strategic opportunity.

Starting early with a digital-twin approach can give you a vantage point whereby you’re building models and generating predictive insight from the outset. That capability can help deliver stronger and more diverse business value, and supports smart upscaling of your system over time. This only becomes more feasible as tools simplify and the underpinning platforms mature.

Digital twins can be a helpful reminder that these devices, sensors and platforms don’t just gather data – they are opportunities to simulate, anticipate and optimize.

And with a continuous, high-fidelity simulation of a real-world systems, it can place additional demand on data collection, processing, and synchronization.

As developers work with increasingly complex models, the hardware must support prolonged computation and communication cycles, which can increase power consumption.

That might mean you need batteries with greater capacity or power capabilities

Subscribe to our monthly newsletter