Digital twins for IoT: what they are and how they are used
In short: A digital twin is a software model of a real asset, system or process that is kept up to date with live IoT data. Unlike a static simulation, it mirrors the current state and can be used to monitor, predict and test changes safely. Because twins hold detailed operational data and may send commands, they need strong protection.
What a digital twin is
A digital twin is a virtual model of something physical, such as a wind turbine, a building or a production line, that is continuously fed with data from that thing. The twin knows not only what the asset is (its design and parts) but what it is doing right now. You can query it, visualise it in 3D, or run "what if" simulations against it without touching the real equipment.
Twin, shadow, dashboard or simulation?
| Concept | What it holds | Live data? |
|---|---|---|
| Dashboard | Charts of current and past readings | Yes, but no model of behaviour |
| Device shadow | Last reported and desired state of one device | Yes, simple key-value state |
| Simulation | A model of how something behaves | No, uses assumed inputs |
| Digital twin | Model plus live state, history and relationships | Yes, and it can simulate |
Where twins are used
- Manufacturing: test a new production schedule on the twin of a line before changing the real one.
- Predictive maintenance: compare a pump's live vibration with its modelled normal behaviour to predict failure.
- Smart buildings: model heating, occupancy and energy to tune settings and plan refurbishments.
- Utilities and cities: model water networks or traffic to plan for demand or emergencies.
Twins range from simple (a data model with live values) to sophisticated (physics-based simulation plus machine learning). Standards bodies and industry groups are working on common twin definitions, but in practice most twins are built on a vendor platform or custom software.
Security of digital twins
A twin is a concentrated, well-organised picture of your operations, which makes it valuable to attackers for reconnaissance. Some twins also send settings back to the real asset, so a compromised twin could cause physical effects.
- Classify twin data at the same level as the systems it mirrors.
- Restrict who can view, edit the model, or push changes back to assets; use MFA and role-based access.
- Require human approval, or strict limits, before twin-generated changes reach real equipment.
- Validate incoming data so false readings cannot poison the model.
- Log all changes to the model and to assets, and review them.
Tip: a twin is only as good as its data. If sensors drift or are spoofed, the twin will confidently show a false picture. Build in data quality checks.
Practice
How does a digital twin differ from a traditional simulation?
Give one use of digital twins in maintenance.
Why does a digital twin need access control on 'write' actions?
Key takeaways
- A digital twin is a live, data-fed model of a physical asset or process.
- It combines state, history, relationships and simulation.
- Used for manufacturing, predictive maintenance, buildings and utilities.
- Twins reveal detailed operations and may control assets, so protect them.
- Poor or spoofed data makes a twin misleading.
Original content written by Mohamed Fayas for FM TecHub. © 2026 FM TecHub. You're welcome to share a link to this page; please don't copy the text elsewhere without permission.