CAA2026- Low-Cost IoT Monitoring for Cultural Heritage
At CAA2026 I will be presenting a piece of work that sits at the intersection of archaeology, electronics, and data workflows: a low-cost, modular IoT system for cultural heritage monitoring.
This project is based on my final MSc project in Advanced Computing at the University of the West of Scotland, where I explored the potential use of low-cost, off-the-shelf components for monitoring heritage environments.
Why this matters
Cultural heritage sites are constantly under pressure.
Environmental changes, humidity fluctuations, visitor behaviour… all of these can have a real impact on preservation. At the same time, many monitoring systems are:
- expensive
- proprietary
- difficult to adapt
That creates a gap. Smaller institutions, research projects, or even individual researchers often cannot deploy these systems.
This work explores a simple idea:
what if we could build something useful with cheap, open, off-the-shelf components?
What I built
The project is a modular wireless sensor network designed around affordability and flexibility.
At a high level, the system has three parts:
- Sensor nodes (ESP32-based) collecting data
- A local hub (Raspberry Pi) doing edge processing
- A cloud backend for storage and visualisation
The sensors cover both environmental and behavioural signals:
- temperature and humidity
- motion and proximity
- sound
- camera-based event capture
The idea is not just logging data, but combining different signals to understand what is happening around an object or space.
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A quick example
Imagine a museum cabinet.
You can monitor:
- humidity changes over time
- whether someone is getting too close
- sudden movements or disturbances
- even simple behaviour patterns through images
All of this, using hardware that costs roughly £20–30 per node.
What worked (and what didn’t)
The prototype was tested in a simulated environment over several days.
Some key takeaways:
- The system was reliable across multiple sensors
- Image capture and transfer worked consistently
- Edge + cloud integration was practical
- Behaviour classification is possible, but depends heavily on the model
At the same time:
- This was a controlled indoor setup
- Power consumption is still a challenge
- WiFi dependency is limiting in some contexts
So, it works. But there is still a lot to do before real deployments.
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Why I think this is interesting
For me, this is less about the specific prototype and more about the direction.
This kind of approach opens up:
- preventive conservation workflows that are data-driven
- experimentation without large budgets
- custom systems tailored to specific sites
- more open and reproducible infrastructures
It moves us away from “black box” systems and towards something we can understand, modify, and share.
What’s next
The obvious next steps are:
- testing in real heritage environments
- improving power management
- exploring alternative communication (LoRa, etc.)
- refining behaviour detection
And probably the most important one:
making it easier for others to replicate and use
Final thoughts
This project is part of a broader line of work I am developing around:
- open hardware
- reproducible workflows
- accessible digital infrastructures in archaeology
If this works, even partially, it means that monitoring is no longer something limited to well-funded institutions.
And that, I think, is worth exploring.
If you are interested, please come and chat about it at CAA2026! Or if you have any questions, feel free to reach out in person or online. I am always happy to discuss the details, challenges, and potential of this kind of approach.