Digital twins – virtual representations of physical systems – are helping to make growing cities safe, efficient and livable. Whilst they open up many innovative solutions for things such as urban planning and clean energy supply, there are a number of challenges that need to be considered before their full potential can be met.
The use of digital twins is commonplace when trying to solve complex problems in the real world. For example, they can be used to test self-driving cars in different scenarios before allowing them onto the road. They’re also used by companies like Nomoko, for helping to visualise real estate projects.
There is potential for this technology to help solve even more complex problems though. In fact, the Luxembourg Institute of Science & Technology (LIST) is looking to create a digital twin of the whole country and turn it into a digital test bed. According to LIST CEO, Thomas Kallstenius, “it is small and flexible enough to become a fully-fledged living lab, digital testbed and policy sandbox.” They have already experimented with the use of digital twins when managing the COVID crisis.
Despite the benefits of this technology, there are a number of challenges to address.
Who owns the data?
A digital twin is an exact replica of an object or system in the physical world. Not just in one moment of time, but in real-time. That means it needs to reflect changes in the real world, so requires a steady stream of up-to-date data which might come from different sources. Who, then, owns the digital twin when many different people may have contributed to creating it?
With large scale digital twins, such as whole cities, the idea of data ownership becomes even more complex, as Nomoko CEO and Co-Founder, Nilson Kufus highlights. We, as private individuals, are contributing our data through public databases (often involuntarily), so do we own part of the model? And could we, therefore, profit from its use?
Who can access the data?
Following on from the ownership question, it’s important to know who can access this data. Depending on the project, commercially sensitive information might be shared to create the digital twin. In this case, it would be crucial that competitors could not access it. Likewise, private individuals might not like their data to be used by certain companies or industries. For public trust to be gained, all of this requires a way to enable those who contribute their data to choose who has access.
How can data security be ensured?
The final question is how data security can be ensured. With so many entities sharing their data, it’s crucial that security is maintained. Not only to prevent someone accessing data they shouldn’t, but to ensure it’s not vulnerable to hacks. This also makes the question of who owns the model more important, as they will likely be responsible for making sure the data stays secure.
Whilst there is incredible potential for solving complex problems using digital twins, there are still a number of questions to be considered to gain public confidence, starting with the above. Once a solution is found though, the opportunities could be endless.