By Anna Liza Montenegro, Director of Marketing at Microsol Resources
In the digital world, data holds the greatest value. That’s why businesses can profit significantly from channeling the ever-increasing amount of data using the combined power of digital twins and edge computing. When put together, you can utilize the two technologies to plan, create, and run large-scale operations, increasing efficiency while lowering risks and costs.
For this reason, we will look into how digital twins and edge computing work side by side. Moreover, we will discuss using twin technology to optimize the edge. But first, we need to discuss what digital twins are.
What are digital twins?
A digital twin is a virtual replica of a real-world object. It is made using data gained from the object itself and other relevant information. A digital twin can be an industrial process, a car, an individual, or even a composite of numerous data sources, such as a traffic crossroads. In general, it combines and synthesizes many layers of information to behave in the same manner as its physical counterpart.
The concept of “digital twins” is shaping the future of how the Internet of Things (IoT) applications are created. In fact, Fortune Business Insights estimates that the digital twin market will grow from $6.9 billion in 2021 to $72.32 billion in 2028.
Why digital twins need the edge
Making digital twins with the highest level of accuracy entails taking into account the unpredictable nature of their properties. For instance, a supply chain can be interrupted by changes in resource availability; a change in pressure and temperature can influence a factory’s processes.
Basically, the physical world is constantly changing. To understand objects in it, you need to learn how they adjust. Some important factors are also time-sensitive. So, to depict the objects with the highest precision, you need to be able to simulate their ongoing flow of change.
So far, cloud infrastructure has acted as an ideal platform for unifying and synthesizing different data sources needed for creating digital twins. But the cloud’s centralized structure has its drawbacks. One is that transferring and analyzing data to a remote location can result in latency concerns.
When the time is slow, sensitive data, entities, or processes risk being displayed incorrectly. In such a case, you end up missing crucial business moments. Moreover, your product batches get damaged, and you waste energy, increasing the overall costs.
The benefits of cloud infrastructure are just too large to forego. But resolving its latency issue is critical if digital twins are to reach their full potential. This is where edge computing is poised to take off.
Edge servers process information closer to the source, which reduces latency problems while transmitting data to the cloud. This new topography eliminates the delays caused by physical distance.
How digital twins optimize edge computing
Digital twins enable application developers to connect with specific nodes in a system in a straightforward, one-to-one manner. Before, modifications to device logic may have required a physical updating of the device’s firmware, which was a time-consuming procedure. You may have used the cloud to manage general policy changes. Still, if each node in the system has a unique logic set, the management soon becomes complicated to maintain.
But, in the case of digital twins, each node works independently. So, alterations to one node will not (at least directly) influence the activity of other nodes. Digital twin applications can handle considerable variance (among digital twins) without raising overall application complexity. Each digital twin works as an independent entity logically detached from the larger system.
Without a doubt, simpler application development and administration are useful. But the primary benefit of digital twins is the potential for considerable efficiency improvements. That is especially true for edge computing architecture.
Reducing bandwidth and storage needs
Edge computing brings application logic to the point where streaming data is created. That enables the filtering of streaming edge data before it is sent across a local network. However, determining which data to filter out may be difficult. Also, filtering out the incorrect data might result in missing insights about key events.
When you implement digital twins at the edge, contextual information spreads locally, and you can utilize it to filter streaming data effectively. You can make digital twins aware of upstream or downstream devices, historical behavior, environmental, or other status information that can be significant to understanding streaming data. Making this information locally available guarantees that edge filtering algorithms are kept up to date with any important context in real-time.
For example, consider a self-driving vehicle encountering severe weather changes, newly created traffic jams, or course changes. In such a situation, having a developed digital twin of the terrain and the vehicle can assist computing technologies in predicting what adjustments it needs to implement. That way, the digital twin can make the journey safe and successful.
While the digital twin sector is still in its early stages, businesses and suppliers are aggressively investigating the potential benefits. They use digital twin technologies from trusted sources like Autodesk and Autodesk resellers to drastically enhance their edge computing.
In any case, edge computing will further drive the development of the digital twin. Edge computing guarantees that environment visibility is updated in real-time, independent of the quantum of sensors collecting data, thus making digital twins more accurate. In many instances, following digital twin iteration, businesses will be able to make improvements to their scaled live environment just as fast. The path for digital twin-edge computing joint solutions has only just started, and there is significant development potential for both businesses and providers.
About the author
Anna Liza Montenegro is a trained architect and an accomplished marketing professional in the architecture, engineering, and construction (AEC) industry. At Microsol Resources, she develops the marketing strategy, brand management, digital marketing, and other demand generation activities for Microsol’s strategic partnerships with Autodesk, McNeel Rhino, Bluebeam, Enscape, Chaos Group V-Ray, Panzura, Ideate Software, and other partners.
DISCLAIMER: Guest posts are submitted content. The views expressed in this post are that of the author, and don’t necessarily reflect the views of Edge Industry Review (EdgeIR.com).
Autodesk | digital twin | edge computing | Microsol Resources