How can digital twins be used to drive the Internet of Things?

In many ways, a digital twin is similar to a virtual machine in that it is a digital entity designed to mimic the way a real object works. But a virtual machine is designed to be a replacement for a real physical computer. In contrast, digital twins are designed to be tested and experimented with, but the ultimate aim is to use any insights gained from a digital twin for its physical counterpart.
Fifty years ago, NASA kept full-scale models of its capsules close at hand to help it diagnose problems with real capsules in space and to help it come up with solutions to problems that could be communicated to distant astronauts. конвертер серийного номера в wifi
The idea of a digital twin is very similar to this. However, rather than building a physical model of a space capsule or any other physical object, a digital twin is an accurate digital simulation of an object that exists only as computer code.
In many ways, a digital twin is similar to a virtual machine in that it is a digital entity designed to mimic the way a real object works. But a virtual machine is designed to be a replacement for a real physical computer. In contrast, digital twins are designed to be tested and experimented with, but the ultimate aim is to use any insights gained from a digital twin for its physical counterpart.
A good example is the use of digital twins of cars and digital crash test dummies. Car designers can run a digital twin of the car and one or more dummies through many different types of crash scenarios, and use the data from those crashes, with the ultimate goal of improving the safety of real car designs.
Digital Twins and the Internet of Things
Creating accurate digital twins of space capsules, cars, and crash-test dummies is a complex undertaking that requires studying the physical properties of these items and then developing an accurate mathematical model to describe them and their behavior.
But the good news for those involved with the Internet of Things is that the things involved are often relatively simple sensors that can be mathematically modeled orders of magnitude simpler than something as complex as a space capsule or a car. промышленный iot-шлюз
This means that creating digital twins of multiple IoT devices is relatively simple, fast and, crucially, inexpensive.
Additionally, many IoT environments consist of a large number of simply identical devices, such as temperature or humidity sensors in shipping containers, or GPS units in vehicles. This means that once a digital twin of a device has been created, it is possible to create a large number of simulations of these devices working together simply by making multiple copies of the digital twin and feeding them data. This can be artificial data or data received by existing physical devices.
As we will see, the implications for large-scale IoT deployment and management are enormous.
Multiple IoT applications
At the outset of IoT initiatives, digital twins can be used as device prototypes to help fine-tune the precise design of the device itself and its firmware, encryption and other software.
Once this process is complete, the digital twin can be used to help optimize the deployment of equipment, testing how much equipment is actually needed, where they should be placed, and how they should be connected to data collection centers through various networks.
Test updates and changes
Once a large number of devices are deployed, digital twins can also be used to test firmware and other software patches and updates before being sent over-the-air to physical devices. This is especially useful when the way devices interact with each other changes, as large-scale simulations allow developers to see the results of patches and updates before they are fully deployed.
Digital twins can also be used to help design and manage changes in network topology, and can even be used to collect data. For example, an enterprise may be collecting data from IoT devices on servers in its data center, but as the IoT network expands, it may decide that it needs to send the data to the cloud for collection.
In such cases, a digital twin can help predict when a switchover will be required, how performance may be positively or negatively impacted, and how large a cloud resource is required to achieve the desired level of data collection and processing performance.
This way of using digital twins in IoT networks is called a "predictive twin". Instead of using real data, the digital twin network as a predictive twin can also be used to test the impact of different types of data flows, increased data traffic and many other scenarios to understand the impact on IoT networks and what changes might be required in the future .
Digital twins can also be used as predictive twins, as they can also be used to predict when their physical counterparts will need to be maintained or replaced in many different usage scenarios.
Digital twins are particularly well-suited for IoT deployments due to the relative simplicity of IoT devices and the fact that digital twins can be replicated at little or no cost. Digital twins could exist for "billions of things" in the near future.
For businesses deploying IoT, the possible benefits of digital twins are staggering. Billions of dollars can potentially be saved in maintaining, repairing and operating and optimizing the performance of IoT assets.