About the IoT Digital Twin Framework

A digital twin is the digital proxy of a physical asset or device. A digital twin can help you successfully deploy and use an IoT application.

A digital twin may also be called a twin or a shadow. Digital twin technology may be referred to as device virtualization and can be implemented in differing ways.

About Implementing the Framework

On an IoT platform, a digital twin is a virtual representation of a physical asset, a machine, a vehicle, or a device. It digitally represents the data, processes, operation states, and lifecycle of the asset.

Implementing IoT with digital twin capabilities in a factory, an airport, or a machine plant enables:
  • Better visibility: You can continually view the operations of the machines or devices, and the status of their interconnected systems.

  • Accurate prediction: You can retrieve the future state of the machines from the digital twin model by using modeling.

  • What-if analysis: You can easily interact with the model to simulate unique machine conditions and perform what-if analysis using well-designed interfaces.

  • Documentation and communication: You can use the digital twin model to help you understand, document, and explain the behavior of a specific machine or a collection of machines.

  • Integration of disparate systems: You can connect with back-end applications related to supply chain operations such as manufacturing, procurement, warehousing, transportation, or logistics.

The digital twin capabilities of an IoT platform depend on its design and implementation. Typically, you can implement a digital twin framework in two ways:
  • Simple device models: In this method, you create and use a JSON document that stores the following information about a machine:

    • Name, serial number, and location

    • A set of observed attributes that the machine's sensors observe (for example, the current speed of the machine)

    • A set of desired attributes that the IoT application can set (for example, the desired speed of the machine)

    In this method, you use the attributes of the machines that its sensors capture. This method works best in situations where the sensors may not be continually available, or when communication with the sensors takes place asynchronously.

  • Industrial twins: This method presents information about the design of a machine and model of a sensor device. The information represents the physics-based properties of the machine.

    This method works well with industrial IoT applications that obtain the required information from product lifecycle management (PLM) tools. In this type of implementation, you can represent the physical attributes, design information, and the real-time data of a machine in an asset-versus-model graph.