One aspect of an [[Internet of Things]] device is to collect data about itself and about the context it is operating in. A sensor that measures how many people walk in and out of a store measures something external while other sensors measure internal data points, for example how long they have been operating, their internal temperature and other telemetry data about their state. In effect they create a digital likeness of themselves, describing their current state (see [[Augmented References and Referents]]). Paired with enough knowledge about the static properties of the object (size, weight and so on), this creates an accurate digital replica, directly linked to the objects’ physical form. This concept is called a [digital twin](https://en.wikipedia.org/wiki/Digital_twin). As the digital twin is the aggregation of sensory data, changes in the physical object or its environment immediately affects the digital representation. Likewise, as digitally augmented objects accept remote commands to trigger their physical functions, changes in the digital representation affect the physical object as well. ![[GE Digital Industrial digital twins.png]] *Industrial digital twins, GE Digital, https://www.ge.com/digital/blog/industrial-digital-twins-real-products-driving-1b-loss-avoidance* The collected data can be stored and analyzed later. This allows a deeper look into how individual objects behave over time: "How did an individual machine behave at peak load times vs. idle times?" "Did the performance or operational capabilities degrade over time?" This also allows looking at multiple objects in a physical environment: "Are the performance metrics of one machine comparable with others in the same location?" "How are machines in one factory / location compare to the same / similar machines in another?" This allows to differentiate between correlation and causation in an environment by looking at all the data points and understanding the interdependencies. ## Key drivers for digital twins Digital twins are a direct result of adding central management systems to IoT and edge devices. It would indeed be more effort to prevent the creation of digital twins than to utilize them. They provide huge returns on investment almost without additional costs and thus supercharge the adoption of IoT and edge computing. One important scenario that digital twins enable is predictive maintenance. If all objects of a specific type are connected and transmit their operational telemetry data, they create a database of behaviors - their usage patterns, performance, maintenance cycles and outages. At some point this database has enough data points to be able to make statistically significant predictions: "Other machines of the same type with the same usage pattern required maintenance about now." Systems like this can also calculate the likelihood for incidents like material fatigue or other potential outages in industrial environments, which is even more valuable. Digital replicas can also be used for simulation purposes, often to understand behavior in complex systems. Such simulations contain a number of digital twins, representing real objects, which are placed into a simulated scenario like a city, a factory or a private home. Because digital twins are for all intents and purposes real "actors" within the scenario, they are able to realistically simulate behavior, especially interdependencies between individual objects. The automotive industry uses simulations like this to train and test models for autonomous driving. They place a vehicle consisting of a number of digital twins representing the IoT / edge devices responsible for its behavior in different scenarios and environments. Additional twins of objects (like road signs or traffic lights) or agents (people and animals) would populate these simulations and provide the models things to react to. ![[Simulating and visualizing physical environments.png]] Simulating and visualizing physical environments, Microsoft & Toyota Material Handling Group, https://news.microsoft.com/transform/toyota-forklift-factory-logistics-digital-transformation/ In industrial settings, simulations like this are used to test malfunctions and how redundancy systems would behave, or in training to let participants train on digital representations that behave like the real objects would, but without potential real world risks. ## Summary **What it is**: An accurate digital replica of a digitally augmented object, directly linked to its physical form. **What it enables**: Everything physical also has a digital layer, which can be used interchangeably: Where changes in the physical form translate into the digital dimension and vice versa.