The Internet of Things (IoT) has created a technological revolution that makes our homes, office buildings, and cities smarter. Countless devices, from light switches to thermostats to sound sensors on street corners, now generate vast amounts of telemetry. We need powerful real-time analytics to examine it all and create actionable insights.
Digital twins provide the answer. Because they can process incoming messages in milliseconds and store contextual information about each IoT device, they can quickly filter out the noise and identify issues that need a response. Acting together, multiple digital twins can capture the big picture about the systems in our smart cities and boost situational awareness for managers and emergency responders.
By maintaining state information about IoT devices, digital twins can intelligently filter incoming telemetry and quickly find issues.
The power of in-memory computing technology enables digital twins to track thousands of IoT devices at once and to build large simulations.
Digital twins can easily model large numbers of interacting entities in simulations to provide new insights for city planners.
With the ability to track critical infrastructure in real time, digital twins provide an important enabling technology for smart cities.
Ready to try ScaleOut Digital Twins? Start your free trial.
As the population surges and IoT technology evolves, city planners and engineers need to harness this technology to improve residents’ lives. For example, they need to reduce congestion at transportation hubs, improve security in public places, and reduce the risks of fires and flooding. Because these systems are complex and involve many intricate interactions, we need tools that can model the use of new technology and measure its potential benefits.
Digital twins offer a powerful framework for modeling complex behaviors within cities. They can simulate thousands of components and model their interactions over time. Simulations like these enable planners of large systems to weigh alternative design choices for reducing congestion or responding to threats so that they can make informed decisions.
Traffic signals in the U.S.
Smart gas meters in Europe in 2021
Gallons of water lost annually due to leaks
Because digital twins have an object-oriented design, they can easily model large numbers of interacting physical entities and complex interactions in a time-driven simulation. For example, in a simulation that evaluates traffic congestion, they can model traffic signals at intersections, vehicles, and road segments.
Digital twins store state information about the physical entities they represent. To model behavior and update their state over time, they run code at each time step in the simulation’s execution. They also exchange messages to model interactions between entities. As the simulation progresses, they record the delays created by these interactions and report performance data to help planners make design tradeoffs.