Dr William Bain, CEO and founder of ScaleOut Software, explains how digital twin technology transforms dormant data into meaningful insights for smart cities.
Every day, thousands of sensors and Internet of Things (IoT) devices aid in energy management, transportation, security, and countless other essential systems. HVAC sensors distributed throughout skyscrapers help keep occupants comfortable while minimising energy use. Leak sensors in buildings and irrigation systems make sure water leaks are quickly identified and addressed. Sound sensors and cameras at intersections help police locate shootings and respond immediately.
Every day, thousands of sensors and Internet of Things (IoT) devices aid in energy management, transportation, security, and countless other essential systems. HVAC sensors distributed throughout skyscrapers help keep occupants comfortable while minimising energy use. Leak sensors in buildings and irrigation systems make sure water leaks are quickly identified and addressed. Sound sensors and cameras at intersections help police locate shootings and respond immediately.
As cities swell in population and strive to address the challenges of the 21st century, they have become increasingly dependent on IoT devices. The problem is that these devices constantly create mountains of telemetry that must be analysed to find and address emerging issues. The value of this data lies dormant until it is transformed into meaningful insights. How can operational managers uncover these insights most effectively and use them to keep our smart cities running smoothly?
The data challenge for smart cities
Streaming data from countless devices in smart cities provides the crucial information managers need to operate systems with peak efficiency and respond to emerging issues. For example, this data can help optimise energy distribution and identify congested traffic intersections. However, sifting through huge volumes of data to quickly detect, analyse, and respond to issues presents a formidable technical challenge.
Several common scenarios reveal the scale of this challenge. Consider the task of sorting through millions of sensors to pinpoint a gas leak before it causes a large explosion. New York City alone has over 7,000 high rise buildings which need continuous monitoring. Similar obstacles exist in monitoring thousands of city intersections to detect gunshots and respond to possible criminal activity. Likewise, metropolitan transportation systems need to identify mechanical issues in complex transit networks and fix emerging problems before they cause massive delays.
Traditional information processing systems are not well suited to the challenge of tracking large volumes of sensor data and finding problems in real time. They typically store incoming data in databases or files for managers to query and run offline batch analysis with big data tools. As smart cities generate ever more numerous telemetry streams, they need to embrace new techniques for organising and analysing sensor data.
This will enable managers to maximise their situational awareness and create fast, effective responses to problems.
Digital twins can revolutionise smart city management
Digital twins offer a breakthrough technology for addressing these challenges. Beyond their origins in product design and testing, digital twins have emerged as a powerful tool for tracking and analysing large volumes of streaming data. In the urban landscape, they can be deployed at scale to efficiently oversee complex data streams from the vast array of sensors dispersed throughout a city.
When used for large applications like citywide systems, digital twins need fast computing power. A technology called in-memory computing meets this need, providing a highly scalable platform for hosting thousands of digital twins and quickly analysing incoming data. In-memory computing unites multiple servers or their cloud-hosted virtual counterparts to provide faster access to data than other technologies, such as cloud-hosted serverless functions.
Living in memory, digital twins can deliver responses in milliseconds and rapidly visualise aggregated results. Instead of sending all incoming data to a monolithic database where important but subtle trends can be hard to find, digital twins track each data source independently and analyse its sensor data right away. They also maintain contextual information about the data source that helps uncover hidden insights.
For example, a digital twin can track each sound sensor at a city’s intersections, with thousands of digital twins simultaneously looking for actionable sounds like gunshots. Contextual information can include known sound patterns for each intersection, as well as the specific characteristics of each sensor, to help provide fast, accurate alerts.
Digital twins can also seamlessly integrate machine learning algorithms to detect unusual trends in incoming data. Machine learning augments other analytics techniques with its powerful ability to detect patterns based on a large set of historical data. Once trained, algorithms can discern subtle patterns and swiftly pinpoint emerging issues. Digital twins can fine-tune their machine learning algorithms even further with specific training data from each data source.
Dynamic contextual information from digital twins gives operational managers immediate insights and maximises overall situational awareness. For example, instead of trying to make sense of raw telemetry from gas leak detectors, managers can look at real-time analytics that eliminate false positives and classify the likelihood of actual leaks. Using this technology, they can quickly identify and respond to real issues.
Beyond real-time monitoring, digital twins are also useful in city planning, helping shape the design of smart cities. The complexity of many urban systems, such as traffic and flood control, requires advanced modelling to capture dynamic interactions and measure their impact. By using digital twins, planners can avoid costly issues after they approve new systems.
Digital twins can model thousands of components within these systems, simulate their interactions, capture key performance metrics, and inform design decisions. For example, a simulated traffic control network can use digital twins to model intersections, traffic signals, and vehicles and measure how well a new traffic control system reduces congestion.
Digital twins can help in disaster recovery
When disasters occur, seconds count. Emergency personnel must be able to immediately identify and respond to the most urgent needs. Because digital twins can ingest and analyse huge volumes of data in milliseconds, they can give responders vital information that helps direct resources and minimise casualties.
To understand the role of digital twins in disaster recovery, consider the potential for wildfires from power transmission lines. Power lines surround urban areas and reach across rural landscapes and forests; wildfires started by overheated powerline components are increasingly frequent and severe.
Emergency personnel must be able to identify the source and direction of a new wildfire quickly so that they can direct evacuations and firefighting equipment before the fire overtakes an urban area (as it did in Paradise, California and Maui, Hawaii). Digital twins can track powerline components and provide timely information that boosts situational awareness on the migration of wildfires and helps minimise casualties.
Digital twins can also help find buried survivors after an earthquake using data from 5G cell towers. The technology within 5G towers enables them to pinpoint the direction and distance of callers. Digital twins can ingest telemetry from the towers to keep track of their fast-changing call histories and locations of recent callers. Immediately after an earthquake occurs, digital twins can provide recent locations of missing individuals to help emergency personnel search areas where people may be trapped under collapsed buildings.
Digital twins provide a catalyst for smart cities
As our cities become smarter, the need for continuous monitoring and effective real-time analysis of countless embedded sensors grows more urgent. Thoroughly evaluating new technologies, such as smart traffic signals, before deployment has also become essential.
Because of their ability to track, analyse, and model large systems with thousands of components, digital twins are poised to become an indispensable tool for city managers and planners. ScaleOut Software’s pioneering technology unites the concept of digital twins with scalable in-memory computing. It unlocks the potential for digital twins to meet important needs for smart cities and catalyse the next steps in their development.
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Autor(en)/Author(s): Dr William Bain
Quelle/Source: Smart Cities World, 06.12.2023
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