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The Internet of Things (IoT) has created a vast network of interconnected devices that collect tons of data, which businesses use to improve product development, automate low-value tasks, and so much more. But businesses with extensive IoT networks may have a constant concern that refuses to fade: what if a sole malfunction throws the entire system into chaos? One faulty sensor in a production line can lead to a domino effect of errors in a connected system that could cause cascading energy waste. It’s the situation you secretly dread. Don’t worry; there is a solution. Please, meet digital twin IoT.
Research and Markets forecasts that the global digital twin market will grow from $10.4 billion in 2023 to $1,034 billion by 2033. Digital twins are quite a popular technology solution that is gaining traction in many sectors, but what exactly are they? What value can this technology add to your IoT network? In our pursuit to make a detailed overview, we’ll discuss all these questions as well as the practical applications of IoT digital twin and implementation strategies. Let us show you how to integrate digital twin in IoT to earn the best rewards.
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Let’s start from the basics and sort out what is digital twin in IoT. In simple words, a digital twin is a virtual reproduction of physical objects or systems that mimic and analyze their working conditions in real-world scenarios. What differentiates digital twins from other virtual models and simulations is their firm connection to their physical counterparts through continuous data collection and measurement. The information for digital twins can be fed in various ways (historical data, external sources, user input), yet the integration with IoT will give them a constant source of real-time data, which will make them even more powerful and beneficial tools for businesses.
Let’s go into detail to see how digital twin IoT gathers and uses data to function:
Sensors are digital twins’ eyes and ears. Installed on the physical object, they collect real-time data on various critical parameters, like temperature, pressure, vibration, and performance metrics, to provide the raw material for the virtual replica.
The collected sensor data is then processed and analyzed to recognize patterns, data correlations, and potential anomalies. Based on the received insights, the twin simulates the physical device’s behavior and creates a virtual model of its performance.
The twin continuously monitors the health and performance of its physical counterpart, and if it notices any deviations from the norm (e.g., temperature, pressure, etc.), that may indicate the start of the potential issues. The discovery of the problems at such an early stage before they cause disruption to the whole system saves time and resources.
One more question that often arises is, what is the difference between digital twin and IoT? We can say that IoT architecture and devices are the data source, while the digital twin is the data interpreter and optimizer. To put it into perspective, consider IoT as the nervous system of a body that collects sensory data from all over. In this case, the digital twin is the brain that processes the received data to make decisions on muscle movement and predictions about the next steps to maintain optimal health.
The concept of IoT digital twin may seem intricate, but in reality, it’s quite straightforward when broken down into its core components. So, what does the digital twin framework consist of?
The core of any virtual twin is obviously a real-world object (machinery or whole factory), system (manufacturing line), or process digital twin (the whole chemical production process that may involve the work of hardware and even employee behavior). So, the physical assets, which are included even in systems and processes, are outfitted with sensors and smart devices that collect information on their performance.
Then, we have the actual digital twins, the high-tech stand-ins for our physical assets. They mimic everything their real-world counterparts do as long as they receive relevant and up-to-date data from the assets.
The system requires communication protocols (MQTT, OPC UA, or AMQP) and data management infrastructure to ensure a seamless flow of data from sensors to digital twins. Communication protocols standardize the language for all the components to understand and exchange data, and data management infrastructure ensures the information is properly formatted, validated, routed, and securely stored before it reaches the digital twins.
There are several connectivity technologies: cellular networks, Wi-Fi, and industrial protocols like Bluetooth Low Energy (BLE) or LoRaWAN, that will suit specific applications best. The right IoT connectivity technology ensures the data stream reaches the digital twins in the best possible way in terms of reliability, speed, and efficiency.
Provided their versatility (component, product, system, or process digital twin), it’s no wonder that digital twins are applicable in a wide array of scenarios and fields. Let’s examine the practical uses of digital twin IoT solutions in different sectors that can help businesses thrive in today’s turbulent market.
The manufacturing sector is a goldmine for IoT-driven digital twin applications. No surprise, 93% of industry leaders have implemented AI in some form! High-value machinery demands proactive management. Digital twins and IoT in manufacturing deliver this by enabling:
Nowadays, most car manufacturers build new cars in virtual environments to perfect the vehicle design before it rolls off the assembly line. Virtual models can simulate production processes and potential on-road issues, which helps companies create safer, more efficient final products. The value of digital twin IoT for products post-release is no less, as it continues to monitor vehicle performance in real conditions and provide feedback on system behavior and reliability.
Although the digital twin in healthcare is nascent, it already makes noticeable impacts across multiple fronts. It lets doctors test various diagnostic scenarios and spot potential issues while still delivering quality patient care. Healthcare providers can also use twin technology to replicate a patient’s body and integrate such information as medical history, genetic information, and lifestyle habits to detect and predict health risks at early stages. Here are some more highly potential use cases in healthcare:
Thanks to an increasing array of smart sensors that flood operators with valuable data on operations, resource use, and other indicators, digital twin IoT takes the complexity out of managing energy and utility facilities. Engineers can create virtual “as-built” or “as-operated” models of utilities to minimize the risk of mishaps that could lead to downtime. Also, simulations of energy assets’ performance reduce the need for physical in-person checks and lower costs. Here are some more applications to consider:
In addition to fleet management and inventory optimization, supply chain operators have to cope with route disruptions, price fluctuations, stock distribution, and warehouse utilization to keep a smooth supply chain flow. Here are a few ways digital twins in logistics can help organizations clear these roadblocks and help you:
Warehouse layouts are no longer a guessing game with digital twin IoT. With a virtual warehouse where you can test different configurations before you ever move a box. Digital twins process data they receive from IoT sensors and smart devices to suggest optimal layouts for maximum efficiency. As a result, shorter picking routes, streamlined workflows, and ultimately, happier pickers and faster fulfillment times – a win for both warehouse managers and customers.
Virtual replicas of vehicles use IoT in fleet management to let managers monitor engine performance, fuel efficiency, and driver behavior. When they have all this information at hand and in real-time, it’s much easier to find the shortest routes and schedule timely maintenance before a major breakdown, which ultimately saves companies costs and improves fleet performance.
Farmers can also benefit from several applications of IoT in agriculture. The replication of farm conditions helps track soil moisture, nutrient levels, and even weather conditions so farmers can see whether their irrigation system needs adjustments. Also, all this information fed to the digital twin solution lets farmers predict crop yields or identify potential diseases before they spread.
Cities that want to encourage investments and promote sustainability for their residents will gain a lot from digital twin adoption. For example, a virtual model of city traffic flow can help identify bottlenecks and set optimal signal timing to ease congestion and enhance roadway efficiency. What’s more, digital twins are an ideal solution to imitate emergencies like natural disasters or terrorist attacks to help cities prepare for and develop an emergency response plan.
Digital twins categorize large amounts of data gathered by IoT into clear and relatable groups like people, products, places, and processes, which is the first step to deriving valuable insights. Yet, data categorization is not the only benefit of digital twin IoT. Here are the advantages our clients reported gaining after we helped them implement digital twin technology.
IoT-powered digital twins are a complex project that is difficult to get started on and only works well when the right groundwork is done. To help you avoid common pitfalls and secure a successful implementation that yields concrete benefits for your business, our experts have shared the best practices they follow.
The first step is to define your goals and select what aspects of your physical system you want to replicate. Will it be a component or a process digital twin? Clear goals will help you set KPIs to measure the success of a project.
Next, you should assess your business readiness for digital twins, which includes data management practices, the IT infrastructure, and your teams’ skills.
Then comes data collection and integration – gather sensor data and identify any gaps that need to be filled. Finally, you should choose the right software platform and integrate all the collected data to create a virtual counterpart that mirrors your real-world system.
You’ll need the right set of tools in your tech stack to ensure accurate simulations, seamless data integration, and sound data analytics. Here’s a table of technology solutions you’ll need at minimum to implement digital twin IoT.
Category | Description | Technologies & tools |
Sensors & IoT Devices | Capture real-time data from physical systems | Temperature sensors in heavy machinery and equipment, GPS trackers in fleet vehicles |
Connectivity Solutions | Ensure seamless communication between devices and digital twins | Cellular networks for remote assets, Wi-Fi for localized data transfer |
Data Management Platforms | Store, organize, and process sensor data | Cloud platforms like Microsoft Azure or Amazon Web Services |
Analytics & Machine Learning (ML) | Extract insights and predict future behavior | Anomaly detection algorithms for preventative maintenance, ML models for optimized logistics routes |
3D Modeling & Simulation Software | Create a visual representation of the physical system | Building Information Modeling (BIM) software for digital twins in construction, CAD software for product design replicas |
IoT gathers tons of information, and how you manage it matters a lot. First things first: identify the data types needed and how your IoT digital twin will collect it (sensors, databases, or IoT devices). Don’t underestimate the volume and ensure your system can handle the massive influx of information, keep it organized and readily available for analysis. Security is paramount, too: strong data encryption and access controls will help protect data from unauthorized access and breaches.
As we mentioned before, digital twin implementation is not an easy endeavor, and you may face some challenges. Let’s review a few of them and discuss how to jump over them.
What Relevant experts recommend to do: The key is to plan for growth from the start. Invest in architectures and elastic cloud solutions that can easily scale and grow with your IoT demands. Opt for modular designs in both hardware and software to enable easier upgrades and integration. Additionally, regularly evaluate and optimize your data management practices to maintain efficiency.
What Relevant experts recommend to do: Perform a thorough audit of the system and processes before you start integration. If you find any issues or inconsistencies that may hinder integration, implement standardized protocols or use middleware. If these don’t help and common practices don’t work in your case, then hire IoT experts to resolve the complexities.
What Relevant experts recommend to do: Invest in top-notch encryption and lock down access with ironclad controls, and regularly patch those digital defenses. Transparency is key too. Be upfront about what data you collect and how you use it. Play by the data protection rulebook, and you’ll build a decent security posture and trust that’s more valuable than any digital gold.
Our expertise in IoT software development is far-reaching, as evidenced by the project we completed for a Norwegian industrial startup that aims to transform project execution in heavy industries. This platform is intended for fully digitalized engineering and operations through digital twin technology.
We built a robust SaaS desktop platform using Angular, Java/NodeJS, and industry-leading solutions like Cognite’s CDF and Reveal. The platform offers users such features:
The outcome is our team has created a cross-device workspace that manages information overload and optimizes workflows through specialist tools. The environment integrates flawlessly with third-party systems and lets users switch between 2D and 3D views and visualize asset models even in the smallest detail. We delivered a digital twin platform solution packed with all the functionalities our client needed to optimize project execution across the entire lifecycle.
If we add AI to the already powerful combination of the digital twin’s simulation strengths and IoT’s data generation capabilities, we can receive something truly transformative. So, what can we expect from this merge?
Possible GenAI solutions. AI in digital twin IoT can open doors to new levels of predictive maintenance: immediate and automated insights on equipment health, predictions of future machine hiccups, and even automated system adjustments that keep your physical asset running smoothly.
Here are a few tips from our experts on how to enhance your digital twin with AI functionality:
Though a digital twin in IoT might be new for many businesses now, a few have already implemented it and enjoy its benefits. It lets companies model, simulate, and experiment in a risk-free environment with any aspect they want to refine or give a boost to and as much as they need.
We know IoT-driven digital twin projects can be extremely complex. So, what you need is a team of experts. You can either assemble your own team of specialists in-house or partner with an IoT development company that will remove the technical complexities of your project. Relevant Software is one such company.
We have the necessary expertise and experienced teams who can help you build a digital twin for any purpose. Talk to our experts to get your questions answered and eliminate your doubts.
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