All posts by Eurotech

Digital twins

Digital twins: what are they and how they can help you

The concept of digital twins has been around since the 1970s, but more recently we’ve started to see rapid adoption of the technology in a number of scenarios. With organizations in energy, transport, manufacturing, medicine and others creating digital twins to help understand how specific processes, instruments, even people will react to changing situations.

What are digital twins?

What are digital twins? Simply put a digital twin is a virtual replica of physical thing – whether that’s a process, product or even a person. The digital twin is created using real-time data from connected sensors on the physical asset, which can then evolve using machine learning and simulation. Digital twins allow us to see what’s going on with the physical asset in the real world right now, but also how it might react in the future too. While it sounds like the stuff of Sci-Fi – it really isn’t. It’s here and now.

IoT sensors are already collecting data on real-world machines for analysis in cloud environments. Building a digital twin for each machine is a cost-effective addition to an already connected model and will help businesses like manufacturers to improve machines and processes in the supply chain.

According to GlobalData there are three key technology trends currently driving the adoption of digital twins.

  1. 5G into 6G – as the networks are rolled out and embraced, digital twins will be able to take advantage of the high-speed wireless connectivity they offer without ‘temporal or spatial’ constraints. This will deliver data in real time back to the virtual twin
  2. Digital relationships and partnerships – those organizations working in specific sectors are working together to deliver concepts for digital twins. This is already happening in the energy industry for example, where the partnerships ‘help oil and gas companies to improve asset performance
  3. Personalized medicine – digital twins are widely used in testing to validate drug candidates. It won’t be long before we see a digital twin for patients created and then tested with 1,000s of drugs to see which provide the best result

How can I use digital twins?

There are numerous benefits and applications of digital twins. Business Intelligence (BI) leaders can use digital twins to optimize IoT applications and get the most out of their data by clarifying what IoT application will deliver the most impact to their physical counterpart.

Digital twins can also streamline the delivery of real-world IoT data. With digital twin systems designed to match the physical machines, data latency is minimized.

For those in engineering, manufacturing and testing, digital twins are a game changer. They let you apply and test changes to the digital asset before they’re applied to the physical version. This lets you see the impact and costs of other connected systems and the environment the machine works in. Predictive monitoring and maintenance of remote IoT assets or sensors is simplified as well. Using the virtual replica, you can review and predict likely faults or errors before they occur.

When combining the twin data with business rules, optimization algorithms or other prescriptive analytics technologies, digital twins can also support human decision making or even automate decision making.

This is especially useful in IoT environments, where data from machines can be fed directly into the integrated digital twin and business intelligence solutions. IoT sensors allow BI solutions to pinpoint problem areas and opportunities to optimize, before developing a comprehensive solution on which manufacturers can act.

Digital Twins for industrial automation

What’s their future application?

While currently digital twin solutions are being adopted to ensure the integrity of a physical asset, it’s likely in the future that we’ll witness integration of the twins themselves, as we need to map increasingly integrated application environments.

If that sounds complicated, it’s not. It’s through edge analytics solutions – specifically, IoT-edge computing frameworks – that companies will be able to realize their ideal digital twin integrations. Using IoT Edge Frameworks – like Eurotech Everyware Software Framework – can help to simplify the concept of digital twins of real assets.

Designers, engineers and producers of machines have long recognized the value of being able to simulate physical assets on software on a computer screen. NASA scientists first looked at the concept fifty years ago, but with the increasing adoption of IoT and edge computing the value of digital twins in understanding and identifying product (and human) reactions is easier than you think.

Contact us to find out more about how digital twins can help your business.

Remote and preditive maintenance of medical equipment

Remote and predictive maintenance of medical equipment in the era of connected healthcare

Digitalization is transforming the medical and healthcare system worldwide, with IoT technologies playing an increasingly fundamental role, particularly in the remote and predictive maintenance of medical equipment. Cloud IoT technologies allow for the remote management and monitoring of medical devices and the ability to perform advanced analysis of the captured data – both in the cloud or at the edge. This brings an impressive range of practical applications and benefits to medical equipment manufacturers and healthcare professionals alike.

Cloud IoT supports predictive and remote maintenance to help healthcare organizations monitor, maintain, and optimize their equipment in real time. Here’s what you need to know about what it can do, the barriers to IoT integration and what to expect in the future.

Predictive maintenance of medical equipment

Performance data from a medical device can be collected and analyzed remotely by healthcare professionals and equipment manufacturers to anticipate malfunctions before they happen. Medical devices contain pumps, filters and parts that, like any other machine, have a certain life cycle and need to be replaced at intervals. Typically, hospital staff have technicians on-site to check these machines and their components to make sure they’re working properly, but if an issue is missed and the machine breaks, it can cause downtime and disruption to patient treatments.

To make this process more efficient, and to reduce the need for manual checks and the likelihood of breakdowns, IoT technologies can be used to read the data from the machine’s components to establish how long it’s been in operation and how long it has left before it reaches the end of its life cycle and needs to be replaced. The hospital maintenance group can then be alerted in advance so replacement parts can be ordered and fitted.

Remote maintenance

IoT also enables the remote configuration of medical devices between patients. Technicians or medical staff would usually be required to change the settings of medical devices manually, for example, amending the settings on a dialysis machine between one patient and the next to ensure the correct treatment is being given. IoT can enable the required patient settings to be configured remotely at the back end to coincide with the different appointments. The only intervention required from medical staff is to clean the machines between each use and to welcome the patient, freeing up more medical time and resource to be focused elsewhere.

Retrofitted vs IoT-native design for medical devices

To enable the remote and predictive maintenance of medical equipment, devices need to have IoT capability built into them. This can happen in two ways:

  • Retrofitting is adapting existing medical devices to become IoT connected devices. This requires the IoT gateway to be built into an existing asset’s board or box so the manufacturer can interface the machine’s electronics and read its data, allowing analysis to take place and the necessary rules and alerts to be set up.
  • IoT-native design is employed when the medical equipment manufacturer decides that the next generation of machines will be IoT connected from the roots. This requires an upfront understanding of what medical device data needs to be analyzed and what alerts and triggers need to be put in place before it moves onto installation and trial. The machines are then manufactured with this capability built into the machine.

The market – both in healthcare and other verticals – is currently more retrofitted design than IoT-native, but specialist IoT software skills and support are required throughout both processes to ensure success.

What are the challenges to IoT integration in medical equipment?

Medical equipment manufacturers will often come across two main barriers to deploying the technology. The first is its perceived complexity – it’s incredibly rare that medical manufacturers have the required IoT skills and expertise in-house capable of delivering their desired project, and for this reason, integrating IoT is often perceived as ‘too complex’. Many need to form a partnership with a specialist they can trust, that not only offers the hardware and software building blocks needed to deliver their vision, but the ongoing training and skills needed to manage the project.

The second is security – a well-designed IoT architecture for distributed medical devices needs to offer solid end-to-end security and provide local processing capabilities to enable functionality like access to technical data and configuration management. When dealing with sensitive patient data, there are a number of stringent security certifications to consider, with each component of the device undergoing rigorous testing.

There are different levels of certification required when the data and machines are kept in the hospital environment, compared to when data is transferred through 3G, 4G, and now 5G, infrastructures. Your IoT partner should be able to lead you through the process and ensure full compliancy, regardless of where and how your devices will be used.

Where is the technology going in future?

As more manufacturers actively look to integrate IoT capability in devices to enable remote and predictive maintenance of medical equipment, IoT-native design is likely to become the norm. Once this data collection and analysis process becomes more established, the functionality can be taken a step further to enable an automated ordering process. Data from machine filters, pumps or sensors can be automatically shared with the manufacturer or supplier of the part so that they can be altered when it’s nearing the end of its life cycle and a replacement part should be sent out.

As a result of the Covid-19 pandemic, we’re also likely to see more medical devices moving into the home of the patient to enable remote treatment. More medical device manufacturers are designing and building new devices that can be moved and connected with beyond the hospital environment, forming partnerships with IoT specialists to enable the effective integration of IoT technologies with all the necessary safety and data measures in place.

To learn more about the integration of IoT technologies in the medical sector, or to find out how we can help with your next project, simply get in touch.

Edge computing for business continuity

Edge computing for business continuity: the case of Internet Traffic Surge

How can edge computing – and High Performance Edge Computing – help to solve internet traffic surge issues? Due to the COVID-19 pandemic, almost the entire world population is working from home and many cities are in lockdown. Over-The-Top (OTT) streaming services, as well as all the other Internet services, are experiencing unprecedentedly high level of traffic.

The amount of data generated by users was already a matter of interest for many players in the market, not only in telecommunications:

“The amount of data generated annually is projected to increase from 40 zettabytes this year (2019) to 175 zettabytes in 2022. To put that in perspective, one zettabyte is a trillion gigabytes, making 175 zettabytes the equivalent of about 5.4 billion years of YouTube videos”

(source: Asha Keddy, Intel’s Vice President of Next Generation and Standards)

but now it seems that these numbers could be reached faster than expected. Teleconferencing services, like Zoom, are experiencing 700% increase of traffic and streaming services like Netflix, Amazon Video, YouTube and Disney+ are seeing similar levels of growth.

All this generated traffic is becoming a concern for the Internet Service Providers (ISP) and some of the world’s biggest Internet interconnection hubs are reporting record traffic numbers. For example, DE-CIX in Frankfurt, one of the world’s busiest Internet interconnection, few weeks ago reported a new all-time traffic peak of more than 9.1 Tbits/s.

Internet traffic report from DE CIX
Internet traffic report from DE-CIX

“Whether it’s for exchanging information, streaming films, playing online games, or the exceptional situation people are currently experiencing with the COVID-19 virus, Internet usage is playing an ever-greater role,”

(source: Thomas King, DE-CIX’s CTO)

This sudden and dramatic surge of Internet usage has also had the effect of reducing the speed of the global network:

Mobile broadband performance

In such a delicate moment for millions of workers, who are obliged to work from home, and companies, which have to ensure business continuity, the European Commission decided to take action to prevent a network congestion, asking streaming operators and services to

“take preventive and mitigating measures and encouraged users to apply settings that reduce data consumption, including the use of Wi-Fi or lower resolution for content.”


This pandemic is showing not only how fragile and interconnected our economy is, but also how paramount Internet has become. How edge computing could help in ensuring business continuity during dramatic emergencies?

Offload Traffic from Core Network to the edge

According to the 2018 State of Online Video report by Limelight Networks

“video rebuffering remains the most frustrating aspect of online viewing for 43 percent of global consumers. The report also shows that 29 percent of viewers will stop watching a video the first time it re-buffers, with an additional 37 percent dropping off after the second time”.


Edge computing can help media companies to increase their capabilities and to ensure quality video and streaming performance. A data center at the edge, or a Mobile Edge Cloud (MEC) can:

  • reduce latency
  • offload heavy traffic from the core network
  • optimize delivery of regional content
  • run essential services at the edge such as Network Function Virtualization (NFV), Caching, AI, Non-Network applications.
Mobile internet data paths
Internet data paths: cloud services vs. Mobile Edge Cloud (MEC)

A real example of edge computing to offload Internet traffic is Netflix with its initiative called Open Connect, where they partner with ISP to serve local traffic using local appliances.

Eurotech and Advanet expertise in edge computing

Eurotech and Advanet bring supercomputing performances to the Edge with their HPEC (High Performance Edge Computing) boards and systems for a faster data access and management.

Bringing HPC from data center to field deployable applications means reducing space, weight and power absorption, increasing resistance, robustness and reliability while maintaining the same advanced computational performance and energy efficiency.

Eurotech and Advanet HPEC boards and systems have a rugged and fanless design: they are ideal for embedded applications in difficult environmental conditions such as 5G edge nodes, or Autonomous Driving.

High Performance Edge Computing infrastructure
Eurotech’s High Performance Edge Computers, Data Loggers and Ethernet Switches enable high-performance computing infrastructures at the Edge

For such applications Eurotech and Advanet provide:

  • The DynaNET family: High Performance Ethernet Switches for a reliable networking infrastructure for rugged and HPEC applications
  • The DynaCOR family: HPEC systems that feature an innovative water-cooling system to ensure reliable performance in embedded applications, even under critical conditions. They are the ideal solution wherever computational activity needs to take place closer to the data gathering points.
IoT project risks

3 IoT project risks that prevent companies from adopting IoT solutions (and how to avoid them)

The Internet of Things (IoT) has brought a lot of benefits and disruptive innovations. Despite this, there are 3 main IoT project risks that prevent companies from adopting IoT solutions:

  • IoT security;
  • lack of open standards;
  • integrating legacy M2M/OT equipment with IoT applications.

Driverless cars, fitness trackers, smart manufacturing, precision farming, connected clothing, smart meters that measure utilities, smart sensors to detect mechanical failures, medical devices that can monitor diseases – everywhere you look around the globe there is talk about the Internet of Things.

IoT’s potential is endless. It changes the way that businesses, government agencies and consumers operate and interact to drive new business opportunities; it increases profits, lowers operating costs and increases productivity . Demand for connected devices is growing exponentially. Many companies – from large, multinational enterprise to SMEs – are all looking to capitalize on this trend.

Let’s analyze the 3 major IoT project risks and the way to avoid them. This would allow companies to start integrating IoT solutions and embracing digital transformation.

IoT security: the number one IoT project risk

IoT project risk 1: IoT Security
The main concern of companies adopting IoT solutions is data security

One of the problems that companies are facing with the growth of IoT is security. Connected devices are being developed at a very fast pace with a general lack of security standards or protocols. Companies must look for smart products with security in mind from day one when adopting IoT solutions. This will avoid the risk of breach vulnerabilities.

Security is a major IoT project risk and is today a more complex task. IoT devices are connected and interconnected into a network and are designed to collect and store increasing amount of data – even sensitive ones. Moreover, smart devices need to connect to each other, to the Internet and to the cloud to exchange data. IoT security issues must be addressed at all levels, from the edge to the cloud.  

In earlier years firewall perimeters and virtual private networks enabled IT security. The widespread use of mobile phones, connected applications and the increased level of sophistication of the attackers has led to breaches in those fortified perimeters.

Because of the lack of best practices, security could dramatically increase the cost of IoT projects. Moreover, this lack of IoT security has the potential to scare users away from adopting IoT technologies.

If companies, government organizations and consumers cannot trust that their data is safe, they will become discouraged at the thought of adopting IoT solutions and buy smart devices. Once the breaches begin, adoption of IoT devices is sure to slow down.

Securing IoT devices is not a simple task, especially when projects employ large, globally-distributed deployments. A single security product solution cannot enable end-to-end security: there is no silver bullet. It is essential to look at the entire system. Security must be a fundamental part of the overall architecture of an IoT project, i.e. be built in, not added afterwards.

Lack of open standards

Most IoT edge solutions are based on the integration of sensors, actuators, PLCs, field buses and protocols. Quite often, the specific combination of new and legacy OT technology is the first challenge to overcome when creating an IoT solution.

For example, PLCs are normally connected through serial or LAN interfaces using field-bus communications protocols. While some of these technologies and protocols are open standards, there are literally hundreds that are proprietary and specific to vendors and vertical solutions. Examples in the industrial domain there are field protocols like Modbus or OPC UA, in transportation CAN, or in energy M-Bus.

IoT project risk 2: lack of open standards
Open standards allow a better integration between IoT components

Since there are many different devices, operating systems and programming languages employed on edge infrastructures, the lack of open standards stands among major IoT project risks. It represents a barrier for companies. They would think that adopting IoT solutions is too complex and a waste of time and resources. 

Again, the IoT security issue shows up when there is a lack of open standards. While plenty of standards exist in the traditional IT world, they have yet to be applied in a consistent manner; this would protect IoT devices – deployed at the edge – from breach.

This means that there is a very vulnerable IoT ecosystem with vendors using different hardware, software and third party services, as well as APIs and patch methods. To achieve IoT security, there is the need to establish solid solutions for device discovery with secure identity, authentication and encrypted communications or the underlying protocols are subject to abuse.

Improper security of just one device could result in situations where many other devices in the network become vulnerable. To succeed with IoT, end-to-end security must be a priority. Device manufacturers and software developers need a security model that has a foundation based on open and industry standards to ensure platform and vendor interoperability and incorporate best practices.

Connecting legacy equipment to IoT

To enable IoT solutions that integrate data collected in the field with enterprise IT applications, companies need to connect their legacy equipment (e.g. industrial machinery and PLCs, on-board and in-vehicle components, power meters, etc.) to the Internet.

The simplest, yet most expensive solution to ensure seamless integration between field equipment and IoT applications is to replace the old equipment with new, IoT-ready one. If a person wanted to remotely access and monitor his home heater, he could replace it with a more recent one. The new one would integrate an IoT gateway that can send temperature, consumption and other useful data to my smartphone and make them accessible on the vendor’s mobile app.

This is a so-called “greenfield” solution, and is ideal for newborn companies. For the vast majority of companies, to completely replace the old equipment is way too expensive; there is the need to adapt it to the IoT project requirements. It is therefore necessary to retrofit field assets with sensors or IoT smart devices and gateways. This again arises issues related to IoT security or to the lack of open standards. 

IoT risk 3: retrofit legacy industrial equipment
Retrofitting legacy equipment a big challenge in the IoT adoption

In industrial applications in particular, M2M machinery and components (such as sensors, actuators and PLCs) communicate with different protocols. The majority of sensors and IoT gateway solutions are designed to target a specific set of protocols. This ends up having a crowd of devices with different protocols that need to be integrated and managed within the same IT/cloud application.

How to reduce IoT project risks and enhance IoT adoption?

Under the brand name of Everyware IoT, Eurotech integrates a set of hardware and software components to enable end-to-end IoT solutions. They are secure, completely managed, integrated and based on open standards.

Everyware IoT solves all the above-mentioned issues related to IoT projects adoption:

Digital Twins for industrial automation

Digital Twin and Its Future in Industrial Automation

Industrial automation is undergoing a digital revolution as ‘Industry 4.0’ and Digital Twin technologies introduce new and innovative ways to execute manufacturing processes.

With IoT, artificial intelligence (AI), machine learning, and deep learning at the forefront, manufacturers will have spent close to $70 billion on these new technologies by 2020 to meet new demands.

Investments in new IoT and AI technologies for digital twins

Among these technologies, Digital Twin is changing how industrial manufacturers approach product design, operations, and post-sale services.

“Digital Twin solutions open new opportunities for industrial firms to speed up their Big Data- and AI-driven innovation transformation efforts,” Forrester reports.

A combination of vendor marketing and education—along with real-world benefits realized in Digital Twin use cases—are driving Digital Twin adoption among industrial manufacturers as they become an integral part of companies’ IoT and digital strategy investments.

What is a ‘Digital Twin’?

“Digital twins — digital representations of people, processes and things — will become more connected, providing a rich and real-time view of company processes and assets. Enterprise architecture and technology innovation leaders must plan for a diverse environment with integration challenges.”

Gartner, Top 10 Strategic Technology Trends for 2019: Digital Twins, 2019

A ‘Digital Twin’ is a digital model of a real-life object, most often a physical machine. Gartner defines a digital twin as “a software design pattern that represents a physical object with the objective of understanding the asset’s state, responding to changes, improving business operations and adding value.”

For industrial manufacturers, a digital twin allows them to create digital models of real machines, then combine both digital and physical data to deliver a better understanding of the performance and potential value of those physical machines.

This applies to all aspects of the machine’s functionality—production, maintenance, user experience, and more.

Each Digital Twin is used as a proxy for a real machine, allowing manufacturers to test concepts and hypotheses before applying them to physical machines themselves.

This digital technique—called ‘encapsulation’—allows manufacturers to adapt the digital ‘machine’ before applying changes to real hardware, which is costly and may impact other connected systems.

Digital Twins therefore have a wide variety of benefits and applications. Manufacturers can simulate plans, processes, and systems with zero risk, helping them visualize any variety of new strategies—and their results—before investing real resources in their development.

Conceptually, Digital Twins are simple. There is one Digital Twin ‘per thing,’ and that Digital Twin is updated alongside the ‘thing.’

This allows for the best possible results during experimentation. But despite its simplicity, Digital Twins have a wide range of applications, and are suitable for just about any machine model.

How to use Digital Twin for industrial automation?

Digital Twins have proven especially useful when integrated with IoT systems in industrial manufacturing.

“We see digital twin adoption in all kinds of organizations,” says Benoit Lheureux, research vice president at Gartner. “However, manufacturers of IoT-connected products are the most progressive, as the opportunity to differentiate their product and establish new service and revenue streams is a clear business driver.”

This is especially critical as IoT becomes more commonplace in industrial manufacturing environments. IoT sensors are already collecting data on real-world machines for analysis in cloud environments—building a congruous Digital Twin with each machine is a cost-effective addition to this already connected model, and can only help manufacturers improve machines and processes in the supply chain.

“The Digital Twin is not a new idea: Designers and makers of machines have long recognized the value of being able to simulate those machines on a computer screen. But growing enthusiasm for connecting machines to the Internet of Things (IoT), and for delivering services instead of only selling products, means that the Digital Twin is becoming relevant to a broader audience.”

Forrester, “Digital Twins Combine Enterprise Data And IoT To Drive New Business Value,” 2019

In this way, Digital Twins significantly reduce the cost and work required to maintain and enhance physical things.

AI, machine learning, and deep learning allow for increased automation in these processes as well. Manufacturers can introduce new concepts in digital environments, then count on digital tools to both optimize them and execute them among their physical counterparts.

Digital Twin and industrial automation use cases

Industrial automation streamlines processes in multiple manufacturing environments and use cases, and its integration with Digital Twins is no exception. Humans can manipulate digital environments, suggest or make changes, and introduce additional resources to optimize Digital Twin (and subsequently machine) performance.

Industrial automation allows for these processes to unfold automatically in digital environments before applying new revelations to physical ones. This relieves humans of these responsibilities but safeguarding equipment until automated solutions are confirmed in digital environments.

As Gartner describes, “When combining the twin data with business rules, optimization algorithms or other prescriptive analytics technologies, digital twins can support human decisions or even automate decision making.”

This is especially useful in IoT environments, where data from machines can be fed directly into integrated Digital Twin and business intelligence (BI) solutions. IoT sensors allow BI solutions to pinpoint problem areas and opportunities to optimize, before developing a comprehensive solution on which manufacturers can act.

In one scenario, a Digital Twin for industrial robots was implemented. The Digital Twin allowed manufacturers to program the robots offline for multiple scenarios, where virtual commissioning of the system could be tested.

This dramatically speeds up testing processes and minimizes downtime for the physical machines themselves, “resulting in a higher availability as well as a higher reliability with less failure risk.” The introduction of automation into this scenario rapidly speeds up the virtual commissioning process, delivering results faster and with greater efficacy.

As machines provide real-time feedback via IoT into a Digital Twin, manufacturers can use the Digital Twin to automated predictive aspects of machine performance and maintenance.

In another scenario, the Digital Twin of a locomotive can analyze real-time and historical data and provide recommendations about on-board system status for predictive maintenance and remote monitoring, with minimal human oversight.

The shared evolution of industrial automation, IoT and Digital Twin technologies

“The rise of Digital Twins coincides with the rise of the IoT. When buying machines and other assets, support for Digital Twins and continuous development of twin capabilities should be a selection factor.”

W. Roy Schulte, Distinguished Vice President Analyst, Gartner

IoT empowers industrial manufacturers to learn more about their equipment and better understand processes in real time. However, IoT adds a new layer of complexity to manufacturing environments, where the optimal application and investment strategy for IoT sensors isn’t always clear upfront.

Industrial BI leaders can use Digital Twins to optimize IoT applications and get the most out of their data. Unlike incongruous systems, Digital Twins help manufacturers improve their IoT strategy, clarifying what are the highest-impact IoT applications to render desirable results in Digital Twin environments that reflect their physical counterparts.

Digital Twins streamline the delivery of real-world IoT data as well, where Digital Twin systems are designed to be congruous with those of physical machines. This is a desirable alternative to discerning IoT data in other analytical environments, where the data must be processed to suit the system.

Manufacturers and other companies are realizing the value of marrying IoT and Digital Twin technologies. Already, 13% of organizations implementing IoT projects use at least one Digital Twin; 62% are either establishing or plan to establish a Digital Twin alongside IoT processes, Gartner reports.

Optimizing Digital Twin with manufacturing intelligence

“Rapidly expanding Internet of Things and analytics programs powered by machine learning and artificial intelligence — as well as improving models and simulations — mean that Digital Twins are coming to fill a number of roles. And they’re going to make life better.”

Forbes, How Orlando’s Work with Digital Twins May Change How We Engineer Everything,” 2019

Industrial manufacturers have already identified dozens of successful use cases for automated and BI tools like machine learning, AI, and deep learning systems.

Digital Twins lend themselves to these environments as they connect seamlessly in existing machine applications. They simply serve as an alternative to physical machines in the application of these technologies to optimize results.

Scholars define this application as an ‘Intelligent Digital Twin’. It “consists of all the characteristics of a Digital Twin as well as artificial intelligence to realize an autonomous system”:

The Intelligent Digital Twin can therefore implement machine learning algorithms on available models and data of the Digital Twin to optimize operation as well as continuously test what-if-scenarios, paving the way for predictive maintenance and an overall more flexible and efficient production through plug and produce scenarios.

Automation Technology, “An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System,” 2019

The introduction of automation allows manufacturers to increasingly remove human beings from processes where they are no longer required, instead applying human resources only to mission critical areas in much larger processes.

In this way, Digital Twins can realize their true potential—simulating vast industrial environments with intense precision, and without increasing workloads for human workers.

Conclusion: Digital Twins and our automated industrial future

Gartner predicts that by 2022, over two-thirds of companies who have already implemented IoT will have deployed at least one Digital Twin.

Today, Digital Twin solutions are most often delivered in coordination or even by the manufacturers of physical equipment in which companies invest. But while this model makes sense today, companies will increasingly look to integrate Digital Twins with one another to accommodate each ‘thing’ in increasingly integrated manufacturing environments.

It’s through edge analytics solutions—specifically, IoT-edge computing frameworks—that industrial manufacturers will realize their ideal Digital Twin integrations.

IoT Edge Framework architecture
The adoption of IoT Edge Frameworks – like Eurotech Everyware Software Framework – simplifies the abstraction of Digital Twins of real assets

Eurotech provides a user-friendly and simplified application development environment. It enables asset digitalization to manage their Digital Twins for advanced analytics and data management. Contact us today to learn more about how Digital Twins and industrial automation can transform manufacturing for your company.

Level 5 Autonomous Driving Challenges

The challenges of Level 5 Autonomous Driving

Autonomous Driving is classified according to the amount of human driver intervention. It ranges from Level 0 (no automation) up to Level 5 (full automation).

Enabling Level 5 Autonomous Driving in Automotive, Defense and other Industries requires collecting, storing and processing data at an unprecedented degree. This has been until now unattainable by Embedded Devices and Edge Computers.

Companies in the Automotive industry are spending billions of dollars in investments for developing Level 5 Autonomous Driving technologies. All these players are encountering a number of new challenges that span across many disciplines and technologies.

Embedded systems installed into vehicles must face six main challenges to enable Level Five Autonomous Driving:

  • Performance
  • Storage capacity
  • Ruggedness
  • Certifications
  • Compactness
  • Cooling


Sensors, LIDARS and other technologies supporting autonomous driving generate unprecedented amounts of data. They require ultra-high computational performance that goes beyond the traditional embedded computer capabilities.

Some sophisticated sensors require a bandwidth of 40Gb/s to transfer data, not only in peak conditions, but for continuous operations. Moreover, Level 5 Autonomous Driving applications require constant, reliable and real-time operations while keeping latency as low as possible.

Storage capacity

Level 5 Autonomous Driving applications largely exceed the storage capacity of typical embedded computing devices. Thinking about the 40Gb/s bandwidth mentioned above, it translates to almost 20TB in only an hour of operations.


High Performance Embedded Computing (HPEC) systems and data loggers installed on vehicles must provide reliable, continuous operations for long period of time. They must operate in very harsh environments: withstanding shocks, vibrations, dust and wide temperature ranges.

DynaCOR 40-34 High Performance Embedded Computing system for Level 5 Autonomous Driving


Embedded and electronic systems and Edge Computers installed into vehicles must comply with industry standards.

Automotive certifications, such as E-Mark and IEC 60068-2-6 / 60068-2-27 for shock and vibration are objective ways for characterizing the behavior of the system under stress in actual operating conditions.


Space is more than often at a premium in embedded applications. Systems designed to fit into embedded environments must come with compact size, to be easily installed into vehicles.

However, HPEC systems provide tremendous amount of computational power and they easily heat up. Dissipating such an intense heat would require a proper and powerful cooling system that can be easily installed in the vehicle.


High Performance Computing systems are typically bulkier than embedded systems due to heat dissipation issues: they are usually equipped with big fans that cannot be used in embedded applications where performance is sacrificed to adapt to space constraints.

However, High Performance Embedded Computing systems for Autonomous Driving must provide HPC performances into a vehicle.

Eurotech has a lot of expertise in designing liquid-cooled HPC (High Performance Computing) and HPEC systems. Liquid cooling is an ideal solution for HPEC systems in Autonomous Driving, as most of the cars are already equipped with liquid cooling systems.

Compared to air cooling, liquid cooling allows more computational density and a better energy efficiency: even though Eurotech’s HPEC systems can use up to 500W, the coolant would maintain a temperature of around 41-43°C.

The DynaCOR 40-34 and the DynaCOR 50-35 are unique examples of High Performance Embedded Computing systems that comply with all these requirements, winning the challenges of Level 5 Autonomous Driving: they provide flexible and configurable platforms that allow the creation of in-vehicle Data Centers to bring Artificial Intelligence to the Edge (Edge AI).

3 key railway standards with which every on-board embedded and IoT system should comply

Railway companies looking for new on-board electronic equipment or intelligent transportation systems should consider products that follow specific requirements in terms of operating temperature, shock and vibration resistance, EMC, and so on, in order to guarantee consistent and reliable performance in harsh and contaminated environments such as trains.

That is why on-board electronic devices like IoT gateways, edge computers and intelligent transportation systems should comply with the parameters defined by specific railway certifications. We will discuss three key standard requirements to meet the needs of today’s on-board railway and rolling stock applications..

Continue reading 3 key railway standards with which every on-board embedded and IoT system should comply

Does your enterprise depend on the Mobile Edge? Considerations for mobile computing at the edge of fleet enterprises

In the United States alone there are over 26 million commercial vehicles on the roadways and in operations1, ranging across the spectrum of logistics and heavy moving equipment to agriculture equipment and waste management vehicles.  In today’s connected enterprises, these commercial fleets are managed through tightly integrated operational systems that perform a multitude of services, including fleet management (automatic vehicle location, asset tracking, route optimization), monitoring of vehicle health and diagnostics, vehicle operator console operations, electronic data recording, and video capture.  These systems improve operational efficiencies, reduce costs, and enable delivery of enhanced services.

Enabling technology for all of these applications is the “Mobile Edge”, the ability to extend an enterprise’s operational systems to mobile computing platforms on deployed fleet vehicles.  A variety of options present themselves to both operators deploying these systems and technology companies offering communications solutions to those operators.  In choosing the right solution for the Mobile Edge, the operator or integrator must consider both hardware and software aspects of their system requirements.

Continue reading Does your enterprise depend on the Mobile Edge? Considerations for mobile computing at the edge of fleet enterprises

IT-Centric IoT Device Management

The edge of the IoT is where solutions connect, communicate and interact using sensors, actuators, gateways, agents and controllers. As technology advances vendors are touting “advanced management capabilities” for these devices at the edge. These capabilities vary, ranging from the simple ability to turn a device on and off to more complex actions such as updating software, managing Wifi connections, configuring security policies or changing data parameters.esf_framework

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The Benefits of a Java-Based Application Framework for IoT Projects

IoT projects present many challenges, even when the hardware is designed exactly to customer specifications. Building on proven architecture and software building blocks that would require many years to develop, the use of a Java-based IoT application framework will result in shorter, more deterministic device software development. Using an IT-centric approach to implement the device logic in smart edge devices improves both device management and embedded application management. Once this standard software platform is in place, connecting and getting business relevant data to the cloud is simpler than it ever has been before.

An advanced software framework that leverages OSGi and Java both isolates the developer from the complexity of the hardware and communications infrastructure and also complements the Multi-Service Gateway hardware for an integrated hardware and software solution.


The benefits of IT-centric application development to implement business logic in smart edge devices/service gateways are:

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