Edge Computing as a solution to achieve the convergence between IT, OT and ET

After more than 8 years working in the implementation of IoT solutions in industrial environments across different sectors from waste treatment plants and desalination plants to refineries and chemical plants, I have seen firsthand how complicated it is to connect the OT and IT worlds. Let alone if we want to bring ET disciplines into the equation. But let's start by defining what is what in this area.

What is IT?

IT (Information Technology) refers to Information Technology, which includes everything related to computing as we understand it generically: software (applications, operating systems, platforms, etc.) and hardware (computers, servers, network equipment, etc.). It is the technology we use to store, retrieve, transmit, and manipulate data.

What is OT?

OT (Operational Technology) refers to Operational Technologies, which includes any type of system that is used to control or interact with physical events, both the physical machines themselves (robots, sensors, actuators, industrial machinery, etc.) and the control systems that monitor and interact with them. In short, it is the hardware and software needed to monitor and control industrial processes.

What is ET?

ET (Engineering Technology) refers to Engineering Technologies, which are those applied in the resolution of engineering problems such as the design and construction of technical solutions (computer-aided design software, simulation tools, control and monitoring systems, etc.). These are tools used by engineers to perform their work in industrial environments.

What is Edge Computing?

Edge Computing is a data processing model that allows data to be stored and processed close to where it is being generated. It is particularly useful in situations when latency must be minimal or when large amounts of data need to be processed and it is too costly and time-consuming doing it in the cloud.

Now that we are familiar with these terms, let's look at the issue of convergence between IT, OT, and ET, and the historical barriers that have hindered their integration. There are certain challenges when trying to integrate systems that have traditionally been separated and therefore have been developed independently without taking into account these possible integrations.

If we also consider that the scenarios in which we need to converge IT, OT, and ET technologies are usually industrial infrastructures where security is essential and where any failure or vulnerability can cost huge amount of money or even put at risk the safety of people, these challenges become even more "interesting".

Let's look at some of the most important challenges:

• Security.  One of the biggest challenges is to ensure the security of the systems. Older OT technologies that are still in use were not designed to connect to the Internet, which makes them vulnerable to cyber-attacks. On the other hand, IT technologies are often more prepared for attacks but are also much more exposed to them. The convergence between the two technologies can increase the risk of exposure to new vulnerabilities if not handled correctly.

• System integration. Normally OT systems are designed for specific tasks and may not be easily compatible or adaptable to the available IT infrastructure. In these cases, the investment in time and resources that may be required for integration may be unfeasible.

• Data management. While each separate technology can already generate huge amounts of data, convergence involves the management of much more data passing from one system to another, so these types of integration projects require companies to have the right skills and tools to store, manage and analyze data effectively.

• Privacy. The more systems we have connected to each other, the more complex it will be to manage the privacy of user data or even business data, and the more necessary it will be to ensure that privacy laws and regulations are complied with.

• Standards. As IT, ET and OT are systems that have not been designed to work together, there is a general lack of standards for integration, which hinders interoperability and information exchange between the systems.

• Cultural barriers. Beyond the technical challenges we have reviewed so far, there is an important cultural factor to consider. As the 3 technologies have historically operated in isolation, each with its own culture and skill set, in convergence projects there is often resistance to change and collaborate between teams. In addition, there can be a skills gap, as professionals in each field may need to learn new competencies to work effectively in a converged environment.

To respond to these challenges and achieve a successful convergence between these technologies, different measures will have to be adopted. From the technological aspect, edge computing is emerging as the technology that can bridge IT , OT and ET.

Edge Computing is a discipline that, beyond the advantages it can offer in facilitating convergence between technologies; it is a key technology for use cases in which large amount of data need to be handled in an agile way while ensuring cost optimization. The idea is to move storage and computation capacity closer to the data source, rather than relying on the cloud or remote data centers.

In this sense, Edge Computing helps the most  in integrating Information, Operation, and Engineering technologies since it converts data into valid information for real-time decision-making and can relate data coming from IT, OT, and ET systems, which can be complicated to integrate due to their very different origins.

Having computing and the storage capacity, where data is generated and where it has to be related and integrated between systems can make the difference between achieving convergence successfully or not.

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If we revisit the above challenges faced by convergence from the point of view of adopting Edge Computing solutions, we can understand how this technology specifically helps at each of the following points:

• Security.  By being able to process data at the edge and thus, reduce the amount of data to be sent to cloud or remote platforms, we minimize security risks and achieve systems that are much more robust against cyber-attacks. What we achieve at this point with the use of Edge Computing systems is to bring IT technologies closer to OT and ET systems that are normally more controlled and more secure as they work with less exposure to the Internet.

• Systems integration. Having computing capacity where data is generated and where technologies converge, facilitates the integration and translation of data from different systems.

• Data management. As we have already mentioned, Edge Computing not only brings the computing capacity closer to where data is being generated but also the storage capacity, so that, in systems that have to manage large amount of data, it facilitates the use of data and the tasks of translation, processing, and structuring of information.

• Privacy. Along the same line as explained in the area of security, by controlling data locally, it is much easier to comply with privacy legislation and regulations affecting the region in which we are operating.

• Standards. The problem of standards is one of the most complicated to solve until systems start to be designed jointly or standards are adopted that apply to the different IT, OT, and ET disciplines. Until this happens, we will have to look for translation solutions that allow us to move from one standard and/or protocol to another. And here, once again, the use of Edge Computing can help in these translation tasks, by providing compute and storage capacity where data is produced and exchanged. In this sense, the Edge Computing component could be understood as a HUB for connection and translation between systems, which would provide spare capacity for this type of task.

• Cultural barriers. In this case, as this is a challenge that is not directly related to the technology itself, but to the way people work, the contribution of Edge Computing is not so obvious. For the integration of Edge Computing in the convergence of technologies, it will also be necessary to train professionals from different fields and to achieve collaboration between teams. However, if the implementation of Edge Computing solutions helps reduce the above challenges, cultural barriers will surely also decrease as operators and workers will see the improvements that can be obtained.

Now that it is clear that the use of Edge Computing can be a decisive factor in achieving the convergence of technologies, let's look at some real examples to grasp all its potential.

The first example is for manufacturing environments, where Edge computing can help integrate IT and OT by allowing data from sensors and plant machinery (OT) to be processed locally and Machine Learning and Predictive Maintenance (IT) processes can be applied on them to facilitate real-time decision making. Furthermore, in this scenario, the processed data can be sent to the cloud for further analysis to train AI models that could then be run on the Edge. Many industrial machinery manufacturers are working in this direction together with Cloud service providers to develop such solutions.

Another less "industrial" example might be container tracking on ships and in remote locations, where connectivity is very limited. In these scenarios, sensors and data from the ship's own control systems can be used to monitor factors such as temperature, humidity, and container location in real-time.

As connectivity may not be guaranteed for long periods of time, such real-time monitoring would have to be done in an edge computing system that would be in charge of communicating the different OT and IT systems in order to process all the data coming from both systems and would also be in charge of making decisions to act on the containers in case they have to meet certain conditions, such as maintaining maximum and minimum temperature ranges. All processed data could be stored locally while waiting to reach the port or coverage area where it could be sent to the cloud for further processing or reporting.

If we focus on ET technology that is used to develop industrial engineering projects, a good example of convergence with IT and OT would be the development of Digital Twin models. A Digital Twin is a digital representation of a physical object or system that is used to simulate, predict and improve the performance and characteristics of its physical counterpart. In this way, Digital Twins combine real-time data, simulation, machine learning algorithms, and analytics to provide detailed and accurate insight into the state, performance, and efficiency of a system or process.

To achieve these objectives, they rely on Engineering data such as industrial parts inventories, 3D modeling of infrastructures, and virtual or augmented reality representations, which would complement sensor and control system data from OT systems and Machine Learning, Artificial Intelligence, cloud services, or IoT processes from IT systems. All this data can be processed in the Edge layer to achieve robust Digital Twin systems that manage and relate data from the 3 technologies for Predictive Maintenance solutions and simulation of complex behaviors.

These are just a few examples of operation and convergence of OT, ET and IT with the support of Edge processing technologies, but the use cases are innumerable, especially in industrial environments, and we will see more and more proposals for integration of different technologies to achieve more and more complex solutions and that will solve many of the problems and challenges faced by technological environments that require the management of increasingly large volumes of data, and increasingly diverse data.

Long live Edge Computing!

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About the Author:

Daniel Garrote, holds a degree in Computer Engineering from Universidad Politécnica de Madrid (UPM) and a Master in Industry 4.0 from Universitat Oberta de Catalunya (UOC). He has more than 15 years of experience leading Digital Transformation processes and the integration strategy of emerging technologies in large IBEX corporations such as BBVA, Ferrovial and Cepsa, and the last 8 years he has been fully dedicated to lead the development and implementation of IoT solutions in industrial environments. He is Global Head Expert and Director of the IoT Master in Nuclio Digital School, Ambassador and professor in Digital Transformation, IoT and Blockchain programs at MIT and professor in the IoT Master of EOI.

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