Edge Computing helps 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 difficult to integrate due to their very different origins. In this article written by Daniel Garrote, we dig into the different systems and the role of Edge Computing in helping integrate all these systems.
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.
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.
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.
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.
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:
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.
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:
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!
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.