How edge computing will unleash the potential of IIoT

Combining the potential of Industrial Internet of Things (IIoT) devices with the processing power of edge computing, automation solutions and analytics is giving manufacturing production data more value. See five ways to make edge IIoT deployment more effective.

By Christian Johansson August 4, 2020


Learning Objectives

  • Edge computing and the cloud can be combined to improve the speed and quality of industrial data coming from the manufacturing floor.
  • Edge computing is better for applications where data is needed immediately; the cloud is better for long-term applications.
  • Cybersecurity should be considered in edge computing implementations.

The Internet of Things (IoT) is present as refrigerators order food deliveries and autonomous vehicles schedule their own service, both examples of computing decisions on the edge of the process and the internet. Manufacturing industries have taken a little longer to implement the Industrial Internet of Things (IIoT), but it is now making up for lost time, with significant developments in technology, including edge computing. A better understanding of how to apply IoT helping to build a significant market which will be worth an estimated $123 billion by 2021 [1].

A major challenge industrial companies currently face is how to make the best use of the data gathered from IIoT-type devices and control systems in production processes. For many companies, unleashing the full value of data has been hampered by a lack of the processing power needed to make the most of the industrial data and converting it into useable knowledge.

Edge computing can help by taking computing power and data storage closer to where it’s needed, delivering benefits including faster processing, improved security and optimizing available bandwidth.

Edge computing benefits for manufacturers

Edge computing and industrial analytics’ biggest benefit for manufacturers is the ability to make use of the mass of industrial data to optimize production processes. Companies already have used edge computing and industrial analytics to help the plant to achieve accurate and efficient production – they are now looking at optimizing it through low cost sensing, data analytics and machine learning (ML) to achieve extra efficiency gains.

Putting the computing power near to the devices it serves produces one major obvious benefit – enhanced speed by reducing latency, the time needed for data to travel from source to destination. Latency can be greatly reduced by edge computing compared to sending it to the cloud.

However, these gains depend on the use case – if an application is trying to control or interact with actuators and motors and the process, then real-time control is needed. If long-term process optimization based on the deep analysis of trends is needed, the cloud is the better alternative because it isn’t time critical.

One of the major benefits of combining the cloud with edge computing is the ability to use the cloud to train models using data from IIoT devices. These models can then be executed at the edge, allowing devices to respond more appropriately to changes.

Cloud connections can represent a security risk, which means cybersecurity needs to be top of mind when selecting edge computers. Automation vendors can supply systems that incorporate end-to-end security built on years of experience and accumulated application expertise.

Edge computing also allows more scalability as the business grows and production plants expand. With a scalable range of computing devices, installations easily can be expanded to account for greater volumes of data and additional applications.

Choose an edge computing use case

Many potential edge computing customers are exploring what’s possible and looking for the right use case that will help make a difference to their operations.

Many industrial users have already made themselves highly efficient through the use of significant automation systems – if users want to tune that plant further and get the extra percentages efficiency from it, something new that won’t compromise what is already being done is needed. That difference can be found in IIoT and edge computing.

From edge computing data to knowledge

Using the edge for computing and as a gateway to the cloud offers the potential to support a wide range of use cases of different complexities and extents, from a self-contained process to a set up involving multiple sites. For example, a large mining company wanted to compare operational excellence, safety incidents and maintenance statistics across its mines, treating them as a “fleet” to be managed like any other asset. If the company is operating multiple sites and wants to optimize operations in different ways or wants to optimize the value chain across different factories or the transfer of goods between factories and sites, then they need to be looking at the cloud.

Using AI and ML in edge computing

When it comes to artificial intelligence (AI) processing of IIoT data, some vendors are driving AI and ML technologies from the cloud, while others are building from the edge. There is also something in between – federated learning – which is a combination between smaller applications and the ability to learn and optimize by learning from a fleet of machines. The right choice depends on what the company is aiming for – edge and cloud are equally important depending on the use case.

Companies that put this together are on the way to an integrated system that benefits from the wealth of data from digital technology, edge computing’s fast processing and the cloud’s deep analysis and learning capabilities.

Five ways to make edge IIoT deployment more effective

In the future, the most successful businesses will be those that deploy an effective site, edge and cloud strategy that can drive value by turning their collected data into useful information quickly, efficiently and cost effectively and share it across the enterprise wherever and whenever it is needed.

Consider these five recommendations that can help to achieve these aims and ensure that expectations on return on investment are met.

1. Start with your use case for edge computing

When starting an edge deployment, consider where the industrial data comes from (such as intelligent devices and/or control systems), connectivity capabilities and how much processing will need to be done at the edge versus how much can be done through the cloud. This ensures applications using real-time control data are hosted near the source where data is processed quickly. This reduces latency and offering the best possible response times while other analytics are done in the cloud and easily shared between sites.

2. Keep it simple when looking at edge computing

The great thing with edge computing is its scalability, with elements such as new edge nodes or devices that can be added later down the line if necessary. Achieving this should be made as easy as possible by choosing solutions that allow easy connectivity between devices and systems and seamlessly bridge information technology (IT) and operations technology (OT) functions to give a greater insight into the data. Fast deployment of new value adding applications should also be easy.

When it comes to selecting the appropriate technologies for an edge IIoT project, it pays to opt for solutions that are already pre-validated and proven to work together. Taking this approach will help ensure the edge IIoT solution can be deployed and/or scaled up quickly with a minimal risk of potential errors caused by untested or non-compatible technologies.

3. Consider the difference between the edge and the cloud

While some believe edge computing will replace the cloud, the reality is both serve different purposes, with neither being more dominant than the other. Again, consideration of the characteristics of the use case, and how the data itself will be used, will help to guide the choice.

The key differences between the two include bandwidth, latency and network availability. The main reasons for choosing edge computing over cloud computing are performance related. Edge computing is ideal for real-time applications where a fast response is needed, while the cloud offers the ideal solution for applications calling for centralized collection and analysis of complex data where time is less critical.

4. Consider security and integrity

Data, which serves as a company’s collection of their know-how, ideas and operations, is the most valuable asset and needs to be protected. In designing an edge IIoT solution, and choosing the right partner to help deliver it, cybersecurity and data privacy must be the top priority. This security should extend to all levels, providing robust protection from cyber infiltration and unauthorized access and ensuring plant integrity and confidentiality through inherent security features. The cybersecurity provider also should be able to help maintain the confidentiality of the data, providing full transparency on how it’s used and stored and ensuring nothing is disclosed to a third party without prior consent.

Another important consideration is to make sure that the edge computing provider can guarantee continued protection against potential security issues by evolving software and hardware to ensure their products keep pace with the latest cybersecurity standards.

5. Pick the right edge computing vendor or partner

Finding the right edge computing vendor or partner to work with can be key to determining the success of an edge IIoT deployment. Things to look out for include an established and proven portfolio of solutions, including products and applications that can be integrated to work together. The vendor or partner also should be able to demonstrate a good track history in deploying and supporting solutions in industries and applications that are either the same or similar to your own.

Finding edge computing opportunities

Extending automation solutions with edge and cloud applications offering analytics opens new opportunities for improved performance spanning all areas of a manufacturing operation from production to fleet management and maintenance. By using the combined power of these technologies to unlock and unleash the data from IIoT-type devices and control systems in their production processes, industrial companies can transform their efficiency and realize Industry 4.0’s benefits.

Christian Johansson, global product manager – digital for ABB Process Control Platform. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology,


Keywords: edge computing, Industrial Internet of Things, IIoT


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ABB (ABBN: SIX Swiss Ex) is a technology provider driving the digital transformation of industries. With a history spanning more than 130 years, ABB has four businesses: Electrification, industrial automation, motion, and robotics and discrete automation, supported by the ABB Ability digital platform. ABB’s Power Grids business will be divested to Hitachi in 2020. ABB operates in more than 100 countries with about 144,000 employees.

Author Bio: Christian Johansson started 1996 with project engineering of ABB control systems in various industries. Since 2002 he has held different marketing and sales positions for ABB control systems including global product management for System 800xA. Christian is now Global Product Manager for Digital Platforms which enable edge and cloud computing products and solutions within industrial automation industries. Christian graduated from the Technical University of Lund, Sweden with a Bachelor degree in Control and Maintenance Engineering.