Leveraging edge computing’s power in Industry 4.0

Transforming raw data into valuable, actionable insights in real-time is a complex task that necessitates advanced technologies and edge computing can help with the process.

By Bruce Slusser April 10, 2024


Learning Objectives

  • Understand how edge computing can help Industry 4.0 applications.
  • Learn about the benefits and challenges that come with using edge computing.
  • Learn what it takes to develop the right edge computing architecture for an application.

Edge computing insights

  • Industry 4.0 emphasizes optimizing entire systems, leveraging connectivity, and contextualizing data for enhanced efficiency and productivity in industries.
  • Edge computing complements cloud services, enabling real-time data analysis, faster decision-making, and unified data management, crucial for Industry 4.0’s success.

Industry 4.0 represents a significant shift from the Third Industrial Revolution, with the focus on optimizing entire systems and production lines, rather than automating single machines and processes by taking what was data in Industry 3.0 and producing information with Industry 4.0. The advent of Industry 4.0 has brought about increased connectivity and data sharing, leading towards improved efficiency, productivity and performance in the industrial landscape.

The amount of data generated by a smart factory can produce upwards of 5 petabytes of data each week. To put that into perspective, that’s about nine and a half times as much data as YouTube’s entire video database where users upload an average of 35 hours of new video every minute. Managing and leveraging this much data is a significant challenge, requiring systems for data collection, storage and analysis. Furthermore, transforming this raw data into valuable, actionable insights in real-time is a complex task that necessitates advanced technologies.

Edge computing’s role in Industry 4.0

Cloud services have become a critical component of digital transformation due to their scalability and ability to store, process, and analyze data in a central location. Edge products complement these services by providing structure and contextualization to complex or disparate plant floor assets, sensors, and historians. Unlike data at the manufacturing execution system (MES) and enterprise resource planning (ERP) levels, plant floor equipment and sensors can collect data in fractions of a second, necessitating the need for Edge products to consume, contextualize, and publish this data in manageable payloads.

Edge applications, which are often embedded within or located near industrial machines and sensors, collect raw data from these sources. This data could be anything from temperature readings and vibration levels to energy consumption and production rates. Instead of only sending this raw data to a centralized server or cloud service for processing, edge applications have the capability to locally analyze this data. This involves filtering out irrelevant data, aggregating relevant data, and applying advanced analytics and machine learning (ML) algorithms to the data.

Through this local processing and analysis, edge computing devices can transform raw data (values) into meaningful information. For example, patterns and trends can be identified, anomalies can be detected and predictions can be made. This information is much more valuable to industrial processes as it provides actionable insights that can be used to improve efficiency, quality and sustainability.

Edge computing benefits for manufacturers

The decentralized nature of analysis performed on an edge device has a benefit over the big data analytics approach in that it is faster and more real-time. Edge nodes are deployed near the devices they are consuming information from and can scale to meet growing needs to alleviate bottlenecks in an infrastructure.

While the ability to perform analytics in near-real time leveraging an edge device is a benefit, another major benefit for edge applications is the ability to implement high speed decision making, allowing for semi-autonomous models to provide feedback to operators and managers leveraging the insights derived by the ML models implemented.

Edge devices play a crucial role in the creation of a unified namespace, which is a common data model that represents all the data sources and destinations in an industrial system.

By processing and analyzing data at the source, edge devices can provide structure and context to the data, making it easier to integrate and communicate across different devices, platforms, and protocols. This results in a single point of truth for data, improving data quality, consistency and reliability.

A unified namespace also can reduce data duplication and complexity, making the data more manageable and useful. Therefore, edge devices not only facilitate the creation of a unified namespace but also enhance the overall data integrity in an Industry 4.0 environment.

Developing the right edge computing architecture

Edge computing requires a robust combination of hardware and software infrastructure to function effectively. Edge computing relies on a distributed computing architecture that brings data processing closer to the source of data generation, reducing latency and enhancing real-time decision-making. The hardware infrastructure for edge computing often involves a network of Edge Nodes, which can include devices such as sensors, IoT devices, gateways and edge servers. These nodes are strategically placed at the edge of a network, allowing them to process and analyze data locally before transmitting relevant information to centralized cloud servers. The hardware should be capable of handling diverse workloads, ranging from simple data filtering and aggregation to more complex analytics. Edge devices also may need to be energy-efficient, rugged and capable of operating in harsh environments.

On the software side, edge computing relies on a robust and flexible software infrastructure. This includes edge computing frameworks that enable developers to deploy and manage applications at the edge. These frameworks facilitate the orchestration of computing tasks across diverse edge nodes, ensuring seamless integration and coordination.

Edge computing software also involves edge analytics tools for processing data locally, reducing the need for extensive data transfers to centralized servers. Security is a critical consideration, and software solutions should include encryption, authentication, and other measures to protect data at the edge.

Edge computing platforms also leverage containerization and virtualization technologies to enhance scalability and manageability, allowing for the deployment of a variety of applications on edge nodes. A well-integrated hardware and software infrastructure is essential for the success of edge computing, addressing the unique challenges posed by decentralized data processing.

Edge computing challenges for users

Edge computing, while offering advantages in reduced latency and improved efficiency, presents challenges such as limited resources on edge devices, variable network connectivity, and security concerns due to the distributed nature of these devices. Managing data at the edge becomes complex, requiring effective governance and storage solutions to prevent inconsistency and duplication. Scaling edge deployments and ensuring interoperability among diverse devices and platforms pose additional hurdles.

The complexity of developing applications for distributed computing, coupled with lifecycle management difficulties for remote devices, further complicates edge computing adoption. Compliance with data privacy regulations and the consideration of costs associated with maintaining distributed infrastructure are also critical factors that demand attention. Addressing these challenges necessitates a comprehensive approach, integrating advancements in hardware, software, and network technologies alongside the establishment of standards and best practices for effective edge computing deployment and management. While these challenges exist, they are not insurmountable.

Implementing edge computing frameworks that prioritize resource-efficient application design, such as containerization and microservices architecture, helps overcome limited resources on edge devices. This allows applications to be broken down into smaller, manageable components, optimizing resource usage and facilitating efficient deployment on devices with constrained capabilities.

Utilizing edge-to-cloud communication protocols that can adapt to varying network conditions help address connectivity challenges. Technologies such as edge caching, where frequently accessed data is stored locally, reduce dependence on constant network connectivity. Implementing edge gateways that aggregate and preprocess data before transmitting it to centralized systems also minimizes the impact of intermittent or low-bandwidth connections.

Edge computing’s benefits for Industry 4.0

In summary, Industry 4.0 marks a transformative shift from its predecessor, emphasizing the optimization of entire industrial systems through enhanced connectivity and data contextualization. The substantial data output from a ‘Smart Factory’ underscores the immense potential for improved efficiency and productivity. However, the sheer volume of generated data, surpassing even major online platforms like YouTube, presents a considerable challenge. Managing and extracting actionable insights from this wealth of information requires sophisticated systems for data collection, storage, and analysis. As Industry 4.0 continues to unfold, the successful navigation of this data-rich landscape hinges on the continued development and integration of advanced technologies, ensuring the promise of increased efficiency and performance is fully realized in the evolving industrial landscape.

Bruce Slusser is digital transformation practice director for Actemium Avanceon, a CFE Media and Technology content partner. Edited by Chris Vavra, web content manager, CFE Media and Technology, cvavra@cfemedia.com.


Keywords: Edge computing, Industry 4.0


See additional edge computing and Industry 4.0 stories at https://www.controleng.com/edge-cloud-computing/


How can edge computing improve your Industry 4.0 operations?

Author Bio: Bruce Slusser is digital transformation practice director for Actemium Avanceon