Edge Computing Adoption in IIoT

Summary

Over the past decade, the Internet of Things (IoT) quietly but significantly changed the way industries operate. Especially in the industrial automation world, the IoT has taken automation to a new level by creating seamless connectivity between machines, systems and people. This gave rise to the Industrial Internet of Things (IIoT), which has pushed these advancements even further into edge computing. ^[1]
 
Edge computing can make a major impact on the efficiency of operations in industrial IoT systems. It improves security measures, speeds up data processing and accurately predicts when maintenance will be required by using the additional computational capabilities on edge. Edge computing will have an even bigger effect than just making things run more smoothly. It will completely change industrial automation in this age of artificial intelligence (AI), paving a path forward for Industry 5.0.^[2]
 
There are big perks, but there are also many obstacles and things to think about on the way to a good adaptation. Let's delve into the transformative impact of edge computing on industrial automation, the advantages, challenges and future growth opportunities, along with some frequently asked questions.

Edge computing advantages ^{[3] [4] [5]}

Edge computing offers numerous advantages to the IIoT, such as enabling faster data processing and analysis at the edge, gaining real-time insights for quicker decision-making and addressing key security risks within industrial automation.
 
Edge computing brings computing power closer to data sources, which helps minimize latency and bandwidth needs by overcoming limitations of cloud-centric architectures by reducing network congestion and improving scalability. It facilitates predictive maintenance, enables autonomous operations and reduces data transmission and storage costs. Edge computing offers the flexibility of moving application data between resources, enhances the processing power for data analysis and opens the door for AI-driven predictive maintenance solutions to be implemented on-prem.
 
Predictive maintenance: By deploying sensors on industrial equipment and computing the data on edge, businesses can gather data quickly and forecast when maintenance is needed, preventing costly downtime.
 
Quality control: Automated systems with a mix of computer vision, sensors and other instrumentation can detect anomalies or other issues and act rapidly on that data by keeping it as close to the process as possible.
 
Warehouse automation: Most retail manufacturers run warehousing next to production lines. Applications on edge can optimize supply chains and reduce losses by making more optimal decisions about what to run locally in the warehouse, whether for latency, cost, security, or any other reason.
 
Artificial intelligence: Artificial intelligence is now referred to as the “golden” use case. Edge computing empowers AI and machine learning applications to process machine generated data in real time, enhancing manufacturing operations.
 
Mitigating security risks: As more devices are connected to external networks, the likelihood of security risk increases. Edge computing improves data security by addressing the potential security vulnerabilities associated with IoT devices. Some of the attacks include: ^[6]

• Data breaches
• Man-in-the-middle attacks
• DDoS attacks
• Insider threats
• Insecure APIs
• Malware and ransomware
• Data sovereignty and privacy concerns
• Inadequate access

Growth trends and market insights ^[7]

The edge computing market in industrial automation is experiencing rapid growth. This is driven by the increasing need for real time processing, increased security and reduced operational cost requirements. The technology advancements in AI are adding more fuel to this growth. As industries embrace digital transformation initiatives, edge computing is becoming a core component in sectors like manufacturing, energy and logistics.
 
Key growth factors include the expansion of IIoT devices and their complex use cases. The demand for low latency solutions within industrial automation is driving the industries to implement more edge-based solutions. Manufacturing industries are investing heavily in edge infrastructure to gain a competitive edge, improve efficiency and reduce downtime.
 
According to a report, the global edge computing market size was valued at USD 16.45 billion in 2023 and is expected to grow to USD 155.90 billion by 2030 at a compound annual growth rate (CAGR) of 36.9% from 2024 to 2030 [8]. Although there are many edge computing offerings available in market, its deployment and operating models have yet to evolve. Edge computing is expected to offer significant growth prospects and open more opportunities for innovation.
 
Globally, North America and Europe are leading the adoption due to their advanced industrial bases and supportive regulatory environments. However, Asia-Pacific is emerging as a strong market. Rapid industrialization in India after the COVID-19 pandemic is driving big investments into smart manufacturing.
 
Looking ahead, the market is set for continued growth, with edge computing expected to play a key role in supporting Industry 4.0 and helping the development of more autonomous and intelligent systems. As technology improves, we can expect smooth integration with other innovations such as 5G, further pushing the capabilities of edge computing in industrial automation into Industry 5.0.

Frequently asked questions

Some of the frequently asked questions about edge computing within industrial automation and the IIoT include:

Is edge computing suitable for small to mid-sized industrial operations?
Yes, edge computing is highly adaptable. Edge applications and devices can be scaled to fit various operational sizes, including small, mid-sized and large industries. It provides flexibility, enabling even smaller companies to benefit from edge computing without requiring significant investments in cloud infrastructure.
 
What kind of infrastructure is needed to implement edge computing?
To implement edge computing, local hardware such as gateways, servers and edge devices are needed to process data near the source. Additionally, good network connectivity is expected and crucial for communication between devices.

Is edge computing expensive to adopt?
While the initial implementation into the edge hardware and infrastructure can be high, the long-term benefits such as reduced downtime, improved performance and lower data transmission costs often outweigh the investment.
 
Can edge computing be integrated with legacy systems?
Yes, many edge solutions are designed to work alongside older systems, allowing companies to upgrade gradually without requiring complete teardown of existing infrastructure. This is usually the recommended upgrade method while performing or installing any latest technologies in an industrial environment.
 
Can edge computing improve response times in critical operations?
Yes, with edge computing, data can be processed and analyzed directly at the source. This minimizes the delay compared to applications processing all the data in cloud. This results in faster response times, which is essential for critical industrial operations such as real-time safety monitoring, fault detection and emergency shutdowns.
 
How does edge computing help in managing remote industrial sites?
Edge computing is very useful in managing remote industrial sites, such as oil rigs, wind farms, or mining operations. One of the main issues with remote industrial sites is connectivity and data bandwidth. Edge computing can help process large sets of data locally which enables real-time control and monitoring without requiring constant cloud access.
 
Do businesses need specialized IT teams to manage edge computing?
A larger or more complex deployment may require a specialized IT team with knowledge in system integration, cybersecurity and data management. Most large-scale businesses are already equipped with specialized IT teams to handle complex tasks. Small to mid-sized businesses can start with basic systems and gradually scale up their IT expertise as the edge infrastructure grows. Most edge solutions are designed with user friendliness in mind, which is beneficial for small to mid-sized businesses.
 
Can edge computing scale as my business grows?
Yes, one of the key advantages with edge computing is its scalability. Companies can always start small deployments and expand their infrastructure as they grow their business and operational demands.
 
In summary, edge computing is reshaping the future of industrial automation. While there are proven studies with cloud-based services, edge-enabled applications are making a stronger case by providing faster, more secure and highly efficient solutions. The ability to process data locally allows businesses to reduce latency, improve real-time decision-making and enhance security. The integration with AI and machine learning is going to fuel rapid growth in its adoption. As edge computing evolves, it is set to play a key role in enabling smarter industrial systems, fostering innovation and enhancing operational efficiency globally.

References:

1. _{Fatima, Z.; Tanveer, M.H.; Waseemullah; Zardari, S.; Naz, L.F.; Khadim, H.; Ahmed, N.; Tahir, M. Production Plant and Warehouse Automation with IoT and Industry 5.0. Appl. Sci. 2022, 12, 2053.}
2. _{Martini, B.; Bellisario, D.; Coletti, P. Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and Perspectives. Sustainability 2024, 16, 5448.}
3. _{Narayanan, Arun, et al. "Key advances in pervasive edge computing for industrial Internet of Things in 5G and beyond." IEEE Access 8 (2020): 206734-206754.}
4. _{Hamdan, S.; Ayyash, M.; Almajali, S. Edge-Computing Architectures for Internet of Things Applications: A Survey. Sensors 2020, 20, 6441}
5. _{Bayar, A., Şener, U., Kayabay, K., Eren, P.E. (2023). Edge Computing Applications in Industrial IoT: A Literature Review. In: Bañares, J.Á., Altmann, J., Agmon Ben-Yehuda, O., Djemame, K., Stankovski, V., Tuffin, B. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2022}
6. _{Sha, Kewei, et al. "A survey of edge computing-based designs for IoT security." Digital Communications and Networks 6.2 (2020)}
7. _{Carvalho, Gonçalo, et al. "Edge computing: current trends, research challenges and future directions." Computing 103.5 (2021): 993-1023.}
8. _{https://www.grandviewresearch.com/industry-analysis/edge-computing-market}
9. _{Bourechak, A.; Zedadra, O.; Kouahla, M.N.; Guerrieri, A.; Seridi, H.; Fortino, G. At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives. Sensors2023, 23, 1639.}
10. _{https://www.rockwellautomation.com/en-us/company/news/the-journal/ai-at-edge-provides-greater-autonomy.html}
11. _{https://www.technologyreview.com/2021/05/24/1025131/edge-computing-powering-the-future-of-manufacturing/}
12. _{https://iebmedia.com/technology/edge-cloud/edge-computing-set-to-revolutionize-manufacturing/}