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An Overview of Edge Computing in Industrial IoT

Industrial Edge Computing

An Overview of Edge Computing in Industrial IoT

26 Jan, 2020

Recent articles covering the cloud computing trends for 2020 prominently feature edge computing as a concept that can revolutionize industrial operations. This is due to its ability to enable non-connected equipment, manufacturing tools, IIoT devices, and workstations to capture and process data without having to collaborate with a centralized database system. With Industrie 4.0 and the millions of sensors and IIoT devices driving it, edge computing for Industrial IoT is set to reach new heights. Having an understanding of its concept and application benefits is essential for enterprises seeking to harness its powers.

This article will discuss:

  • What edge computing is and how it functions
  • The application of edge computing for Industrial IoT and Industrie 4.0
  • The benefits of edge computing for Industrial IoT applications

 

What is edge computing?

Edge computing can be defined as the deployment of data-handling activities and operations at the source without having to go through centralized network segments. This computing process optimizes equipment, IoT devices, and applications by bringing computing closer to the network edge of these items that produce data. Depending on the device or equipment being considered, the network edge can refer to the area where the device communicates with the internet.

For IIoT devices, such as a smart camera, the network edge will be the processor within the camera, while for non-connected equipment, the network edge will be within the smart edge device attached to the equipment. And what are smart edge devices? This refers to devices equipped with features for capturing data and processing it such as the JSmart HMI. Smart edge devices bring edge computing to legacy machinery within industrial shop floors.

 

Edge computing vs fog computing

The concept of a network’s edge introduces some slight differences in defining edge computing and how edge computing for Industrial IoT functions. IoT devices with network edges or processors located at endpoints inside the device define edge computing, while devices with their network edges located at the local area network (LAN) define fog computing.

Edge computing and fog computing are two sides of the same coin with minor differences. Edge computing is represented by an individual process tied to individual devices while fog computing brings edge computing to multiple devices connected to the same LAN.  We’ll focus on the term edge computing, since it is the predominant focus for the future that will ultimately replace fog computing.

 

How edge computing for industrial IoT functions

The features and function of edge computing are what deliver the diverse benefits associated with it and makes it attractive to organizations. To understand the automation and security benefits it offers, the deployment of an IIoT camera within a shop floor creates an excellent scenario for descriptive purposes.

An IoT camera deployed within a warehouse for capturing the behavioral pattern of employees will capture data about an employee’s movement patterns, shop floor traffic, and points of delay. The IoT camera’s processor can then analyze movement patterns and shop floor traffic with the aim of saving only coordinate data extracted from employees while discarding sensitive employee information and physical appearance data. The analyzed coordinates can then be sent to a centralized system to drive new material handling policies that eliminate shop floor traffic and enhance productivity.

This scenario highlights the fact that edge computing for Industrial IoT occurs within the moment, and captured data can be processed to make real-time decisions or sent to centralized platforms to drive policies. It also highlights the layer of security it brings to the Industrial IoT and the deployment of robots within shop floors. 

 

Applying edge computing for industrial IoT

Industrial edge computing also refers to the different attempts to bring low latency computing to manufacturing facilities. These attempts have been successful and beneficial in elevating the use of edge computing in IIoT devices. To better understand the benefits of edge computing for Industrial IoT, highlighting use cases of its application paint clearer pictures for enterprises.

 

  • Driving Automation – Achieving true automation within industrial shop floors can be possible through the complete integration of edge computing in the equipment, devices, and processes that drive operations. In situations where IIoT devices produce large data sets, sending captured data to centralized systems for analysis before sending actionable results back to the device slows down automation. Edge computing can be integrated to eliminate communication and processing time lags to drive real-time automation. This means a lights-out factory in the real sense can be achieved where human contact and labor are reduced.

 

  • Automating Maintenance Procedures – One of the building blocks of Industrie 4.0 is its ability to introduce predictive maintenance to shop floors. And the Industrial IoT devices across smart facilities must also be maintained and optimized to ensure they operate at full capacity. Edge computing can ensure the maintenance procedure of IIoT devices is completely automated given the device has the ability to know when to seek recharging points and take that action.

 

Thus, Individual IIoT devices will not only capture data about the immediate environment but also monitor their condition and take preemptive actions that ensure they continue to function. These actions could be running diagnostic checks, reporting to a charging port or as advanced as purchasing replacement components for a device’s physical system. The benefits of automating maintenance include reducing downtime and enhancing productivity.

 

  • Enhancing IT Security – In manufacturing, dealing with security loopholes across IT ecosystems has proven to be a challenge for both large enterprises and SMEs. The introduction of IIoT to the shop floor also increases these challenges as IIoT devices create multiple access points for attackers to exploit. Applying edge computing for Industrial IoT is one way to tackle these security challenges due to the concept behind its computing process.

 

The application of edge computing reduces the points of failure or access points that can be exploited as every IIoT device operates independently from one another. The independence edge computing enables can also be extended to legacy equipment and it creates a shop floor in which data manipulation becomes local. This means IIoT and legacy equipment will capture, process, and discard temporary data while sending specific permanent data to a centralized gateway.

 

Conclusion

The build of IIoT devices and equipment makes the introduction of edge computing within facilities an easier process than integrating it within brownfield shop floors. This is because most IIoT devices are already equipped with processing units that can be tweaked to support edge computing. Thus creating an excellent opportunity for enterprises to experiment and take advantage of edge computing for Industrial IoT.

 

 

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