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Mobility is the Future of Industrial Edge Computing

Industrial Edge Computing, Industrial Wearables

Mobility is the Future of Industrial Edge Computing

10 Feb, 2020

The complete automation of industrial processes relies on connectivity. For the Internet of Things (IoT) to function without downtime, low latency communications must be achievable to ensure that interconnected networks function both online and offline. This is why 5G, edge computing, edge analytics, and OPC UA over TSN are being integrated into shop floors. But in most cases, the hardware that drives edge computing, such as sensors, human-machine interfaces (HMIs), and smart edge technologies, must be embedded into larger equipment and systems to function properly. Thus, a poorly installed smart device, a defective sensor, or even a shop floor accident could disrupt ongoing edge computing activities and other automated processes. This leaves us with the question, “What is the best option for integrating edge computing into industrial settings?”

This article will discuss:

  • What is mobile industrial edge computing?
  • How mobile edge computing could define the future of industrial processes
  • The benefits of mobile industrial edge computing

 

Edge computing and mobile devices

Many individuals believe that mobile devices are IoT devices – which is wrong because, fundamentally, mobile devices are those which can fit into the user’s hand. IoT devices or objects, on the other hand, refer to any item, regardless of size, that can access the internet. This is why a piece of large industrial equipment or a car may be an IoT device, but it is definitely not a mobile device.

Understanding this difference is important to grasping what mobility means to industrial edge computing; a function which large pieces of equipment may well be capable of. Today, IoT is becoming more mobile and, according to a PWC study, this is because of stakeholders’ attempts to deliver interconnected services to remote areas across the globe, reduce service costs, and improve the services they offer. These reasons will also serve as the major driving factors to deliver mobile edge computing to Industrie 4.0 and industrial operations in general.

 

Understanding why industrial edge computing will become mobile

Imagine a scenario in which an automated guided vehicle (AGV) transporting heavy equipment through a shop floor loses its direction (coordinates) because it struggles to reconnect with the cloud. In this scenario, either the heavily loaded AGV moves like a bull in a china shop or the AGV stops, leading to downtime. Here, the ability of mobile edge computing devices to monitor the AGV’s movement and connect with it in real-time will forestall the negative effects of an uncontrolled automated vehicle. Thus, mobile edge computing can add an extra layer of safety to the process of industrial automation.

The process of transferring large data sets produced by IIoT devices, as well as receiving information from centralized databases can be simplified by mobile industrial edge computing. For example, a shop floor technician can simply walk into a facility with an edge computing-enabled mobile HMI that can link with machines within the shop floor. The mobile HMI’s ability to link with shop floor equipment while staying connected to the cloud enhances the real-time transfer of information. This interconnected process can then be used to streamline maintenance procedures, equipment updates, and ease storage concerns associated with the equipment’s computing resources.

Another important scenario in which industrial edge computing may have no choice than to become mobile is in capturing data and delivering computing power to the deepest edges of brownfield shop floors. Although edge computing devices are now being used to capture specific data from legacy equipment, limitations exist. These limitations are due to the computing devices being able to capture only a single data source – such as machine vibration or temperature – at a time. Thus, the monitoring of tasks is limited to a single variable.

To be able to capture multiple variables such as CNC tool speeds, vibration, temperature, and energy consumption rate multiple sensors will be required. With an increase in the number of variables, more sensors must be added, which is not a cost-effective way to capture data. This situation will lead to the field of industrial edge computing exploring mobile options equipped with diverse applications or software that can capture data from varying sources in real-time. The mobile edge computing technology will also be able to compute varying data to provide the machine with accurate operational information.

Finally, with edge computing comes diverse cybersecurity risks. Statistics show that 70% of edge devices do not mandate authentication of third-party APIs. Also, 60% of industrial edge computing devices do not encrypt captured data natively or at the source. This provides enormous loopholes for attackers to exploit with bots, ransomware, and distributed denial of service attacks. To counter these loopholes, original equipment manufacturers are likely to turn to delivering industrial edge computing through mobile devices. This is because the mobile edge computing device can be designed with features such as data encryption capabilities, anti-virus, and authentication parameters. Thus, safer spaces are created for industrial edge computing to take place.

 

The benefits of industrial edge computing going mobile

The examples provided here already highlight some of the most important benefits of industrial computing going mobile and help explain why mobility is the future. Other benefits which make mobility an important consideration for edge computing service providers include:

  • Cost Reduction – The cost of bringing brownfield facilities in line with smart industrial concepts through the integration of IoT, sensors, and edge computing technology is high. To reduce these added expenses, developers will consider developing mobile industrial edge computing devices that serve as single sources of truth for brownfield facilities.
  • Bringing AI and Machine Learning to Facilities – Industrial edge computing can be enhanced by mobile devices equipped with AI features, which simplify the automation of systems and processes within facilities.
  • Enhance the Integration of Interrelated Technologies in Industries – Emerging technological solutions such as the digital twin and generative design require data from every asset within shop floors to deliver accurate results. Thus, the need to capture data from the deepest parts of facilities will increase the call for mobile edge computing devices. These devices can then capture the data needed for emerging technologies to function properly.

 

Conclusion

The future of industrial edge computing will be defined by mobility. This is due to the resource savings, accuracy, security, and simplicity it brings to industrial automation. To take advantage of this paradigm shift, industrial enterprises must understand its implications for manufacturing processes and systems. Only then will an enterprise be able to integrate mobile industrial edge computing to stay ahead of the competition.

 

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