Top 5 edge computing use cases and examples


From autonomous vehicles to machine automation, here are five examples of edge computing in action.

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Edge computing represents a technological concept involving distributed cloud computing using resources at the network edge in order for optimized access to data sources. In other words, devices placed in close proximity to the other devices or systems with which they will exchange data. This structure streamlines network efficiency and scalability to improve data processing and real-time applications such as machine learning and augmented/virtual reality.

The growth of the Internet of Things is tied closely in with the advancement of edge computing, as these devices collect data which need to be quickly analyzed and processed. Edge computing can be handled by sensor-based devices, network devices transmitting data, or on-site servers located close to the related devices sending or receiving data.

What are the benefits of edge computing?

The benefits of edge computing include lower operational costs, better longevity, and a reduction in bandwidth requirements and network traffic. Real-time processing optimized by network and device can keep key processes on track.

SEE: Don’t curb your enthusiasm: Trends and challenges in edge computing (TechRepublic)

They also offer four key attributes that elevate those organizations taking advantage of edge computing — robust security, impressive scalability to grow alongside an operation, versatility to tackle varied challenges and reliability users can count on.

Top 5 edge computing use cases

Autonomous vehicles

Autonomous vehicles aren’t a new thing; consumers are well versed with Tesla electric vehicles which can do the driving for them.

However, autonomous vehicles linked to edge computing can take advantage of fully self-driving vehicles which can utilize sensors to gauge location, traffic, environment and safety conditions, make decisions as to how to handle or respond to such conditions or condition changes, and share data with other vehicles.

Traffic management itself ties in with autonomous vehicle data handled by edge computing, making it easier to direct vehicles to paths of least congestion or circumvent roadblocks and accidents.

Security

Security is a promising segment in the edge computing space, as audio and video monitoring, biometric scanning and other authorization mechanisms require real time data processing to ensure only the appropriate personnel are allowed in a facility. Rapid response time to address security violations or threats are a key component to successful ongoing business operations.

Workplace safety

Safety in the workplace is a crucial priority for any business, and edge computing helps make this happen. The safety concept ties in well with the prior example as it is possible to analyze workspace conditions to ensure safety policies are being followed correctly to protect workers and on-site visitors.

For instance, social distancing intended to reduce risk during the COVID-19 pandemic can be enforced by edge computing. Industrial robots can be used with edge computing to reduce risks to live humans and perform routine operations more efficiently by employing actions not subject to fatigue, confusion or misunderstanding.

Energy facility remote monitoring

Remote monitoring of energy through edge computing can improve both safety and operations. Many such industries operate in dangerous environments, such as offshore in turbulent weather conditions, underground (as in mining operations) or even in space. Monitoring to ensure critical machinery and systems are protected against disaster or unnecessary wear and tear can increase efficiency and lower costs.

For instance, IoT devices can monitor temperature, humidity, pressure, sound, moisture and radiation to gain insights into service functionality and reduce malfunction risk. It can also be used to prevent catastrophic disasters such as those involving power plants, which might involve damaged assets or risk to human life.

Machine automation

Machine automation benefits from edge computing by making better use of manufacturing equipment based on manufacturing patterns. Predictive maintenance efforts as well as better energy efficiency can be achieved through edge computing. Assembly line automations can help increase production quality efforts and require fewer human eyes on these processes.

According to a 2020 report by PWC.de, “91% of industrial companies are investing in creating digital factories in the heart of Europe, 98% expect to increase efficiency with digital  technologies like integrated MES, predictive maintenance or augmented reality solutions (all of which tie in with edge computing), and 90% of respondents believe that digitization offers their companies more opportunities than risks.”

Machine learning in edge computing is closely related, helping devices evolve their processing and operational endeavors as conditions or resources change. This is especially essential in development and design structures where determining what works well versus what works poorly is essential for success.



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