Top 5 Use Cases and Examples of Edge Computing

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 edge of the network in order to optimize access to data sources. In other words, devices placed close to 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 closely tied to the advancement of edge computing, as these devices collect data that needs to be quickly analyzed and processed. Edge computing can be managed by sensor-based devices, network devices transmitting data, or on-premises servers located near associated devices that send or receive data.

What are the benefits of edge computing?

The benefits of edge computing include lower operating costs, better longevity, and reduced bandwidth requirements and network traffic. Network and device-optimized real-time processing can keep key processes on track.

SEE: Don’t Curb Your Excitement: Edge Computing Trends and Challenges (TechRepublic)

They also offer four key attributes that elevate organizations that leverage edge computing: robust security, impressive scalability to grow alongside an operation, versatility to meet varied challenges, and reliability that users can rely on. count.

Top 5 Use Cases for Edge Computing

Autonomous vehicles

Autonomous vehicles are not new; consumers are familiar with Tesla electric vehicles that can drive for them.

However, autonomous vehicles linked to edge computing can take advantage of fully autonomous vehicles that can use sensors to assess location, traffic, environment and safety conditions, make decisions about how to manage or respond to these conditions or condition changes, and share data with other vehicles.

Traffic management itself is linked to data from self-driving vehicles managed by edge computing, making it easier to direct vehicles to less congested lanes or around roadblocks and crashes.


Security is a promising segment in the edge computing space, as audio and video surveillance, biometric scanning, and other authorization mechanisms require real-time data processing to ensure that only appropriate personnel are authorized in an installation. A quick response time to deal with security breaches or threats is key to the success of ongoing business operations.

Workplace Safety

Safety in the workplace is a critical priority for any business, and edge computing helps with that. The concept of security fits well with the previous example because it is possible to analyze workspace conditions to ensure that security policies are properly followed to protect workers and visitors on site.

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

Remote monitoring of energy installations

Remote energy monitoring through edge computing can improve both safety and operations. Many of these industries operate in hazardous 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 from disaster or unnecessary wear and tear can increase efficiency and reduce costs.

For example, IoT devices can monitor temperature, humidity, pressure, sound, humidity, and radiation to better understand service functionality and reduce the risk of malfunction. It can also be used to prevent catastrophic disasters such as those involving power plants, which could 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 advanced computing. Assembly line automations can help increase production quality efforts and require fewer human eyes on these processes.

According to a 2020 report from, “91% of industrial companies are investing in building digital factories in the heart of Europe, 98% expect to gain efficiency through digital technologies like integrated MES, predictive maintenance or augmented reality solutions (all related to edge computing), and 90% of respondents believe that digitization offers their businesses more opportunities than risks. »

Machine learning in edge computing is intertwined, helping devices scale their processing and operational efforts 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.

About Shirley L. Kreger

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