Edge Computing Explained: Definition, Capabilities, and Applications

Edge Computing Explained: Definition, Capabilities, and Applications

What is Edge Computing?

The storage, processing, analysis, and transportation of data produced by billions of IoT and other devices is being transformed by edge computing. 

Early edge computing initiatives aimed to lower the price of bandwidth used to transfer raw data from the point of creation to either a business data centre or the cloud. The idea is being advanced more recently thanks to the growth of real-time applications that demand little latency, like autonomous vehicles and multi-camera video analytics. Due to 5G's ability to speed up processing for these cutting-edge, low-latency use cases and applications, edge computing is closely related to the current global implementation of the 5G wireless standard.

About Edge Computing 

A variety of networks and devices that are at or close to the user are referred to as edge computing, an emerging computing paradigm. Edge is about processing data more quickly and in larger volume near to the point of generation, providing action-driven solutions in real time.

Compared to conventional models, where computing power is centralised at an on-premise data centre, it has some distinctive advantages. By locating computers at the edge, businesses can better manage and utilise physical assets and develop fresh, interactive, human experiences. Self-driving cars, autonomous robots, data from smart equipment, and automated retail are a few examples of edge use cases.

As centralised as it all may sound, the truly remarkable aspect of cloud computing is the fact that a very large portion of all businesses worldwide now depend on the infrastructure, hosting, machine learning, and compute power of a very small number of cloud providers: Amazon, Microsoft, Google, and IBM.

Capabilities of Edge Computing

Many edge use cases stem from the requirement to process data locally and instantly in circumstances where sending the data to a data center would result in unacceptably high latency.

Consider a modern manufacturing plant as an example of edge computing motivated by the demand for real-time data processing. Internet of Things (IoT) sensors on the factory floor produce a constant stream of data that can be utilised to avoid failures and enhance operations. A modern plant with 2,000 pieces of equipment is able to produce 2,200 terabytes of data each month, according to one estimate. Processing that wealth of data close to the equipment instead of sending it to a distant datacenter first is quicker and less expensive. The devices should nevertheless be connected via a centralised data platform. With this approach, for instance, equipment can exchange filtered data and get standardised software updates that can enhance operations in other plant locations.

Another typical example of edge computing is connected cars. Computers are available on buses and railways to monitor service delivery and passenger flow. With the help of the technology in their trucks, delivery drivers can determine the most practical routes. Each vehicle operates on the same standardised platform as the rest of the fleet when deployed using an edge computing strategy, increasing service reliability and guaranteeing uniform data protection.

Advantages of Edge Computing

Cost savings are often enough to motivate businesses to implement edge computing. Businesses that initially adopted the cloud for many of their applications may now be searching for a less expensive option after learning that the costs for bandwidth were higher than anticipated. Edge computing may be appropriate.

  1. AI and Edge Computing

Companies like Nvidia continue to create hardware that acknowledges the need for additional edge processing, including modules with AI functionality built into them. The Jetson AGX Orin developer kit, a portable and energy-efficient AI supercomputer geared toward creators of robotics, autonomous machines, and cutting-edge embedded and edge computing systems, is the company's most recent offering in this field.

  1. Security and Privacy Issues

Data at the edge can be problematic from a security perspective, particularly when it's being handled by various devices that might not be as secure as centralised or cloud-based systems. It is crucial that IT recognises the potential security concerns and ensures those systems can be secured as the number of IoT devices increases. This includes using access-control techniques, encrypting data, and possibly VPN tunnelling.

The reliability of an edge device can also be impacted by different device requirements for processing power, electricity, and network connectivity. For devices that process data at the edge, redundancy and failover management are essential to ensuring that the data is delivered and processed properly in the event of a single node failure.

Technology in Edge Computing

Edge computing can be implemented on networks other than 5G (like 4G LTE), but the opposite isn't always true. In other words, without an edge computing infrastructure, businesses cannot really benefit from 5G.

Even more of your life's experiences will be under corporate control than they are today.

But Google is also making a lot of effort to make even webpages more cutting-edge. Progressive Web Apps frequently prioritise offline use. As a result, you can use your phone to access a "website" without an internet connection, work on it, save your changes locally, and only sync with the cloud when it's convenient. Google is also becoming more adept at fusing local AI elements to reduce bandwidth usage and protect privacy. One such example is Google Clips, which by default saves all of your data local and performs its amazing AI inference locally. It struggles to accomplish its stated goal of preserving cool moments in your life. However, conceptually, it is pure edge computing. 

Examples of Edge Computing

The number of use cases where edge computing can help a business save money or benefit from extremely low latency is increasing along with the number of internet-connected devices.

For instance, Verizon Business outlines several edge scenarios such as end-of-life quality control procedures for manufacturing equipment, the use of 5G edge networks to create popup network ecosystems that transform how live content is streamed with millisecond latency, the use of edge-enabled sensors to provide detailed imaging of crowds in public spaces to improve health and safety, and automated manufacturing safety, which uses near real-time monitoring to send alerts about potential hazards.


You won't need to be concerned about security if Google, Amazon, Microsoft, and Apple are managing the electronics in your house and garage. No need to be concerned about updates. Functionality need not be a concern. You don't need to be concerned about ability. You'll simply accept what is offered and make the greatest use of it.

In the worst-case scenario, you ask Alexa Siri Cortana Assistant when you wake up in the morning what features your corporate overlords have added to your toaster, dishwasher, car, and phone overnight. In the era of personal computers, software was "installed." You'll only use it in the era of edge computing.

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