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Edge Computing Write For Us

Edge computing refers to the computing infrastructure close to data sources, for example, industrial machines or controllers bringing computational capabilities to sensors and data collection devices.

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Business applications are located close to data sources, such as on-premises edge servers and IoT devices, to achieve this. By doing this, organizations can gain insights from data much faster and speed up response times.

Edge Computing Components

• Gateways
• Edge nodes
• Cloud services:

Why is Edge Computing so Important?

Over the past two decades, the explosive advancement of technology has made the world more data-centric. Thanks to the development and availability of IoT devices, businesses now have access to never-before-seen levels of data.

While this is great, analyzing and getting meaningful insights from large amounts of data is challenging. And with 5G networks just around the corner, the speed at which data generates only for increase.

It is where edge computing comes into play. Edge computing allows companies to store and analyze data closer to the source. It eliminates latency issues with cloud computing, making it easier for companies to extract real-time insights from their data.

It also allows businesses to access more powerful computing resources at the edge of their network. Thus, they can quickly process large volumes of data and address specific use cases that require fast response times, such as autonomous vehicles.

How does Edge Computing Work?

Traditionally, data generated at endpoints is transmitted over a wireless access network (WAN) to a local area network (LAN) for storage and analysis. The extracted information is then transmitted to the client endpoint.

This method is effective and reliable, but only up to a point. The process loses efficiency as the number of internet-connected devices and the data they generate increase.

Edge computing is a distributed computing architecture that brings storage and computing closer to the data source, an endpoint device, or a sensor. It allows data to be processed and analyzed closer to the head, eliminating the need for a central processing unit.

Edge computing means businesses can access the information they need in real time without waiting for data to transfer from the cloud. It enables the company to make informed decisions and act faster.

Key Features of Edge Computing

Edge computing has some key features that set it apart from other computing models:

1. Ultra-high network performance
2. Flexibility of implementation
3. Differentiated experiences
4. Integrated virtual scanning and real-time analysis
5. Automation and organization
6. Decentralized processing
7. Security.
8. Scalability

Edge Computing Example

Consider a listed building with dozens of high-definition IoT video cameras. These “dummy” cameras output a raw video signal and continuously stream that signal to a cloud server.

On the cloud server, the video output from all cameras is passed through a motion detection application to ensure that only clips with activity are saved to the server’s database.

That means there is a constant and significant strain on the building’s Internet infrastructure, as the high volume of video streams being transferred consumes considerable bandwidth. Also, the cloud server has a hefty load that simultaneously processes the video streams from all the cameras.

Now imagine the motion sensor computation moves the network perimeter. What if each camera used its internal computer to run the motion detection application and then sent images to the cloud server as needed?

It significantly reduces bandwidth usage, as much camera footage will not have to travel to the cloud server.

Also, the cloud server will now only be responsible for storing the important images, which means the server could communicate with many cameras without overloading. It is how edge computing works.

What are Other Potential use Cases for Edge Computing?

It can be incorporated into various applications, products, and services. Some possible use cases could be:

• Security System Monitoring: As described above.
• IoT Devices: Smart devices that connect to the Internet can take advantage of executing code on the device itself, rather than in the cloud, for more efficient user interactions.
• Autonomous Cars: Autonomous vehicles must react in real time without waiting for orders from a server.
• More efficient caching: By running code on a CDN perimeter, an application can customize how content is cached to serve content to users more efficiently.
• Medical monitoring devices: Medical devices must respond in real time without waiting for news from a cloud server.
• Video conferencing: Live interactive video requires significant bandwidth, so moving back-end processes closer to the video source can decrease delay and latency.

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