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Edge Computing

Edge computing is a type of computing that happens near a data source. It allows you to perform computing tasks as close to an IoT device or end user as possible instead of using a data center or the cloud.

What is edge computing?

Edge computing is a type of computing that happens near a data source. It allows you to perform computing tasks as close to an IoT device or end user as possible instead of using a data center or the cloud. This approach can prevent latency issues that impact the performance of an application and reduce the amount of data you send to another location. The method involves an edge server, which executes data processing at the edge network.

How does edge computing work?

Edge computing infrastructure is part of a hierarchical architecture. The cloud is at the top layer of the hierarchy, followed by edge data centers, edge nodes, edge gateways, and edge devices. Those devices include smartphones, security cameras, autonomous vehicles, IoT sensors, and IoT gateways. You can execute computing tasks at the top of the hierarchy and latency-critical jobs at the bottom.

While this infrastructure and architecture seem complicated, the edge model is a relatively simple method for managing data close to the edge of a network. IoT devices connect to an edge module for quick data processing. Edge servers are computation resources that process data at the network’s edge.

Why is edge computing important?

Edge computing is a distributed computing paradigm that has recently increased in popularity. Normally, you would transfer data to a centralized location or the cloud, which takes time depending on the network path’s bandwidth. Edge computing provides a faster alternative to conventional computing and data storage by distributing computing resources and application services via a decentralized infrastructure.

How does edge computing compare against other computing models?

You can always collect data at the edge of a network and send it back to a traditional data center for processing. However, sending large amounts of data to a centralized location like a remote data center or the cloud for processing and storage can be expensive and time-intensive. Edge computing facilitates data processing close to the data source, removing the bandwidth costs associated with the IoT computing model. Edge architecture can send data to a data center or the cloud as needed. However, it does this on a case-by-case basis, removing the need to send all your data to another location.

Edge computing also has advantages over personal computing—the most popular computing model before cloud computing. Instead of processing and storing data locally on a device or on-premise data center, the edge model performs tasks as close to IoT devices, user devices or the network edge as feasible.

Edge computing use cases

Edge computing solutions can benefit organizations in various industries that want to improve performance and process data closer to devices. NASA, for example, uses edge computing to process data in space instead of sending it back to Earth and waiting days for a location to receive it. Astronauts will soon be able to analyze and sequence the DNA of microbes from the International Space Station rather than transporting that information to Space Center Houston. Closer to home, fleet managers can use the edge model to collect data from sensors and cameras on IoT devices connected to vehicles, helping them improve transportation and logistics workflows. Edge computing can also solve day-to-day challenges in the corporate sector, such as removing the latency that sometimes exists when using video conferencing applications.


Manufacturers can incorporate edge computing into their organizations and improve supply chain processes by moving data closer to its source. Instead of transferring data to a data center for analysis—which might involve latency problems—companies can generate real-time insights about production, logistics, and sales workflows and use this information to provide better customer service.

Healthcare Industry

Healthcare providers can use edge computing to collect patient data from IoT devices, sensors, monitors, and other wearables and process this data quickly. That allows providers to identify patterns and trends in patient data in real time without sending all of that data to a remote location like a data center. Another use case for the edge model in the healthcare sector is robot-assisted surgery, which allows doctors to perform procedures using robotic systems. These procedures require zero latency, which edge servers and devices can guarantee.

Self-driving cars

Self-driving cars generate large amounts of data about vehicle safety, road conditions, and surrounding vehicles. Sending this data to the cloud or a data center can cause latency issues that might delay information processing and put other drivers at risk. Edge computing solves this problem by collecting and processing data locally, which reduces latency and allows autonomous vehicles to make real-time decisions based on data insights.


Energy companies that invest in the edge model can improve workflows by moving data storage and processing closer to the network’s edge. An energy supplier, for example, can grow their businesses by creating new services that use real-time information. It can also eliminate the problems associated with cloud-based data processing, such as high costs, scalability issues, and high latency. The supplier can also improve compliance with regulatory standards by offering customers information in a more timely manner.

Benefits & Advantages of Edge Computing

There are many benefits of the edge computing model including improving speed, latency, cost savings, reduced operational expenses and more. Let’s explore them in detail below.

Speed and latency

As previously mentioned, edge computing removes the need to transfer data to another location for processing and storage. That location might be a data center or the cloud. Conventional computing models can slow down processing because of latency issues that occur when data packets move from one point to another. Because edge computing processes data close to a device, it removes the time it typically takes for data to reach another location.

Cost savings and reduced operational expenses

Moving data to the cloud can be expensive, with prices typically based on a per-GB monthly rate and network and API request charges. Hosting a data center can be just as costly and require a monthly outlay that your organization might not be able to afford. Edge computing can reduce operational expenses and save costs by removing the need to process and store data in a centralized location or the cloud.

Privacy and security

Because the edge model eliminates the requirement to send data to another location, you can improve privacy and alleviate security concerns. Edge computing lets you keep sensitive data close to the nearest edge, so there’s no need to transfer it to a data center that might have security vulnerabilities or a public cloud with weak authorization.

Scalability and flexibility

You can invest in more edge devices as you grow your organization, which improves scalability and flexibility. Over time, you might make the edge paradigm your dominant computing model and store and process more data nearer its source.


How is edge computing implemented?

You can implement edge computing with edge computing architecture, such as IoT devices, edge servers, edge nodes, and so on.

What is the network edge?

The network edge is where a device or local network connects to the internet. It is close to the device it communicates with.

How does edge computing reduce latency?

The edge computing model reduces latency by processing data at the network edge and closer to the user, removing the need to transfer data to a data center or the cloud.

Will edge computing replace cloud computing?

Edge computing has become a popular computing model in various industries. It’s unknown whether this model will replace cloud computing in the future.

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