Edge Computing: What Is It And Why Is It Important In 2022

The argument for edge computing is being made with the introduction of 5G fast wireless and the spread of IoT devices, which places compute, storage, and analytics near to where data is created.

The storage, processing, analysis, and transportation of data produced by billions of IoT and other devices are 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 center or the cloud. The idea is being advanced more lately thanks to the growth of real-time applications that demand little latency, like autonomous vehicles and multi-camera video analytics.

Edge Computing: What Is It?

Edge computing is described as “a component of a distributed computing topology in which information processing is located close to the edge—where things and people produce or consume that information.”

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At its most basic level, edge computing reduces reliance on a central location that may be thousands of miles away by bringing processing and data storage closer to the devices where information is being gathered. This is done to prevent latency problems from impacting the performance of an application when dealing with data, especially real-time data. Local processing reduces the quantity of data that needs to be transferred to a centralized or cloud-based location, which also allows businesses to save money.

Consider systems that track factory floor production machinery or a web-connected video camera that transmits real-time video from a distant office. While a single device may transport data across a network without much difficulty, issues start to appear when the number of devices transmitting data concurrently rises. Imagine hundreds or thousands of devices sending live video instead of just one camera. Due to latency, quality will degrade, but bandwidth prices could also skyrocket.

By giving many of these systems access to a local source of processing and storage, edge computing hardware and services assist in resolving this issue. For instance, an edge gateway can process data coming from an edge device and only send the pertinent data back to the cloud. If a real-time application requires it, it can also send data back to the edge device.

What Connection Exists Between Edge Computing And 5G?

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

“5G just lowers the network latency between the endpoint and the mobile tower, but it does not solve the distance to a data center, which might be problematic for applications that require low network latency.”

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The relationship between edge computing and 5G wireless will continue to be intertwined as more 5G networks are implemented, but businesses can still install edge computing infrastructure through several network architectures, including wired and even Wi-Fi, if necessary. However, it’s more likely that edge infrastructure would use a 5G network because of the faster speeds it offers, especially in rural locations where wired networks aren’t available.

How Is Edge Computing Implemented?

Although the edge’s physical architecture can be complex, the fundamental concept is that client devices link to neighboring edge modules for faster processing and more seamless activities. IoT sensors, a worker’s laptop, their most recent smartphone, security cameras, or even the internet-connected microwave in the break room can all be considered edge devices.

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An autonomous mobile robot or a robot arm in the automotive industry are two examples of edge devices in industrial settings. In the field of medicine, it could refer to a sophisticated surgical technology that enables surgeons to work remotely. An edge computing architecture includes edge gateways as edge devices in and of themselves. You might hear the modules referred to as edge servers or edge gateways because terminology varies.

Even though service providers will deploy a lot of edge gateways and servers to enable edge networks, businesses intending to adopt a private edge network will also need to take this technology into account.

How To Acquire And Use Edge Computing Equipment

There are several options for purchasing and setting up an edge system. On the one hand, a company might prefer to manage most of the process internally. To execute this, edge devices would need to be chosen, possibly from a hardware provider. Additionally, a network would need to be designed to meet the use case’s requirements, and management and analytic software would need to be purchased.

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Although it would be labor-intensive and necessitate a significant amount of in-house IT experience, it would still be an appealing choice for a large corporation seeking a fully tailored edge deployment.

On the other end of the spectrum, suppliers in specific industries are increasingly promoting cutting-edge services that they will oversee for you. If an organization chooses to choose this path, it need just ask a vendor to set up its hardware, software, and networking in exchange for a recurring fee for use and upkeep. Companies like GE and Siemens offer these IIoT products.

Which Examples Of Edge Computing Come To Mind?

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.

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The gear needed for various deployment options will vary greatly. For instance, industrial users would prioritize dependability and low latency, necessitating ruggedized edge nodes that can operate in a manufacturing floor’s tough environment as well as specialized communication lines to meet their needs.

While low latency may still be necessary for coordinating the movement of heavy equipment, connected agriculture users will still need a rugged edge device to handle outdoor deployment. However, the connectivity piece may take on a very different appearance because environmental sensors are more likely to have higher ranges and lower data requirements. The best option there might be an LP-WAN connection, Sigfox, or something comparable.

Other use cases pose altogether other difficulties. Retailers can employ edge nodes as an in-store clearinghouse for a variety of functions, connecting point-of-sale information with targeted advertising, monitoring foot traffic, and more for a unified shop management application.

What Advantages Does Edge Computing Offer?

For many firms, using edge computing is primarily driven by cost savings. Businesses who first used the cloud for a number of their apps may now be looking for a less expensive alternative after realizing that the costs for bandwidth were higher than expected. In some cases, edge computing is useful.

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However, the capacity to process and store data more quickly is increasingly the main advantage of edge computing, making it possible for more effective real-time applications that are essential to businesses. Before the development of edge computing, a smartphone scanning a person’s face for facial recognition would have to process the facial recognition algorithm using a cloud-based service, which would take a long time. The algorithm might execute locally using an edge server, gateway, or even the smartphone itself in an edge computing architecture.

This sort of quick processing and responsiveness is necessary for applications like virtual and augmented reality, self-driving cars, smart cities, and even building automation systems.

AI And Edge Computing

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

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Orin outperforms Jetson AGX Xavier by an 8x factor, delivering 275 trillion operations per second (TOPS). Deep learning, visual acceleration, memory bandwidth, and multimodal sensor compatibility upgrades are also included.

Security And Privacy Issues

Data at the edge can be problematic from a security perspective, particularly when it’s being handled by many devices that might not be as safe as centralized or cloud-based systems. It is crucial that IT recognizes the potential security concerns and ensures that systems can be secured as the number of IoT devices increases. This includes using access-control techniques, encrypting data, and perhaps VPN tunneling.

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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.

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