The Best Examples Of Edge Computing Solutions In Use Today

According to Gartner, the global enterprise edge computing market will reach $19 billion in 2024 with a Compound Annual Growth Rate (CAGR) of nearly 14%.?

This might come as a surprise to you, right?

Edge computing adoption and use are increasing in tandem with the growth of IoT edge devices. It maintains peak device and network performance while saving organizations money on costly cloud computing.?

On the other hand, edge and cloud computing will continue to work well together. While the cloud allows for large-scale computing, the edge offloads localized tasks to make better use of limited resources.

So what are some of the most common uses of edge computing in this era of digital transformation?

Read on to find out.

Rapid Growth In Edge Computing

The Internet of Things (IoT) expansion has increased the volume of data collected at the network edge. In turn, managing that data volume has fueled rapid growth in edge computing use cases.

The relatively small number of connected devices in the early days of the Internet of Things expansion meant that organizations could afford to send all of their IoT data to the cloud or central data centers for processing, analysis, and data storage. However, this methodology is impractical because of the recent proliferation of connected devices in virtually every industry and the steadily increasing volume of data they collect.?

Key Drivers of Edge Computing Growth

There are various reasons for the rapid growth of the edge computing market.?

Aside from the increased number of connected devices, there are three key growth drivers for edge computing:

1. Latency: The monitored process requires a near-real-time response with near-zero latency for many time-sensitive applications. In these cases, a round trip of data to and from the cloud or a corporate data center is impractical.
2. Bandwidth: Edge computing is an appealing alternative due to the physical limits of available bandwidth and the price of transmitting large amounts of data.
3. Reliability: Network congestion can disrupt data flow, resulting in unacceptable interruptions in use cases such as Point of Sale (POS) systems.

The vast majority of monitoring data collected by IoT sensors are standard "heartbeat" data, which indicates that systems are operating normally. There's no reason to send such information to the cloud or distant corporate edge data centers.?

Edge computing use cases are expanding as Artificial Intelligence (AI) and machine learning capabilities improve.

Edge Computing Solutions

Any edge computing solution is required by two broad categories of users:

1. Network administrators and systems integrators who need plug-and-play connectivity to connect devices across their IoT networks and quickly implement edge computing functionality for optimal system performance and sensitive data management.
2. Original Equipment Manufacturers (OEMs) and developers who design and manufacture products with embedded edge computing capabilities and require programmable development modules, wireless radios, and gateways to support rapid development and time-to-market.

Examples of Edge Computing Solutions Today

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Edge computing is applicable in a wide range of applications and industries.

Here are some of the most promising examples of edge computing solutions in use today.

Predictive Maintenance

Organizations frequently use rugged edge computers because they can collect data from various sensors, cameras, and other devices and use that data to determine when components or machinery fail.?

Predicting the failure of a machine or component enables factory operators to perform machine maintenance or replace a component before a failure occurs during normal machine operation, saving organizations money from lost productivity and missed delivery times and expectations.

This contrasts with the conventional model, in which organizations perform labor-intensive and costly routine diagnoses and inspections. Furthermore, with the traditional model, performing maintenance before a component or machine fails is difficult. Organizations can use predictive maintenance to intervene and maintain machinery and equipment before a failure occurs.

Machine And Computer Vision

It is a solution that is most commonly used in industrial settings, where cloud edge computers run machine vision applications.?

Rugged edge computers, for example, are frequently linked to high-speed cameras and infrared sensors that capture a video or photo of the product and analyze it in real-time to determine whether it has any defects. If there are flaws, the product is flagged for additional inspection or removed from the assembly line.?

Edge computers that perform machine vision are typically outfitted with performance accelerators to provide additional processing power. Graphics Processing Units (GPUs) and Vision Processing Units (VPUs) are frequently used to speed up machine vision applications.

Edge Video Orchestration

Edge video orchestration uses edge computing resources to implement a highly optimized delivery method for video, which is widely used but consumes a lot of bandwidth.?

It intelligently orchestrates, caches, and distributes video files as close to the device as possible, rather than delivering video through all network hops from a centralized core network. Consider it a highly efficient and specialized instance of a Content Download Network (CDN) for video, right at the end-users fingertips.

Large public venues benefit the most from MEC-powered video orchestration. Sports stadiums, concerts, and other localized events heavily use live video streaming and analytics to create and increase revenue streams.

Media processing applications running on mobile edge computing and hotspots can quickly serve newly created video clips and live streams to paying customers in venues. This reduces service costs and avoids many quality issues that can arise when terabytes of heavy video traffic hit mobile networks.

This is something that 5G edge computing intends to address in the coming years.

Autonomous Vehicles

Autonomous vehicles must collect real-time data about their location, direction, speed, traffic conditions, and other factors. This requires enough onboard computing power to turn each autonomous vehicle into its network edge.?

Edge computing devices can collect data from vehicle sensors and cameras, process it, and make decisions in milliseconds or less.?

Edge computing applications such as lane departure warnings and self-parking are already widely available. And, as vehicles' ability to interact with their surroundings becomes more widespread, so will the need for a fast and responsive network.?

On busy thoroughfares and intersections, autonomous vehicles will work alongside other connected vehicles, traffic management systems, roadside units, and pedestrians.

Electric vehicles require continuous monitoring and can benefit from edge computing for data management to support predictive maintenance. Edge computing allows for data aggregation to report actionable performance and maintenance data.

Edge computing in EV charging stations can support real-time monitoring and data aggregation of various usage and availability metrics to support charging station optimization and planning for future station placement.

Smart Surveillance

Rugged edge Network Video Recorder (NVR) computers allow surveillance systems in harsh edge environments where regular computers cannot survive. Powerful NVR computers collect, process, and analyze video footage before sending it to the cloud for remote monitoring and analysis.?

This reduces the amount of internet bandwidth required because not all video footage goes to the cloud but only clips with triggers.

Deploying rugged NVR computers to manage smart surveillance systems is especially advantageous for those on metered data plans, where they pay only for the data they use.

Traffic Management

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Because of the computationally expensive complexities of traffic management efficiency, improving real-time data is one of the most effective methods for optimizing traffic management systems. Intelligent transportation systems rely heavily on edge computing technologies, particularly for traffic management processes.

Before thousands of data streams can reach core/cloud networks, the influx of IoT devices and massive amounts of live data necessitates preprocessing and filtering closer to the devices.

Gigabytes of sensory and special data are analyzed, filtered, and compressed using edge computing before being transmitted on IoT edge Gateways to various systems for further use. This edge processing reduces operating costs for network expenses, storage, and traffic management solutions.

Green Technology, Clean Energy, and Smart Cities

The green technology movement is expanding. Smart grid systems and cities can use Edge computing devices to monitor public buildings and facilities for increased efficiency in heating, lighting, clean energy, and other areas.

As an example:

• Edge computing devices get used in intelligent lighting controls to control individual or groups of lights in public spaces to maximize efficiency while ensuring safety.
• Solar fields detect changes using embedded edge computing devices in weather, adjust positioning, and report metrics like battery usage.
• Wind farms use Edge computing to connect to cell towers and route sensor data to substations via cellular routers and switches.

Azions Edge Computing Platform

Edge computing has altered the way data is accessed and rationally computed. According to research, the global IoT base will surpass 75 billion connected devices by 2025.??

Edge computing architecture will reach its peak sooner than expected due to advances in artificial intelligence and connectivity technologies and increased demand for smart IoT applications.

This means that you cannot afford to be left behind. And that is why we are here to help.

Using Edge Computing Solutions, we create an innovative and adaptable technology platform to meet the needs of your market.

With a serverless model that lowers costs and streamlines day-to-day operations, Azion's Edge Platform maximizes the benefits of edge computing for IoT, giving businesses more time to develop innovative IoT solutions.?

Visit our product page or contact our sales team for more information about Azion and Edge Functions' features.