Examples of Edge Computing 11 Excellent Examples

This can help alert workers of machinery errors immediately or help self-driving cars identify obstacles more quickly. Some industry watchers believe the cloud will one day be used mostly for storage and massive computations. Major innovations that are now taking off, like IoT, AI and high-tech farming also lean on the edge. “The whole point of edge computing is to get closer to devices, to reduce the amount of data that needs to be moved around for latency reasons, to get closer so that responses are faster,” said Matt Trifiro, chief marketing officer at Vapor IO. Edge computing is a distributed computing framework that brings computing and data storage closer to devices, reducing the amount of data needed to move around and making responses faster.

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This results in significant improvements in real-time applications such as video streaming, online gaming, and augmented reality — providing users with seamless and immersive experiences. With reduced latency, user interactions become more responsive, edge computing definition enhancing the overall satisfaction and usability of mobile applications. They are deployed, for example, in 5G networks and are capable of hosting applications and caching content close to where end-users are doing their computing.

Examples of edge computing

No matter which variety of edge computing interests you — cloud edge, IoT edge or mobile edge — be sure that you find a solution that can help you accomplish the following goals. Using the edge as a deployment and encryption point provides digital businesses, including those in commerce, banking, retail, gaming, travel, healthcare, government, and other sectors where fraud runs rampant, with a number of benefits. Food and beverage companies leverage edge computing because of its real-time nature, Andersen says. Today, almost every business has job functions that can benefit from the adoption of edge AI. In fact, edge applications are driving the next wave of AI computing in ways that improve our lives at home, at work, in school and in transit. But there will be balancing factors, the most important of which has to do with maintenance and upkeep.

  • Sending all that device-generated data to a centralized data center or to the cloud causes bandwidth and latency issues.
  • Enterprises improve operational and employee productivity by responding more quickly to information.
  • One disadvantage of cloud edge computing is that it can introduce additional complexity to the network.
  • This efficiency is critical for businesses and applications that rely on real-time data access.

The military uses industrial PCs for video processing, data acquisition, and for in vehicle applications. Furthermore, the ruggedness of rugged edge computers allows them to withstand deployment in vehicles where they are subjected to frequent shock and vibration, dust, debris, and extreme temperatures. The rugged design and build quality allows them to operate reliably and optimally without interruption. For example, the fanless design of rugged edge computers allows them to withstand exposure to dust and small particles since the system is ventless because there is no need to circulate air to cool down the system. Systems are passively cooled via the use of heatsink, transferring heat away from the internal components to the outer enclosure of the system. Telecom providers are the next significant adopters of edge computing, aiming to converge cloud, compute, and connectivity for the edge.

Edge Computing Basics

Within manufacturing, edge computing improves the efficiency of production while simultaneously creating a safer environment for workers. Logistics service providers leverage IoT telematics data to realize effective fleet management operations. Drivers rely on vehicle-to-vehicle communication as well as information from backend control towers to make better decisions.

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ATMs using biometric authentication can process facial or fingerprint data in real time and immediately alert authorities if fraud is detected. Plus, since user data would be stored locally with edge devices, this prevents possible data loss from having to transfer banking data to data centers. Edge devices help collect and filter data on-device or through local edge servers and transmit only the most necessary data to data centers situated atop the core network (like the cloud and data warehouses). These edge devices can include motion sensors, smart cameras, smart thermometers, robots, drones and other IoT sensors. Data is generated or collected in many locations and then moved to the cloud, where computing is centralized, making it easier and cheaper to process data together in one place and at scale.

Reduce Bandwidth Requirements

In terms of driving decisions, this delay can have significant impact on the reaction of self-driving vehicles. Edge computing’s decentralized nature means one compromised edge device doesn’t affect data on all other devices. Extra security measures can also be implemented directly on edge devices like firewalls or intrusion detection systems. Low data latency, less data traffic and spending less time transmitting data between networks means quicker response times on software applications.

Not all data collected by edge sensors is sent to data centers, which reduces data management needs, transmission costs and costs needed to process and store data in the cloud. These days, there’s a good chance that everything from the light bulb in your kitchen to the car in your garage is “connected.” That never-ending — and always-increasing — stream of data processing jobs means heavy strain on data centers. Edge computing eases that burden by moving some of the processing closer to its point of origin — as close as possible to where the action occurs.

Improved data security

For the longest time, centralized cloud computing has been a standard in the IT industry and continues to be the undisputed leader. A predecessor to edge, cloud computing is a huge tool for storing and processing computer resources in a central data center. On the other hand, edge computing is a distributed model that is most likely to be used by those applications and devices that require quick responses, real-time data processing, and key insights. At its most basic level, edge computing brings computation and data storage closer to the devices where it’s being gathered, rather than relying on a central location that can be thousands of miles away.

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As a result, the CPU and wireless transmitter spend less time in active mode and consume less power, leading to improved battery life. Latency, the time delay between initiating an action and receiving a response, significantly impacts the performance of mobile devices. During data transmissions, latency occurs when establishing a secure connection and exchanging information with the cloud. This delay degrades the end-user experience and drains the device’s battery power. One disadvantage of cloud edge computing is that it can introduce additional complexity to the network. This is because data must be routed to the appropriate location for processing, which can require extra infrastructure and management.

Are you following a Cloud First or Cloud Smart initiative?

Edge computing works by bringing computation and storage closer to the producers and consumers of data. Edge deployments vary for different use cases, but can be grouped into two broad categories. Accelerate data monetization to extend applications and models to the edge for real-time insights, without the need to move your data. Dryad’s Silvanet solution detects fires within the first hour by applying machine learning to solar-powered gas sensors, detecting hydrogen, carbon monoxide, and volatile organic compounds, he says. “At Dryad, we provide ultra-early detection of wildfires using solar-powered gas sensors in a large-scale IoT mesh network placed in the forest,” Brinkschulte says.

A current use of edge computing that saves companies both time and labor is predictive maintenance. This form of edge computing technology depends on devices or software that monitor the performance of an asset. If something is on the verge of malfunctioning, data transmitted through the edge computing system can help detect the potential issue ahead of time. According to Gartner, approximately 10% of data generated by enterprises is processed or produced outside a central data center or cloud—or at the edge of a network. Particularly for use cases that involve AI voice assistance capabilities, the technology needs go beyond computational power and data transmission speed.

Edge computing use cases and examples

This is done so that data, especially real-time data, does not suffer latency issues that can affect an application’s performance. In addition, companies can save money by having the processing done locally, reducing the amount of data that needs to be sent to a centralized or cloud-based location. Edge application services reduce the volumes of data that must be moved, the consequent traffic, and the distance that data must travel. Edge computing has emerged as a powerful solution for minimizing latency and battery consumption in mobile devices. By bringing computation and storage closer to the source of data generation, edge computing reduces the time and energy required for data transmission to and from the cloud. Edge computing addresses the issue of latency by bringing computation and data storage closer to the source of data generation.