Edge automation: Seven industry use cases and examples

The nature of an edge server differs across different types of edges, depending on the use case and where the edge compute resource is deployed. For example, companies can use locational data from on-site employees to enforce the social distancing requirements brought on by the COVID-19 pandemic, alerting them if they move and stay too close together. Because such locational data has no value beyond that moment, the information can be collected https://www.globalcloudteam.com/ and processed on the edge rather than moved and stored in the corporate data center. The edge is also a device that empowers people to connect to their online and physical worlds by providing on-demand access to information. It is the first step in a chain of technologies that link people through the physical world. IoT cameras numbering hundreds or even thousands can feed AI image recognition models processed on stores by NVIDIA EGX.

• That’s a lot of work and would require a considerable amount of in-house expertise on the IT side, but it could still be an attractive option for a large organization that wants a fully customized edge deployment.
• Edge computing is a distributed information technology (IT) architecture in which client data is processed at the periphery of the network, as close to the originating source as possible.
• With the number of IoT devices increasing every year, it is a natural choice for IoT solution architects to make use of IoT edge computing devices to provide the most optimal and cost-effective IoT solutions.
• An edge device is an external add-on that adds new functionality to a computer, phone, tablet, or other electronic devices.
• If an enterprise has thousands of IoT devices, it’s not efficient for them all to try to communicate with the same resource at once.

However, with the higher speeds offered by 5G, particularly in rural areas not served by wired networks, it’s more likely edge infrastructure will use a 5G network. Enterprise networking has evolved in the last decade, moving away from proprietary appliances, to universal platforms that allow for flexibility and choice for how to manage enterprise network services and functions. Question is – can these be extended to provide edge computing for non-network services? This article evaluates the opportunity and provides examples for companies innovating in the edge uCPE space. A server is hardware on which edge software workloads run – this could be both on customer premises or at a site within a telecoms operator’s network. A device tends to refer to the piece of hardware or end-point typically owned by the end-customer.

Network Edge

On the other hand, edge computing provides feasible, scalable, low-latency computing. As a result, information — particularly real-time information — does not suffer latency issues that can affect the performance of an application. Moreover, businesses and organizations can save money by getting the data processing done locally. This will reduce the amount of data that needs to be processed in a cloud-based location. Edge computing is transforming and revolutionizing how data is being managed, processed, and delivered from billions of devices worldwide. The huge increase in the number of IoT data sources (cameras, sensors, embedded systems, personal devices) will give rise to network-edge computing.

At viso.ai, we’ve built a highly scalable, end-to-end computer vision platform that provides an integrated edge device management to enroll and remotely manage a large number of edge devices, fully automated. The visual no-code editor allows to rapidly wire together AI models and cameras using an intuitive workflow builder. Application versions can be managed and deployed to edge device fleets at the click of a button.

Examples of Edge Computing Success across a Wide Range of Industries

Lastly, telcos and OEMs are exploring changing existing customer premises equipment used for networking to host non-networking applications. These could either be enterprise CPE boxes, Wi-Fi gateways or programmable logic controllers in industrial settings. At its basic level, edge computing is a technology that brings data storage and computation closer to the hardware where its collection is taking place, rather than depending on a central location located several miles away.

However, cloud computing requires network connectivity, increases latency over local computing, and requires reliance upon 3rd party security. Edge computing, on the other hand, provides low-latency, reliable computing that can be deployed in areas with no network connections or in extreme security conditions where 3rd-party security is disallowed. However, data can become incomplete due to the higher cost of storage, and local computing has higher overall maintenance than cloud computing because it must be managed in-house. Edge computing and edge computing infrastructure create a local edge layer where a great deal of functionality for automation and response can be conducted near the source, reducing latency and bandwidth constraints. Built-in processors and onboard analytics do much of the work that would’ve been sent to the cloud.

Examples of Edge Computing (11 Examples)

Consequently, onboard computing power and edge data centers are needed for mission-critical processing for navigation, vehicle-to-vehicle communications and integration with emerging smart cities. Popular Edge Devices include embedded computing platforms such as the Intel NUC or SoC computers. There is no definitive form factor, hence edge computers can also be edge servers, mobile devices, or essentially any desktop computing device with regular or embedded hardware (on Intel, AMD, or ARM platforms). Since there is no narrow definition, edge devices can be IoT computing devices, smart cameras, embedded computers, mobile devices, and even smart TVs or other connected devices. However, any edge device needs some sort of computing capabilities, and a sensor itself or a low-power IoT chip are not considered an edge device. But the number of devices connected to the internet, and the volume of data being produced by those devices and used by businesses, is growing far too quickly for traditional data center infrastructures to accommodate.

Edge devices are a fundamental component of modern, distributed real-world AI systems. In the early days, IoT devices could only collect and send data to the cloud for analysis. However, the increasing computing capacity of today’s devices allows them to perform complex computations on-device, resulting in edge computing. As a result, edge computing extends cloud computing capabilities by bringing services close to the edge of a network and thus supports a new variety of AI services and machine learning applications. Smart factories use advanced cloud-based machine data platforms with edge networks, edge computing, and edge devices to drive advanced automation and improve business practices.

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By processing data locally, the amount of data to be sent can be vastly reduced, requiring far less bandwidth or connectivity time than might otherwise be necessary. Computing tasks demand suitable architectures, and the architecture that suits one type of computing task doesn’t necessarily fit all types of computing tasks. Edge computing has emerged as a viable and important architecture that supports distributed computing to deploy compute and storage resources closer to — ideally in the same physical location as — the data source. In general, distributed computing models are hardly new, and the concepts of remote offices, branch offices, data center colocation and cloud computing have a long and proven track record. Leveraging edge computing offers certain benefits that can’t be achieved by cloud computing, alone. In particular, putting the computing power at the edge helps to reduce latency and provide data processing at the source, not potentially many miles away.

Edge computing offers a powerful strategy to help alleviate future network congestion driven by new technologies. Sports stadiums, Concerts and other localized events rely heavily on live video streaming and analytics to create and increase revenue streams. Smart Grids, as we now know them, essentially work by establishing two-way communication edge device examples channels between power distribution infrastructure, the recipient consumers (residential households, commercial buildings, etc.) and the utility head-end. This is done by using the tried and proven wide-area network (WAN) internet protocols. It’s estimated that the number of IoT devices will increase by 24 billion by the end of 2030.

What are some examples of edge computing?

Or, they can be tasked to identify quality and strength requirements for military and aerospace parts.

Instead of one video camera transmitting live footage, multiply that by hundreds or thousands of devices. Not only will quality suffer due to latency, but the bandwidth costs can be astronomical. U.S. private industry employers reported 2.1 million nonfatal workplace injuries in 2020, according to the federal Bureau of Labor Statistics (BLS). There were 5,333 deaths due to work-related injuries in 2019, the most recent BLS figures. But industry is using a combination of technologies — such as endpoint sensors, computer vision and artificial intelligence, as well as edge devices — to power workplace safety applications.

Examples of Edge Computing Solutions in Use Today

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. Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center. This traffic includes the content of websites as well as communications like video chat, email, and Voice over Internet Protocol (VoIP) transmissions. Routers direct traffic on the internet, sending it from one point to another, allowing different edge devices to communicate with each other.