Top Use Cases and Benefits of Edge Computing

Millisecond decision making is a requirement for autonomous vehicles because if vehicles cannot react fast enough to their environment, they will collide with other vehicles, humans, or other objects. To ensure that the vehicles function seamlessly, these autonomous vehicles need to collect and process their data around multiple parameters such as direction, speed, location, traffic congestion, and many more. This requires every vehicle to operate as an edge device with strong computing capability.

Recent strides in the efficacy of AI, the adoption of IoT devices and the power of edge computing have come together to unlock the power of edge AI. The Fortinet Next-Generation Firewall (NGFW) solution, FortiGate, brings security to every edge by inspecting incoming and outgoing traffic for threats and unauthorized users. As the cloud edge expands, secure access becomes more and more necessary, particularly because more cloud-enabled devices and cloud users considerably expand edge device examples the attack surface. Leverage an edge computing solution that nurtures the ability to innovate and can handle the diversity of equipment and devices in today’s marketplace. Currently, network operator EE is investigating the potential for these types of services in collaboration with Wembley Stadium, the national soccer stadium of the UK. Edge computing is transforming how data generated by billions of IoT and other devices  is stored, processed, analyzed and transported.

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These devices can thus translate between the protocols or languages that local systems use into the protocols where data is further processed. The decentralized character makes the system much more robust since the individual edge nodes can operate autonomously and provide offline capabilities. Hence, mission-critical AI applications depend on highly reliable infrastructure that only edge computing can provide.

It includes routers, switches, wide-area networks (WANs), firewalls, and integrated access devices (IADs). Another example of an edge computing device is in the area of security, particularly worker safety. This ensures that there is no unauthorized access to the site and monitors the safety policies followed by employees.

Edge Computing Examples

Increasingly, though, the biggest benefit of edge computing is the ability to process and store data faster, enabling more efficient real-time applications that are critical to companies. Before edge computing, a smartphone scanning a person’s face for facial recognition would need to run the facial recognition algorithm through a cloud-based service, which would take a lot of time to process. With an edge computing model, the algorithm could run locally on an edge server or gateway, or even on the smartphone itself. One of the challenges for edge computing solution providers and infrastructure developers is determining the processing capabilities within the edge server. Some use cases that require heavy visual data processing or image rendering will need GPUs (Graphics Processing Unit). Others that need high performance computing or low latency, high throughput processing may require specialised hardware accelerators, such as FPGAs (Field Programmable Gate Arrays) or ASICs (Application-specific Integrated Circuits).

In simple terms, edge computing is the processing of data closer to the source where the data is stored or processed, particularly where there is an IoT device. Edge computing can easily be processed using sensors, routers, or gateways that are connected to the devices to communicate data. Alternately, these devices are connected through a set of small servers that are installed on-premise in a small cluster. Using edge computing the gigabytes of sensory and special data is analyzed, filtered and compressed before being transmitted on IoT edge Gateways to several systems for further use.

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Applications such as virtual and augmented reality, self-driving cars, smart cities and even building-automation systems require this level of fast processing and response. For instance, with the help of the NVIDIA EGX platform, Walmart was able to compute in real-time over 1.6 terabytes of data generated a second. The platform leverages the potential of AI for a wide range of tasks such as opening new checkout lanes, automatically alerting employees to restock products, or fetching shopping carts. Many industries, such as medical, pharmaceutical, defense, and aerospace, have tight regulation and compliance requirements.

• Another example of an edge computing device is in the area of security, particularly worker safety.
• An edge device is a device that provides an entry point into enterprise or service provider core networks.
• Without an edge device, these types of data would be incompatible and unable to reach cloud services for deep analysis.
• If the data is informational or transactional, the device may send it directly to the cloud for storage.
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Real-time Safety monitoring is of the utmost importance for critical infrastructure and utilities like oil and gas. With this safety and reliability in mind, many cutting edge IoT monitoring devices are still being developed in order to safeguard critical machinery and systems against disaster. From a security standpoint, data at the edge can be troublesome, especially when it’s being handled by different devices that might not be as secure as centralized or cloud-based systems. As the number of IoT devices grows, it’s imperative that IT understands the potential security issues and makes sure those systems can be secured. This includes encrypting data, employing access-control methods and possibly VPN tunneling.

What is an edge server?

For example, a small enclosure with several servers and some storage might be installed atop a wind turbine to collect and process data produced by sensors within the turbine itself. As another example, a railway station might place a modest amount of compute and storage within the station to collect and process myriad track and rail traffic sensor data. The results of any such https://www.globalcloudteam.com/ processing can then be sent back to another data center for human review, archiving and to be merged with other data results for broader analytics. Traditional edge devices transfer data over a secure network with little or no processing capability. Intelligent edge devices are smart devices that can perform edge computing tasks near the data source for industrial automation.

If we look at the most sophisticated application, the New York City traffic management system is where we see the use case of edge computing devices. It can perform functions such as adjusting the timing of traffic signals and opening and closing extra traffic lanes on an ad-hoc basis. Here if we notice, through edge computing, traffic is managed intelligently and on a real-time basis to avoid any sort of congestion. Banks may need edge to analyze ATM video feeds in real-time in order to increase consumer safety. Mining companies can use their data to optimize their operations, improve worker safety, reduce energy consumption and increase productivity.

Security

Industrial IoT has added millions of connected devices in manufacturing plants and other such industries to gather data on production lines, equipment performance and finished products. However, all the data doesn’t need to be handled in centralized servers — every temperature reading from every connected thermometer isn’t important. In some cases, moving data to the centralized servers — whether in the cloud or on premises — could be prohibitively expensive or impossible because of a facility’s remote location.

The biggest difference between edge computing and cloud computing surrounds centralization. Whereas cloud computing is centralized, like in the “core” of a network, edge computing is decentralized in order to empower use on the edge. Rugged industrial computers are often used for military applications on the ground, at sea, and in the air. The military uses a wide variety of industrial PCs that include rack-mount military computer, industrial touchscreen PCs, embedded PCs, and wall mount computers. Rugged industrial computers are designed and built to endure the challenging environments in which they are deployed. The military uses industrial PCs for video processing, data acquisition, and for in vehicle applications.

What is an Edge Device and Why Is It Essential for IoT?

These AI applications would be impractical or even impossible to deploy in a centralized cloud or enterprise data center due to issues related to latency, bandwidth and privacy. Since the internet has global reach, the edge of the network can connote any location. It can be a retail store, factory, hospital or devices all around us, like traffic lights, autonomous machines and phones. In the past, the promise of cloud and AI was to automate and speed innovation by driving actionable insight from data. But the unprecedented scale and complexity of data that’s created by connected devices has outpaced network and infrastructure capabilities. The influx of IoT devices and massive amounts of live data necessitate preprocessing and filtering closer to the devices, before these thousands of data streams can hit the core/cloud networks.