How To Explain Edge Computing In Plain English

Transmitting and processing massive quantities of raw data puts a significant load on the network’s bandwidth. According to the Gartner Hype Cycle 2017, edge computing is drawing closer to the Peak of Inflated Expectations and will likely reach the Plateau of Productivity in 2-5 years. Considering the ongoing research and developments in AI and 5G connectivity technologies, and the rising demands of smart industrial IoT applications, Edge Computing may reach maturity faster than expected. Edge computing is computing that’s done at or near the source of the data, instead of relying on the cloud at one of a dozen data centers to do all the work. Challenges around device capabilities — including the ability to develop software and hardware that can handle computational offloading from the cloud — are likely to arise.

What is meant by edge computing

Doctors can then evaluate patients on the spot and provide on-demand feedback about their health. Experts say collecting data from smart wearable devices may prove highly useful in cases of a pandemic, where faster data processing near the source has the potential to be life saving. While autonomous cars are expected to account for a significant amount of this growth, other intelligent transportation systems such as automated mass transit are likely to see substantial growth in the coming years.

More than 1.7M users gain insight and guidance from Datamation every year. An edge framework introduces flexibility, agility and scalability that’s required for a growing array of business use cases. For example, a sensor might provide real-time updates about the temperature a vaccine is stored at and whether it has been kept at a required temperature throughout transport. And while some use cases may prove the value of edge computing what is edge computing with example more clearly than others, the potential impact on our connected ecosystem as a whole could be game-changing. As we move towards a more connected ecosystem, data generation will continue to skyrocket, especially as 5G technology takes off and enables faster connections. Quicker computing near the source of the transaction also allows banks to experiment with services geared towards providing increased convenience to users.

Edge deployments need the capabilities to support both application types on a single infrastructure, and they need a flexible storage solution that can accommodate a broad set of applications with rich data services and native block and file services. Edge infrastructure can span thousands of deployments, so organizations need a solution that provides remote, centralized management with zero touch provisioning. Organizations are often concerned with security at edge locations, so they need an infrastructure with intrinsic security throughout. Finally, edge deployments often run on small form factor hardware in environments that don’t have traditional data center infrastructure. The ability to use alternative connectivity, for instance, with wi-fi or cellular networks, is needed.

Jump On 2022 Hyperconverged Infrastructure Trends

From wearables to vehicles to robots, IoT devices are gaining momentum. Cashierless checkout tech startup Zippin reports that its cameras don’t use facial recognition, instead relying on edge computing to discern more general characteristics of shoppers. Edge computing is ideal for agriculture, given the often remote locations and hostile conditions of farms that may present bandwidth and connectivity concerns. The company’s plan to spend $5B in IoT by 2022 involves its edge computing initiatives. Internet of Things devices requires a high response time and considerable bandwidth for proper operation.

What is meant by edge computing

Although conventional servers, storage, and cloud computing continue to play a key role in computing, edge technologies are radically redefining business and life. By moving data processing at or near the source of data generation, edge devices become smarter and they’re able to handle tasks that would have been unimaginable only a few years ago. This data fuels real-time insights and applications ranging from sleep tracking and ridesharing to the condition of a drilling bit on an oil rig. Customers are using edge deployments for a wide variety of applications, from traditional enterprise applications for office work to modern applications, such as AI/ML.

What Is Edge Computing And What Makes It Unique?

This paper describe our contribution that enables the composition of virtual devices from physical devices, sensors, and actuators available on the network. Moreover, we present a model of application offloading and virtual devices networking on mobile clouds. In addition to the formal specifications and architecture this paper presents a case studies showing the structural congruence between a locally executed application and an offloaded version of that same application. At the same time, edge computing spreads storage, processing, and related applications on devices and local data centers. In some cases, IoT devices might process data onboard, or send the data to a smartphone, an edge server or storage device to handle calculations.

What is meant by edge computing

Earlier in 2020, the company announced its expansion with Nvidia’s T4 Tensor Core GPU to bring machine learning to the edge. Microsoft acquired IoT/OT security firm CyberX for approximately $165M in June 2020, an acquisition that will allow Microsoft to further develop the security protocols of Azure in industrial settings, such as increasingly automated factories. Across healthcare, wearables are increasingly important for collecting patient data. With the uptick in patient data in hospitals, experiencing even the smallest delay in processing can be a matter of life or death.

Deemphasizing the cloud’s role also decreases the potential for having a single point of failure. Reliability – with the operation proceedings occurring close to the user, the system is less dependent on the state of the central network. Known patterns like “toothbrushes and toothpaste being bought together” then go to the central cloud and further optimize the system. In addition to this, the constant movement of large quantities of data back and forth is beyond reasonable cost-effectiveness.

How To Explain Edge Computing In Plain English

The HIPAA-compliant solution can also be updated via firmware, which could offer healthcare systems significant cost savings by prolonging the shelf life of IT hardware. Doctors and clinicians would be able to offer faster, better care to patients while also adding an additional layer of security to the patient-generated health data . The average hospital bed has upwards of 20 connected devices, generating a considerable amount of data. Instead of sending confidential data to the cloud where it could be improperly accessed, it would happen closer to the edge. But it’s not just autonomous vehicles that generate a significant amount of data and require real-time processing. It’s also planes, trains, and other forms of transportation — driverless or not.

  • Edge computing should allow for greater, quicker insight generated from big data, and a greater amount of machine learning to be applied to operations.
  • Others, such as AWS, Microsoft Azure and Google Cloud, deliver cloud-based software and services that support IoT and edge functionality—including device management, machine learning and specialized analytics.
  • Quicker computing near the source of the transaction also allows banks to experiment with services geared towards providing increased convenience to users.
  • IoT operation combines data processing on the spot and subsequently on the cloud .
  • As a large number of banking services have gone digital, financial institutions are using cloud computing capacities at a much larger level.

Advertise with TechnologyAdvice on Datamation and our other data and technology-focused platforms. Even so, as adoption picks up, there will be more opportunities for companies to test and deploy this technology across sectors. Oil giant Saudi Aramco’s Energy Ventures arm and GE Ventures have both invested in IoT security startup Xage, which uses blockchain to distribute the authentication necessary to access edge entry points. The end goals are capitalizing on the untapped value of the massive amount of data being created, preventing safety hazards, and lessening disruptions on the factory floor.

A Changing Computing Landscape

The two technology platforms are not oppositional; they are complementary. Edge computing fundamentally rewires and revamps the way organizations generate, manage and consume data. Gartner estimates that by 2025, 75% of data will be created and processed outside the traditional data center or cloud. Furthermore, data collected at the edge, including credit card details and identifiable-traits shared with stores, does not need to be stored in a central server and can be automatically forgotten by devices after processing. For retailers shifting focus to autonomous store development, edge computing can be a vital component of their in-store technology. Retail is expected to become the fastest-growing economic sector in terms of edge computing deployments by 2022, suggesting significant potential for these technologies to improve the shopping experience for consumers and retailers alike.

The Commonwealth Bank of Australia also embarked on a similar venture in 2019 when it began working with Australian telecommunications company Telstra and Ericsson to implement 5G-enabled edge computing technologies across its branches. Additionally, the tech may become more popular as financial services increasingly rely on biometrics. For example, edge computing makes it possible to capture — and forget — facial recognition data on a specific device, rather than having to send this back to a database filled with pictures of users. While edge computing promises to revolutionize the shopping experience for consumers, retailers may see even greater benefits, driven primarily by advances in real-time and predictive behavioral analytics.

Both Ford and GM are currently developing and trialing autonomous vehicles, but it will likely be several years before Level 4 and 5 vehicles enter mainstream commercial production for the consumer market. In the private market, for example, Dell and Intel have invested in Foghorn, an edge intelligence provider for industrial and commercial IoT applications. Both companies participated in Foghorn’s latest $25M Series C round in February 2020. Dell has also participated in a seed and Series A round to IIoT edge platform IOTech. While edge computing refers more specifically to the computational processes being done at or near the “edge” of a network, fog computing refers to the network connections between the edge devices and the cloud.

Benefits And Challenges Of Edge Computing

In 2006, the cost of manufacturing downtime in the automotive industry was estimated at $1.3 million per hour. A decade later, the rising financial investment toward vehicle technologies and the growing profitability in the market make unexpected service interruptions more expensive in multiple orders of magnitude. For autonomous driving technologies to replace human drivers, cars must be capable of reacting to road incidents in real-time. On average, it may take 100 milliseconds for data transmission between vehicle sensors and backend cloud datacenters. In terms of driving decisions, this delay can have significant impact on the reaction of self-driving vehicles. Today, edge computing takes this concept further, introducing computational capabilities into nodes at the network edge to process information and deliver services.

The decentralized infrastructure of edge computing requires additional monitoring and management systems to handle data from the edge. “Edge computing” is a type of distributed architecture in which data processing occurs close to the source of data, i.e., at the “edge” of the system. This approach reduces the need to bounce data back and forth between the cloud and device while maintaining consistent performance. 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. Locations of low connectivity and signal strength are limited in terms of the speed and volume of data that can be transmitted between vehicles and backend cloud networks.

Edge computing could also offer significant improvements in the efficiency and response times of emergency services vehicles and waste collection, as well as logistics route management and real-time traffic monitoring. This also places more responsibility on the hardware underlying edge computing technology, which consists of sensors for collecting data and CPUs or GPUs for processing data within connected devices. Given its broad range of applications, from helping autonomous vehicles speed up reaction times to protecting sensitive health data, the edge computing infrastructure market is projected to be worth $450B, according to CB Insights’ Industry Analyst Consensus.

Cloud Computing Enables A Connected World

Edge computing is already in use all around us – from the wearable on your wrist to the computers parsing intersection traffic flow. Other examples include smart utility grid analysis, safety monitoring of oil rigs, streaming video optimization, and drone-enabled crop management. Let’s example considering a trucking company, the main goal is to combine and send data from multiple operational data points like wheels, brakes, battery , etc to the cloud. Thus,essentially a fleet management solution encourages the vehicle to lower the cost. It has the ability to process data without even putting on a public cloud, this ensures full security.

In order for IoT devices to deliver real value, there must be a way to connect the edge to the cloud and corporate data centers. Sensors and edge IoT devices can track traffic patterns and provide real-time insights into congestion and routing. And motion sensors can incorporate AI algorithms that detect when an earthquake has occurred to provide an early warning that allows businesses and homes to shut off gas supplies and other systems that could result in a fire or explosion. Intel- and Samsung-backed edge infrastructure startup Pixeom offers a suite of edge services, including financial services solutions that help analyze global markets data. Spanish financial institution BBVA said it is the country’s first financial institution to implement a private 5G network at its headquarters and is also developing its own edge computing platform. BBVA believes this proprietary platform will enable it to offer consumers greater customization of specific financial products and detect fraud more effectively.

That’s why Microsoft is working on Azure Sphere, which is a managed Linux OS, a certified microcontroller, and a cloud service. The idea is that your toaster should be as difficult to hack, and as centrally updated and managed, as your Xbox. Data might get corrupt while on an extended network thus affecting the data reliability for the industries to use.

GE Digital partner, Intel, estimates that autonomous cars, with hundreds of on-vehicle sensors, will generate 40 TB of data for every eight hours of driving. The car immediately response to the events which has valuable data when coupled into digital twin and performance of other cars of its class. Essentially, the edge network feeds data to the main network—and pulls data from it as needed. Early edge networks encompassed content delivery networks that helped speed video delivery to mobile devices.

And as more connected devices become available, edge computing will see increasing applications across industries, especially as cloud computing proves inefficient in some cases. The same players that have been dominant in cloud computing are emerging as edge computing leaders. The significant financial resources and extensive proprietary network infrastructures of these companies ideally position them to capitalize on this significant shift in computing technologies and diversify their existing cloud service offerings. Localizing data processing and storage puts less of a strain on computing networks.

For instance, if you buy one security camera, you can probably stream all of its footage to the cloud. But if the cameras are smart enough to only save the “important” footage and discard the rest, your internet pipes are saved. Voice assistants typically need to resolve your requests in the cloud, and the roundtrip time can be very noticeable.

Processing all this data through a centralized cloud would be more expensive and time-consuming. The technology’s high bandwidth, low latency potential could also make remote surgical procedures significantly more viable by reducing the delay between physician input and robotic surgical implements considerably. AT&T hopes to deploy its MEC edge computing solution to VA facilities across the U.S. upon completion of the trial, which could benefit more than 9M veterans nationwide. Verizon is also developing 5G-enabled edge computing technologies at its 5G Lab in Cambridge, MA, that minimize latency between the surgeon and robotic operator. Despite significant advances in edge computing and other autonomous vehicle technologies, fully automated vehicles remain highly ambitious.

This enables analytics and machine learning on the edge, the ability to isolate devices, manage traffic patterns more effectively, and connect the gateway to other gateways, thus establishing a larger and more modular network of connected devices. The consumer experience on retail shop floors is also undergoing radical changes thanks to edge computing technologies. In July 2019, AT&T partnered with commercial robotics specialist Badger Technologies to deploy autonomous robots in retail environments.

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