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Edge Computing, Smarter Technology for the Future


By NIIT Editorial

Published on 10/12/2020

8 minutes

Introduction to Edge Computing

Edge Computing is the computational processing of sensor data that is distanced from the centralized nodes while being close to the logical edge network towards individual sources of data. Commonly cited as a distributed IT network architecture, edge computing also aids mobile computing for locally produced data. The edge computing network disperses the processing power to ensure real-time processing without latency rather than distributing the data to cloud data centers. The bandwidth and storage requirements on the network are reduced during the process. 

Edge Computing amplifies the communication speed while enabling the end-users to enjoy the analytical computational resources. A traditional cloud-based system is surpassed by a well-designed edge platform that significantly outperforms it in a lot of respects. Edge computing is a remarkably more viable option than cloud computing because of the short response time some applications rely upon. Certain applications that involve human perception such as facial recognition taking a human 370-620 ms to perform are outperformed by Edge Computing. In applications like augmented reality requiring headset recognition, chances are highest that edge computing can replicate the same perception speed just like humans.

Why is Edge Computing Important?

Operational efficiency, improved performance, and safety – these are the three imperative factors that industrial and enterprise-level business concerns can maximize because of Edge Computing. Not only this, but Edge Computing also aids to automate all the core business processes, and guarantee availability all the time. All in all, it is a principal method to attain digital transformation in the business world. And we can sum up the importance of edge computing with the following points:

  • New functionalities are disclosed
  • Easy to configure
  • Increased hacking potential
  • Reduction of load on the network
  • Application Programming Interface
  • Increasing extensibility
  • Centralized management
  • Licensing costs
  • Supports and updates

Some Characteristics of Intelligent Edge Computing

Open Architectures

Though proprietary protocols and closed architectures have existed in edge environments for years, they have been proved to cause high integration and switching costs as vendors lock-in their customers. Whereas, the modern and smart edge computing resources arrange the open architectures in such a way that they leverage the standardized protocols. For example, Protocols like OPC UA, MQTT and semantic data structures like Sparkplug help in reducing the integration costs along with increasing vendor interoperability.

Data Pre-processing and Filtering

Since transmitting and storing the generated data by edge computing resources in the cloud can turn out to be costly as well as inefficient. Intelligent edge computing resources have the ability to pre-process the data at the edge and will only send relevant information to the cloud, this will lead to a reduction of data transmission and storage costs. An intelligent edge computing device running an edge agent that pre-processes data at the edge before sending it to the cloud is one of the best examples of data pre-processing and filtering.

Edge Analytics

While most of the legacy edge computing resources possess limited processing power and are able to perform only one particular task or function, the intelligent edge computing resources have better processing capabilities that are designed to analyze the edge’s data. Then these edge analytics applications allow the new use cases that rely on low-latency and high data throughput. For instance, ARM-based intelligent sensors are used by Octonion in order to create collaborative learning networks at the edge. These networks aid in sharing the knowledge amid the intelligent edge sensors while allowing the end-users to create predictive maintenance solutions that are based on advanced anomaly detection algorithms.

Distributed Applications

The applications that are run by legacy edge computing devices are generally tightly fixed to the hardware. The applications are then de-coupled by the intelligent edge computing resources from the underlying hardware. This de-coupling helps the applications to move both vertically from the intelligent edge computing resource to the cloud, and horizontally like from one intelligent edge computing resource to another. Basically, there are three types of architectures: 100% edge architectures, thick edge + cloud architectures, and thin/micro-edge + cloud architectures.

All in all, for the 3 edge architectures to run properly, it is important to use modern edge applications. Two examples of modern edge applications that are known to render more flexibility when designing edge architectures are Lightweight Edge “agents” and Containerized Applications.

How are IoT and Edge Computing Related?

Edge Computing addresses challenges like network congestion and latency that are faced by IoT. For this reason, IoT and edge computing are parallel. As soon as 5G matures, they are likely to become the core support for IoT devices. One of the chief characteristics in the entire 5G architecture is the capabilities of Edge Computing in areas like network nodes and especially in small cell base stations located in dense urban areas.

How Edge Computing is Smarter?

There is a notable difference between IoT edge computing and non-IoT edge computing. Both have diverse demands and considerations. Generally, IoT devices comprise limited data processing and storage capabilities. With that being said, substantial data processing has to occur off the device, while the edge creates an environment to undertake this processing and manage large volumes of IoT devices and data. This will, in turn, lead to a reduction of device cost because of several functions that have to be off-loaded to the edge. And, the edge’s position also influences the various possibilities that will differ according to the use case. For instance: Be it the operator’s local data center or regional data center, the edge for IoT could reside in either of them. 

Depending on the nature of the service required by the customer, the position of the edge for IoT can be customized. However, services needing the lowest latency will be requiring an entirely different edge as compared to the ones with less crucial data processing requirements.

There are several potential benefits for many IoT deployments, such as decreased response time and increased communications efficiency, because of the edge for the IoT. 

For example, various IoT processes might have a high level of automation at the edge resulting in low latency for rapid data processing. Then, only the vital information has to be sent to the cloud to conduct further action or investigation. Numerous new IoT services like intelligent vehicles, drones, or smart grids, are relying on edge computing. Several aids of IoT edge will have to be refined in the future to be presented as a proof-of-concept for deployments by mobile operators that have to depict that the model is highly beneficial. 

Below-listed are some of the benefits that IoT edge computing encompasses of:

 

  • Longer battery life for IoT devices
  • Higher efficient data management
  • Access to data analytics and AI
  • Resilience
  • Scalability

Following are some of the edge computing use cases, have a look:

In Healthcare: As discussed earlier, edge enables us to manage our connectivity and disperse processing nearer to the data. This added advantage is a progression when we optimize some part of our stack in the network while giving it more localized services for our application. Healthcare organizations that want to benefit from inculcating digitalization to eradicate the healthcare problems, shifting the analysis of clinical information to edge computing is imperative. 

For instance, in the hospital, if we collect data from IoT devices, that is used in monitoring the patient’s health and transfer it to the trust’s electronic health record (EHR) from the bedside, with the authentication of staff to the IoT devices through proximity cards.

Virtualized radio networks and 5G (vRAN):

Operators, nowadays, are gradually moving towards virtualizing parts of their mobile networks (vRAN). It includes both cost and flexibility benefits. The new vRAN hardware has to perform more complex processing with low latency. Then only the operators will need edge servers to support the virtualization of their RAN close to the cell tower.

Cloud gaming:

An evolved kind of gaming that streams a game’s live feed directly to the devices and is highly dependent on latency is known as cloud gaming. Cloud gaming companies want to structure edge servers as near as possible to the gamers so that they can cut down the latency and offer a responsive and immersive gaming experience in its entirety.

Traffic management:

 Edge computing has the potential to allow more effective city traffic management. For example, optimizing bus frequency, given fluctuations in demand, managing the opening and closing of extra lanes, and, in the future, managing autonomous car flows. There is no need to transport large volumes of traffic data to the centralized cloud with edge computing, therefore reducing the cost of bandwidth and latency.

Advantages of Enabling Edge Computing

  • Increased speed 
  • Increased reliability 
  • Reduction of random is reduced.
  • Reduction of the compliance issue 
  • Reduction of hacking issues 
  • Reduction of random issues

According to the Gartner Hype Cycle 2017, edge computing is moving towards the peak of inflated expectations and is anticipated to reach the plateau of productivity in 2-5 years approximately. Observing the ongoing research and developments in AI and 5G connectivity technologies, Edge Computing may reach a pinnacle faster than expected.



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