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Innovation in Edge Computing and Its Relationship to 5G

Innovation in Edge Computing and Its Relationship to 5G

Published on : Jul-2023 Report Code : 18 Report Format : PDF


What is Edge Computing combined with Cloud Computing and How have they changed the Working of Businesses?

Edge computing is basically a distributed IT architecture wherein the client data is processed as near to the original source as is practical at the network's edge. Data processing and edge computing are undergoing a revolution. The requirement for data centres has increased due to the widespread migration of suitable technology to the cloud. Yet, more connected gadgets have also increased workloads and speed up data processing.

Cloud computing has altered how businesses operate over the last ten years by providing micro-services and connectivity that increase productivity and lower expenses. Edge computing has developed and now plays a crucial part in many manufacturers' daily operations, even though it initially appeared to be a fad. Edge computing avoids slower rates and lag related to data-relay processes used in cloud computing or remote data storage by not relying on unneeded data relays.

Let's examine how 5G is developing and how it has a big impact on edge innovation across several industries.

The Edge Network - 4G/LTE

Edge computing can be used with a variety of different connectivity technologies, despite being somewhat associated with 5G. (e.g. LTE, 4G, satellite). By improving latency & reliability, bringing workloads and processing closer to the end user allows for a more effective and intelligent use of already available resources, enhancing the technology's potential. Adopting edge is also cost-critical for organisations. Consumption in centralised data centres is accelerating due to the shift towards cloud-native and virtualized jobs technologies.

There is a tremendous demand for cloud computing, and businesses who run data-intensive applications may have to pay expensive backhaul costs. Edge is more of an expansion of existing technology since it was already a well-established technology prior to the launch of 5G. For more than 20 years, CDNs, for instance, have been processed at the edge of networks. To relieve the load on centralized web servers, streaming platforms like Netflix cache material locally today.

The concept that networks would need to use 5G and 4G across the spectrum to confirm demands can be met has been strengthened by the sluggish deployment of 5G, especially the high-band spectrum. Even with additional 5G installations, 4G will still be able to fulfil edge use cases. By eliminating lengthy buffering times and enabling users to access information while on the go, 4G has played a significant role in the delivery of on-demand video streaming. Customers increasingly anticipate smooth multimedia streaming to their smartphones, laptops, and other gadgets. But by 2025, there will likely be 100 billion connected devices, pushing 4G to its limits.

Edge CDN gives media firms the freedom to install at the network edge rather than within their own locations/infrastructures, allowing them to cache material closer to the end user than standard CDNs (reducing latency). Streaming services that are 4G enabled are already being improved by businesses like Stackpath and Broadpeak, and while the switch to 5G will welcome new capabilities, the use case will mainly remain the same. 4G has also continued to enable use cases in extractives and manufacturing like push-to-X. Workers can better communicate during mission-critical situations through a ‘walkie-talkie’ instrument or through video communication.

A few private networks are frequently used to guarantee complete dependability and security, and edge computing provides the option to handle crucial messages in close to real time. A testing ground for the introduction of 5G-based edge use cases has also been established using 4G. To increase drivers' awareness of potential risks, VicRoads and Lexus teamed together with Telstra to develop connected car driving assistance. This technology uses low-latency cellular technology. After successful 4G installations, their plans will be expanded to include 5G.

As part of a three-year commitment to create innovative industrial use cases, Ericsson and OBS implemented their 5G/4G private cellular networks for ArcelorMittal past year. Use cases from many industries will continue to be provided by 4G edge, which also serves as a testing ground for 5G edge technologies. The case study will discuss how 5G can enable more sophisticated B2B use cases with greater revenue prospects since 4G has its limitations.

Top 3 Edge Computing Advances

New developments in the edge computing domain offers benefits to both potential and current customers. This is only the beginning of a new era intended to increase data efficiency across the business and on the shop floor, from data reduction procedures to edge computing technologies driven by 5G.

  1. Data Reduction Processes

Data reduction techniques enable providers to drastically reduce the amount of storage space needed for data. Data-thinning significantly improves storage and aids in cost savings for businesses. The storage of data following data reduction techniques is referred to by terminology like "raw capacity" and "effective capacity" by cloud edge computing companies.

  1. 5G Connectivity for Edge Computing

Although it has a place at the edge computing table, 5G is mostly utilized to connect mobile devices. With its focus on fostering connectivity across software, equipment, tools, and devices, 5G is set to revolutionise the way edge computing functions today. The right network buddy for edge computing is 5G, which offers extremely fast connection speeds, low latency, and streamlined operations.

  1. Fog Computing

When an edge computing system uses a decentralized computing infrastructure, it is known as fogging or fog computing. The data center is positioned at the network's edge when fogging. The required data, storage, compute, and applications are then arranged in places that permit faster relay and are logically appropriate with communication and data. Fog computing reduces the amount of data sent to the cloud by bringing services of cloud computing to the edge of a network.

How Video Analytics Provoke the Edge Opportunity?

How do horizontal video use cases benefit from 5G & Edge Computing capabilities?

Today, video data is often analyzed in the cloud or on specialized cameras and computers, but both approaches have substantial disadvantages like high bandwidth costs. These problems are solved by moving computing to the edge, which lowers costs and boosts market expansion. Real-time video processing can also be made possible via edge computing and 5G. The following application cases are supported by technical advancements in various industries:

  1. Logistics: Security analytics can direct video camera traffic away from the centralised cloud and towards an edge site. Several industries can benefit from security use cases, which can also help firms cut operating expenses. With enough capacity, 5G can combine video streams from many camera types and use AI to filter out specific occurrences. Incident detection using alarm systems or/and real-time facial recognition can be supported by edge computing.
  2. Manufacturing: Real-time quality inspection using video analytics can ensure accurate results and cut down on flaws. Objects’ manual inspection is error-prone, raises labour expenses, slows cycle times, and strains employees' minds. Reduced latency is necessary to provide quality control and real-time analysis of possible bottlenecks. To keep operations moving along quickly production lines, intake, and analysis must occur almost instantly.
  3. Retail: Retailers can gain insights on how customers, staff, products, and/or users move through their facilities by using video ingest for flow analysis. Actionable insights from this analysis include where to store expensive items, where to divert traffic, or better crowd tracking and management. Real-time insights are made possible by 5G and edge computing and may be securely analysed locally.

What are the 3 Major Innovations Rooting for Edge?

For businesses to fully benefit from edge computing, they must invest in flexible infrastructure, unique apps, and subject expertise. Three examples of industries leading the edge revolution are given below.

  1. Autonomous Drilling

The oil and gas sector, despite its stereotype of roughnecks drilling oil wells, is primarily data-driven and is available since years. At the edge, a significant amount of processing, computation, and data analytics take place.

Autonomous drilling, where edge artificial intelligence can control crucial well activities without needing human interaction, is the next development that edge computing can enable. The present design is referred to as a "open-loop system," where software and sensors inform the drillers of what is happening and allow them to make quick adjustments to the situation. A closed-loop system, in which recommendations are made automatically and machines carry them out, is the intended outcome. Meanwhile, these AI-based insights will support human activity in some crucial applications.

  1. Self-Driving Car Maintenance

While the auto business has an increasing amount of connections to the cloud, it is still rooted in the real world like the oil and gas sector is. Particularly the automotive services industry is adapting swiftly to accommodate electric automobiles.

Although the majority of the attention in this area is on the inside of the cars, we also see advances at service centres. Garages will transition from performing oil changes to data-driven auto maintenance and managing software as cars grow more electric and intelligent. To meet the demands of data sovereignty, predictive and regular maintenance will become crucial, with local infrastructure storing software upgrades for cars.

It just isn't practical to backhaul all of the data that connected automobiles generate every second straight to the cloud. Aggregated data can be used by local organization at service centres to assess battery performance and the general health of vehicles. To further analyse trends across a full fleet, a portion of this data can be then transmitted to the cloud.

For use cases involving connected automobiles, it's also crucial to distinguish between latency-sensitive and latency-critical workloads.  Since autonomous vehicles are essentially mobile data centres, all latency-sensitive decisions (such as when to apply the brakes or deploy the airbag) will always be done locally.

  1. Industrial Automation

Using edge computing capabilities has already advanced the manufacturing sector significantly. Industrial robots gather information as they operate and make minute adjustments in real time to improve workflow. Edge computing is becoming increasingly more necessary as a result of the new manufacturing use cases that private 5G is making possible. Although 5G allows a low-latency local connection, high-bandwidth, the bottleneck between the facility and the cloud still exists, necessitating on-site data pre-processing.

As an example, Telstra has integrated a private 5G network having edge AI workloads to uphold quality, boost productivity, and protect employees. These approaches decrease decision-making latency, guarantee autonomous operations irrespective of cloud connectivity, and lower the overall volume of data that needs to be backhauled to the cloud.

Manufacturers can improve monitoring, management, maintenance, and optimisation of their production assets by using edge computing. To maintain the OS versions and deploy security covers to edge devices in the field, BOBST, one of the top providers of equipment and services to the packaging sector, set out to automate as much of the process as possible. Quality and yield went up, while risks and expenses went down.


Since hosted solutions' latency frequently necessitates a novel method, especially for processing that must be completed quickly, data must frequently be treated at the edge. Edge clouds or cloud edge services are being developed by cloud service providers on their own, "intended to achieve jobs at scale across the worldwide organization's data, systems, and processes."