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Edge computing and IoT: a Whole New World

Edge computing and IoT: a Whole New World
Date
6/9/2022
Time to read
8 min
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Nowadays data is such a buzzword that we lose its primary meaning and purpose. With the advent of edge computing and 5G technology, data reclaimed its value, offering more opportunities for businesses that decide to harness its power. 

As IoT devices are booming on the market, so does the scope of data produced by them. Such rapid development and enormous scopes of information create latency issues and decrease the productivity of data transfer and operational systems within organizations. Thus, edge computing is suggesting a perfect solution for it all — processing the data at the very point where it’s created, without losing the time on sending it to a centralized data center or into the cloud and other secondary operations.  

So what is Edge computing?

Edge Computing is a distributed framework or architecture that brings software closer to the sources of data, like IoT devices or local edge servers. Such proximity to data sources allows to decrease latency, improve flexibility and bandwidth availability, receive faster insights, and have better response times. 

Edge vs. Cloud 

The notion of Edge Computing is often confused with the notion of cloud computing. Let us explain what is the difference between the two. 

The main discrepancy between the edge and the cloud is their location. In cloud computing, the data processing takes place in a remote center, to which the data needs to be transferred. In edge computing, this process takes place immediately in the location where the data was just created, locally. The data is stored in an edge device that now instead of relying on the internet connection to transfer the data to the cloud, can function as a standalone node. 

Pros and Cons of Edge Computing 

By 2025, the amount of enterprise-generated data that will be produced and processed outside a data center or cloud will reach 75%, Gartner predicts. So, with more and more companies considering leveraging the opportunities of edge computing, we need to think smart and clear: are there more good or bad?

Let’s start with the pros.

  • Lower latency due to no data travel. This allows data to be used instantly for insights, for example in autonomous vehicles that require real-time response and data to be received within milliseconds.
  • Lower costs of storage. Edge computing usually uses local area networks, so the bandwidth becomes higher. Moreover, when data is processed at the source of its creation, less of it needs to be transported to the cloud, thus the costs of data transfer are lower as well. 
  • New horizons. While cloud computing needs stable internet access, edge computing can operate without it. The edge opens up distant and remote locations, which has never been reached with the cloud. 
  • Autonomy. Edge computing comes to the rescue when there is unreliable connectivity or restricted bandwidth, for instance, in locations like rainforests or deserts, on distant farms, and on oil rigs.

What about the disadvantages of this rapidly evolving technology? Does it have some inherent risks that need to be taken care of and addressed with more attention? 

  • The key concern in edge computing is the one connected with security. When using the edge to gather and process your data, you extend the area of your operations, thus making it a potential object for a cyber attack. Unsecure locations may be used as entry points to the core networks and may result in critical malfunctions of the whole system.
  • A rather controversial point is the cost. Many may claim that without transferring data to the cloud and the usage of local area networks the overall cost is lower when compared to the cloud expenditures. But there is another side to the story. Managing an edge computing environment may go beyond the financial benefits of the project. And when scalability becomes the first priority, companies may even suffer from their success, with more and more challenges with new emerging trends and technologies to tackle. 
  • The last point we would like to mention is the amount of real-time data created and utilized with edge computing. The main bulk of real-time data processing is used in real-time analytics, which is not information for long-term storage, so the companies should think carefully about which data is critical and whether to keep it. And the retained data should be protected according to the relevant business policies. 

How the Edge is connected with the Internet of Things

For some people, it is also not clear what is the connection between Edge Computing and the Internet of Things. Are those not interchangeable? Not quite. 

Edge computing goes hand in hand with the phenomenon of the Internet of Things. The edge brings the data as close as possible to the Internet of Things devices and their sensors, decreasing latency and boosting performance with the code running in the device itself rather than in the distant cloud. The IoT devices on their side provide the edge with more opportunities, as they can perform various tasks locally so that the edge has lots of unique data to process.

Edge computing and the Internet of Things highly depend on each other and benefit from one another. 

Use Cases of Edge Computing

Edge computing solutions offer advantages for any industry and may take on any form that is needed. Its capabilities vary from basic filtering to batch processing. Let us tackle more precise examples of the Edge computing implementation across various industries.

Edge Computing in Telecom 

Edge Computing in Telecom, or Mobile Edge Computing leverages mainly the technology of 5G. It provides the resources for computing and storage for applications as close as possible to the end-user, within or at the operator network. Carriers across the globe turn to 5G to increase their data bandwidth. And to ensure even faster real-time data processing, they integrate 5G with edge computing solutions and strategies. 

Edge Computing in Automotive

The most popular usage of edge computing is in autonomous vehicles or self-driving cars. They require instant insights and data, such vehicles cannot wait for a second to receive an instruction from a distant server. Driver safety and overall transportation performance can be enhanced with real-time data processing, as this industry is generally delay-intolerant. The number of connected cars on the market is expected to grow at a remarkable annual rate, so the question of lower latency is only to grow in popularity with time. 

Edge Computing in Medical Robotics

This is a perfect instance where it is advisable to choose edge computing rather than the cloud. Surgeons and medical personnel need access to real-time data. Things such as smart analytics or robotics controls in the operating room cannot stumble upon latency, bandwidth issues, or network instability. Here, the decision lies between life or death. 

Edge Computing and AI

McKinsey's survey on the state of AI in 2021 claims that the interest in AI adoption across enterprises continues to grow, and 56% of respondents have already adopted AI in at least one function in their company. No wonder such raging technologies like the Edge and AI will come together for creating more benefits. Fusing edge computing and AI technologies require creating a suitable Edge AI model in order to really reap the rewards of such a winning combination. It results in efficient asset management, reduces field issues, and ensures more customer satisfaction. 

Summary

IDC’s research establishes the point that the main driver for Edge computing is transforming the user experience. The convergence of such technologies as 5G, Artificial Intelligence, and Edge computing in particular already ignites the process of innovations that we’ve never seen before. 

Edge computing will impact each and every corner of the IT environment, forcing all industries to employ and integrate new architectures in their systems. Ignoring the Edge might cost you a lifetime of catching up with your competitors in the pursuit of delivering the best user experience.