Let’s start with the definition of Edge computing.
The term refers to the computation of data that happens right where the data is produced, i.e. “at the edge” of the IoT network.
So, instead of having a centralized, remote cloud to do all the work, the data is handled and stored locally, i.e. on the IoT device itself or at the nearest network node.
But how does edge computing work?
To explain how it works in real life, we can take any smart device out there as an example.
Every IoT sensor produces tons of data every second. In the case of cloud computing, the data is instantly transferred to the central, unified cloud database where it’s processed and stored.
If there’s any action required, the central server will send its response back to the device upon receiving and analyzing the acquired data.
While the whole process typically takes less than a second to complete, there might be situations when the response may be delayed or interrupted. This can happen due to a network glitch, weak internet connection, or simply because the data center is located too far from the device.
Now, in the case of edge computing, you don’t need to send the data acquired by the IoT sensors anywhere. The device itself or the nearest network node (e.g. the router) is responsible for data processing and can respond in a proper manner if action is required. As a result, the IoT device is no longer dependent on the internet connection and can function as a standalone network node. As you can see, the main purpose of edge computing is to decentralize data handling.
Scope, Need, and Demand
Now AI has brought disruption in every industry. Automotive, Healthcare and Manufacturing industries are at the top of them. In the future, these following industries will introduce more IoT devices and wearables than any other. Hence, they will need fast data connectivity to operate flexibly.
Edge Computing will not just increase efficiency and security it will enhance growth cost-effectively. Now industries are using cloud storage as a primary data collector where all types of low and high frequencies are measured. This high-frequency capture will increase data burden and create complications in data analyzing. Instead of sending high-frequency data to cloud storage, IoT gateway acts as a computing node and analyzes data locally by using Edge computing. Edge computing offers better memory storage solutions according to the future needs of industry. Making automated devices more sufficient, effective and most importantly more responsive.
Characteristics:
A few of the characteristics of edge computing are:
1. Privacy and Security
2. Reliability
3. Scalability
As the data is following the distributed computing paradigm, security must be the top priority. The data must be encrypted with different methods since the data may travel through a number of nodes before reaching the cloud over the internet. By keeping data at the edge, it is possible to shift ownership of collected data from service providers to end-users.
With the increasing number of IoT enabled devices it is important to have scalability. These devices generate a large amount of data and hence the data centers need to support that kind of processing.
Again, because of the large amount of data and its main application; IoT, there shouldn’t be any downtime and/or errors as those devices depend on the processing of data over the internet immediately.
Applications of Edge computing are not limited to specific areas like IoT, it has more useful applications and a lot of future scopes. Edge computing services reduce the volumes of data that must be moved, the resulting traffic, and the distance that data travels. This provides minimal latency and reduces the consequent costs. Computation for real-time applications, such as facial recognition algorithms, showed considerable improvements in response times as per research.
Another application of the architecture is to allow cloud gaming where the game runs in the cloud and is transferred to various clients such as mobile, VR glasses, etc. Most of the cloud games may suffer from high latency and insufficient bandwidth since the amount of data transferred is huge due to the high resolutions required by some services. As real-time games such as FPS (First-person Shooting) games have strict constraints on latency, processing game simulation at the edge node is necessary for the immersive gameplays.
Other applications include smart homes, smart cars, and other autonomous applications. Even some companies are betting on edge computing for efficient and reliable 5G networks.
Some market leaders such as Microsoft, Amazon, Cloudflare, AT&T and many more are working with edge computing to provide the customers with state-of-the-art services.
Purpose of Edge Computing
The main advantages of the Edge computing solution mentioned by experts are confidentiality, reduction of delays and minimization of communication problems.
For the first benefit, confidential information is pre-processed on-site, and only data that meets the privacy policy is transferred to the cloud for further analysis. The second advantage is the reduction of delays and is the most frequently mentioned advantage associated with the use of Edge Computing solutions. Currently, due to the huge amount of data sent to the cloud, processed there and transmitted back to peripheral devices, there may be delays in obtaining conclusions from the analysis, which can have serious consequences for the functioning of the enterprise.
Thirdly, in the case of Edge Computing, part of the calculations is performed on peripheral devices, which not only reduces the risk of delays but also gives a “potential” guarantee that the work will not be interrupted in case of a limited or intermittent network connection. This is especially important when solutions are deployed in hard-to-reach locations where communication coverage is very limited.
Will Edge Computing replace Cloud Computing?
“Edge Computing” is simply the contemporary fashionable term. It isn’t anything new. The “client-server” paradigm was established a long time ago and remains to this day. A web browser (“client”) connects to a web server (“server”).“Edge Computing” is just today’s word for “client”.
Today’s word for “server” is “Cloud Computing”.
Hence, it doesn’t replace cloud computing, it is the partner.
EDGE COMPUTING in AUTONOMOUS VEHICLES
Autonomous vehicles will communicate to one another over 5G to pass data some of which is downloaded from the cloud but a great deal of which is gathered from other AVs. That is, the edge — for an autonomous vehicle — is other autonomous vehicles. The cloud remains the same where other information can be uploaded for wider area processing, just as it is now with traffic apps. Due to the high transfer and low latency rates of 5G, passing data from vehicle-to-vehicle is a near-perfect use of the technology, especially because 5G does not travel well through boundaries but AV’s will presumably have some form of an external antenna to pass data along.
Conclusion
The demand for automation and the Internet of Things keeps growing, and devices need to deal with real-time data and produce immediate outputs. When industries like healthcare and autonomous transportation start investing in automation, new data processing challenges arise.
Even a second of delay can make a life-or-death difference and lead to multi-million economic and reputational damage. Under such conditions, it’s imperative to have a reliable data processing technology that can answer offline requests and deliver prompt responses.
Shifting data storage from cloud data centers closer to the network allows reducing operation costs, delivering faster performance, and working with low bandwidth. These benefits can potentially solve multiple issues for IoT, healthcare, AI, AR — any field and technology that requires fast real-time data processing.
You can implement edge computing into your enterprise operations right now and access these benefits. It’s possible with an experienced tech partner who knows how to set up data transfers, secure local networks and connect systems to edge storage.
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