With connected devices ranging from smart ovens to data collection devices for industrial research, the internet of things (IoT) is expanding fast. IDC predicts that by 2025, there will be 41.6 billion linked IoT devices, producing 79.4 zettabytes (ZB) of data. One Zettabyte is roughly equal to one billion terabytes.
The majority of these devices would upload all of the data they gathered in the early IoT era to the cloud for analysis. The data pipeline begins to slow when you attempt to send trillions of gigabytes to the cloud. That’s where edge computing comes in; it lets IoT devices process part of that data locally rather than sending it to the cloud. This is where the names come from—instead of being sent elsewhere, the information is handled at the edge of your own network.
Role of Edge Computing in IoT
In today’s IoT ecosystem, edge computing serves a distinct purpose. IoT devices are freed from latency and connectivity problems that would otherwise prevent some IoT use cases from being realised thanks to this distributed, local computing architecture. This crucial technology serves as the foundation for IoT applications that use classified data, require quick or low-latency decision-making, take place in an environment with vulnerable or non-existent cloud access, and have data-intensive use cases, such as industrial IoT implementations.
As opposed to cloud-based analysis, edge computing devices have minimal latency because data is evaluated locally. This has the potential to make or break the functionality of IoT devices for precision in time-sensitive tasks. Utilizing the Internet of things at scale without running the danger of data theft or network overloads is possible with edge computing, which is computationally safe, affordable, private, and effective.
Additionally, edge computing adds a layer of redundancy and resilience for tasks that are mission-critical. Businesses can continue operating normally even if an element breaks down because this process is dispersed rather than centralised to a single system.
This is not to say that edge computing cannot coexist alongside cloud-based technology; it most certainly can and frequently does. In these situations, edge computing might be able to offer some real-time data as well as act as a filter to determine which data should be uploaded to the cloud over time for use in more in-depth or sophisticated analytic techniques.
Edge computing is properly minimised in an industrial IoT situation, for example, on the floor of a production plant, to lower the risk of downtime or data breach and for the more effective administration of huge amounts of data.
The low latency component of edge computing is a massive plus for worker safety for manufacturers who use it. For instance, rather than waiting for cloud analysis, whose latency may result in downtime and scrap parts, if the data collected from a data adapter shows subtle anomalies—for instance, chatter—that may indicate a stress fracture or other form of near-term failure, a machine can be immediately shut off.
In summary, edge computing analyses some IoT device data close to the edge of a local network rather than sending it to the cloud for faster, redundant, connectivity-independent, and easily scaled IoT processing.
What is an example of Cloud Computing?
Consider a structure that is protected by a large number of high-quality IoT video cameras. These “poor cameras” only emit a raw video signal, which they then continuously feed to a cloud server. To ensure that only clips with activity are added to the server’s database, the video output from all the cameras is run via a motion-detection app on the cloud server.
As a result of the huge number of video content being transferred, there is a continual and severe demand for the building’s internet infrastructure. The cloud server is under a tremendous amount of strain as it processes video from all the cameras at once.
Imagine the network edge receiving the motion sensor computation. What if each camera ran the motion detection software on its internal computer before sending the necessary footage to the cloud server? Because a large portion of the camera footage won’t ever need to travel to the cloud server, it will significantly reduce the amount of bandwidth used.
As a result, the cloud server would only be in charge of keeping crucial footage, allowing it to communicate with many cameras without becoming overburdened. This is an example of edge computing.
Conclusion
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