Edge Computing is a way to streamline the traffic flow of IoT devices and provide real-time local data analysis.
Edge Computing allows data produced by Internet of Things (IoT) devices to be processed closer to where it is created instead of sending it over long routes to data centers or clouds.
Making this calculation closer to the edge of the network allows organizations to analyze important data in near real time, a need of organizations in many industries, including manufacturing, health, telecommunications and finance.
What exactly is ‘Edge Computing’?
Edge Computing is a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, on an area of less than 100 square feet.”, according to the IDC research firm.
Reference is usually made to cases of IoT use, in which edge devices would collect data, sometimes massive quantities, and send it to a data center or a cloud for processing. Edge computing calculates data locally so that some of them are processed locally, reducing backhaul traffic to the central repository.
Normally, this is done by IoT devices that transfer the data to a local device that includes computing, storage and network connectivity in a small form factor. The data is processed at the edge and all or a portion thereof is sent to the central processing or storage repository in a corporate data center, co-location or IaaS cloud facility.
Read more: What is Augmented Analytics
Real-life examples of edge computing.
Oil rigs are a good example of how edge computing is used in the real world. Because of their remote locations at sea, they rely on technology to mitigate long distances to the data center and poor network connections. It is also expensive, inefficient and time-consuming for platforms to send real-time data to a centralized cloud. Having a localized data processing facility helps a platform run without delays or interruptions. ververica.com Platform enables every enterprise to take advantage and derive immediate insight from its data in real time.
Similarly, autonomous vehicles, which operate with low connectivity, need real-time data analysis to navigate the roads. The gateways housed inside the vehicle can aggregate data from other vehicles, traffic signals, GPS devices, proximity sensors, on-board control units and cloud applications, and can process and analyze this information locally.
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Security risks at the edge.
The IDC report “Edge Computing is reshaping IoT: it’s time for European companies to take note” suggests that Edge Computing security is really about IoT security.
As an extension of a data center, an edge computing infrastructure naturally increases the surface area exposed to threats. Many state-of-the-art computing devices are not designed with traditional IT security protocols, which means that unprotected endpoints can be incorporated into distributed denial of service (DDoS) attacks, and can even offer hackers access to wider network to which they connect.
Physical security should also be a consideration, if the devices are accessible to bad actors or people who could manipulate them.
However, if proper precautions are taken, perimeter computing can reduce the security and privacy risks of IoT by limiting the flow of data between the collection point and the main storage center, according to IDC.
Also read: How is Cybersecurity defined
