Even with technology moving to the cloud, a simultaneous shift is already happening as computing moves increasingly edge computing. One reason for this is more and more connected devices in use. Edge computing won’t replace the cloud but instead represent an opportunity for investment and growth. Wired networks have been predominantly used in the past to address the latency and response time requirements of edge computing, but limitations on wired networks have limited their use. New research however suggests that when wireless networks are deployed in edge computing systems, they provide an entirely new level of functionality not possible with wired systems.
One of the new possibilities of 5G networks is their capacity to create private networks. With these, however, a fast connection between the gateways and sensors is required so that they may fully take advantage of the network’s high speeds.
Edge computing and IoT
Edge computing is crucial in the Industrial Internet of Things. The only way to guarantee interoperability is with the use of standard protocols that are created by the manufacturers themselves. Organizations need to provide customers with fast and personalized data. Organizations can do this by uploading it live to a cloud service or by installing it on their devices to be processed near the customer.
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Why Edge Computing is Important
As more people adopt edge-devices and the ability to process data globally, the need for creating edge-adapted infrastructure and tools becomes necessary. As cloud computing continues to grow, data processing will be needed at the point of creation due to high global demand.
Edge computing is a technology that has been in use for decades, which allows businesses to process and analyze data efficiently, without having to transfer large amounts of raw data across WANs. Low latency is today’s biggest challenge. If latency is high, an application needs to be able to respond quickly. Problems between these parts of the IoT devices mainly happen to the gateways. The gateways are practically the interface between Operational Technology devices and Information Technology devices because they have to “translate” communication in both directions.
Additional advances include driverless cars and robots. Driverless cars use computer vision to see the environment around them without a human, while robots are prime examples of how machines can allow humans to do things they can’t.
The often competing demands of low-latency network connections, high-power computing, and large storage requirements are driving innovation in edge hardware. Other sectors including healthcare need advanced machine learning to support AI devices and software. Most hospitals, however, lack the time or capability to make their own powerful edge computing hardware. The ability to run applications at the edge becomes more realistic as semiconductor circuits continue to advance at a fast pace. This can allow organizations with little access to cloud processing to benefit from highly advanced technology.
5G’s prices will steadily decline and show up in a wider variety of areas. It is still early for the next generation of wireless, yet carriers are already defining 6G wireless standards that promise another set of innovation in wireless networks. With container technology, developers can develop quickly and deploy applications to decentralized environments. Industry API’s and frameworks are emerging to help developers with more complicated tasks. Cloud based platforms are racing to create frameworks for their distributed applications to better work with edge architecture. Machine learning and AI will have a huge impact on what comes next.
Conclusion
Bridging the gap between OT and IT teams can be difficult, as these teams uphold different values. It is important to work diligently in order to solidly implement both the CPU and OT security of edge platforms. It can be hard to find people with cybersecurity expertise. The edge will soon be secured and organizations should spend billions of dollars to do so.
