Deep Learning for Edge Computing
As the number of devices built of Internet of Things (IoT) continues to grow, the term edge computing has become very common and frequent. With items like drones and advanced robots, the complexity has increased. As a result, they require a fast processing of data. Can edge computing help enhance the speed of data processing?
According to IDC, 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”. The most applicable use cases for edge computing lies within IoT. Basically, the terms with sensors or embedded devices are the primary source of data generation. It’s an estimate that there will be tens of billions of connected devices within next few years. There’s still a lot of data to transfer and streaming to a centralized cloud or data center for processing.
Edge computing reduces the latency and network congestion because data doesn’t need to travel over network for processing. This is particular in industries where even a few milliseconds of latency could spell disaster.
Edge Computing in Action
The devices like wearable heart monitors are edge computing at its most basic. These devices can provide and analyze data without needing to connect to the cloud very often. More advanced use cases include gateways, such as a vehicle receiving information from GPS devices. And perhaps the more complex use case deals with cell phones.
The buildout for the next generation of network (5G) can take the advantage of edge computing. However, edge computing is not without its risks. As a rapidly evolving technology, security can be a concern with such a widespread network, especially when the endpoints are not secured. The cost of an edge computing project can be prohibitive, for small organizations.
How Will It Transform the Cloud?
Edge Computing’s real power lies within the real-time insights. According to a research by Gartner, about 10% of enterprise generated data is created and processed outside a centralized data center or cloud. Gartner has also predicted that by 2020, the count will increase to 50%. There are some who argue that edge computing could replace cloud altogether.
But what we end up seeing is a hybrid model that combines the best of both the worlds. Cloud providers can deploy micro data centers at few key geographic locations rather than an entire decentralized architecture. Even a provider can keep control while moving data processing capabilities closer to the user.
Edge Computing and IoT in 2019 and Beyond
Edge Computing as a term and an architecture as said exists since longer. However, the scope of Industrial IoT Edge Computing is focused on devices and technologies that are attached to the Internet of Things. As IoT is all about connecting and in order to acquire, analyze and leverage data from assets and devices that contribute to our goals.
In a nutshell quoting “edge computing pushes the intelligence, processing power, and communication capabilities of an edge gateway or appliance direct into devices like programmable automation controllers”. As always, having the trusted guidance of a managed service provider can help in making these tough organizational decisions. So, in order to get the best guidance, you can contact TechNEXA Technologies from our contact page. As more devices enter the IoT, more additional use cases develop. Hence it will be exciting to see what Edge Computing can accomplish.
