Today, as data has become an integral asset for every business across diverse industries, shifting to cloud computing has transformed the dynamic of data processing and functioning of devices. With the rapid growth of the Internet of Things applications, the traditional centralized cloud infrastructure is coming across with many challenges, including high latency, low Spectral Efficiency, and non-adaptive machine type of communication. However, these challenges emboldened a new wave of computing, shifting the centralized functions of cloud computing to edge devices of networks.

Edge computing refers to the transformation of the way data is being handled, processed, and delivered from millions of devices. While IoT devices intend to make people’s lives easier, edge computing is focused on making computing faster. It decentralizes data from the centralized network and moves forward it onto distributed networks of the server that are closer to the place of data generation.

As the growth of connected devices, along with new applications that require real-time computing power continues to climb, the demand of edge computing systems also accelerates. The evolution of 5G networks is also enabling edge computing systems to drive the support of real-time applications, including video processing and analytics, autonomous vehicles, artificial intelligence, robotics, among others.

In a recent research by Intel on self-driving cars or autonomous vehicles, a car would be generating around 1GB of data every second that would require computation of this data within a fraction of second for decision making. Undeniably, cloud network solutions are quite fast enough to receive 1GB of data within a second, by computing possibilities, and sending it back. But the time taken to ask for a car to switch on the light and the time it hits an object would leave very little time.

Above all that edge computing is fast becoming pervasive for most IoT devices. While this allows users to relay more data to the cloud, several firms are trying to put more computing power in devices.

Why Edge for IoT?

Previously, edge computing was focused on addressing the costs of bandwidth for data traveling long distances owing to the growth of IoT-generated data. However, the rise of real-time applications that require processing at the edge is now driving the technology ahead.

According to Gartner, around 10 percent of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. However, this figure will reach 75 percent by 2022. The transition to edge computing may have an intense impact on businesses’ IT and OT systems, and on how new digital products are built.

Moreover, edge computing solutions are encouraged to deliver the immense machine type of communication, ultra-reliable low-latency communication, and high Spectral Efficiency. In the modern digitization age, several digital products need to be autonomous in their operation that lets them gain the required safety, reliability, and user experience needs. Computing on edge can draw various forms, providing the ability to have local storage and local computation and improves the security and privacy of an IoT application.