Edge computing: Getting an edge everywhere

By Sanjay Gupta

The rise of the connected devices ecosystem has led to a rise in demand for high-speed networks, especially in the prevailing situation of Covid. But this crisis has also exposed the limitations of the overall network architecture, highlighting the need for edge computing.

Edge computing is a distributed computing paradigm that brings critical information analysis and knowledge storage closer to the location where it is needed. With the growing number of IoT devices along with connected automotive and industrial applications; latency, privacy, and bandwidth become critical limiting factors and edge computing solves this by bringing the intelligence closer to the source.

Why Edge?
In the coming times, IoT will prevail everywhere, from autonomous mobility and vehicles, machinery equipment, smart devices equipment, wearable devices, enterprises to healthcare, among many more. As a result of the explosive growth of IoT devices generating an intense amount of data, the pressure on the internet infrastructure is immense. This has led to the need for real-time computing power, thus bringing edge computing systems into play.

For the majority of organisations, reduction in expenditure is a major driver towards deploying an edge-computing architecture. The biggest added advantage is the ability to process and store data faster, enabling for more efficient real-time applications that are critical to companies.

Backbone for Smart Cities
Edge computing represents a crucial investment for any smart city in order to really reap the benefits of a next-generation IoT network. Not only does it control transmission and network requirements, but equal financial savings are also involved. Its prime feature comprises closer proximity between data storage and processing which can be leveraged in enhancing the data management and processing for smart cities.

Core of autonomous vehicles
With the advent of next-gen technologies in the autonomous vehicle ecosystem, challenges such as delay in data transmission, real-time results, on-the-spot accurate and vital decisions, processing of large quantities of data, etc., have also increased. Automotive players are focused on leveraging edge computing to address these ever-evolving challenges. For instance, a vehicle running on a highway will send the live feed to the cloud and then wait for cloud’s response to applying brakes in an event of a collision or when approaching an obstacle. With edge computing, the live video can be processed faster, and real-time action can be taken without any negative impact.

There are hundreds of sensors in a modern-day car that create tonne of data. And while most of it is processed in the vehicle itself, transfer of data to the cloud might be needed by some in-car applications. Data moved to the cloud could be constrained more intelligently with edge computing.

Industrial Revolution 4.0
As telecom players are prepping up to deploy 5G, tech enthusiasts believe that it will assist edge computing phenomenally. Owing majorly to the benefits of high bandwidth and low latency for applications, 5G will unlock possibilities for far-away sensors to instantly give updates about the connected devices and the edge is poised to support this highly responsive computing.

Edge computing is expected to emerge as a driving force behind the unfolding of the Industrial Revolution 4.0. Machines will continue to take over repeatable and even decision-making tasks with processing power and lower latency offered by edge computing, enabling human capital to undertake more creative and disruptive positions in the industry.

The writer is vice-president & India country manager – NXP Semiconductors