The rise of the connected devices ecosystem has led to a rise in demand for high-speed networks. It has exposed the limitations of the overall network architecture, highlighting the need for edge computing. There are hundreds of sensors in a modern-day car and creates tons 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. 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.

In traditional enterprise computing, data is produced at a user's computer. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application. Results of that work are then conveyed back to the end-user. With regards to infrastructure of edge computing, it is a network of local micro datacenters for storage and processing purposes. At the same time, the central datacenter oversees the proceedings and gets valuable insights into the local data processing. However, we need to be mindful that edge computing is a kind of expansion of cloud computing architecture - an optimized solution for decentralized infrastructure. The fact is in a digital-first world, personalized content drives everything in the digital economy.


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