The idea of edge computing is slowly but surely making its way into our technology landscape. Most people see edge computing as a contemporary of cloud computing but in reality, it complements the cloud in many ways.
With digitization and connectivity reaching unprecedented heights and still growing, there was a dire need for a technology that was geared more towards localized processing needs, rather than globalized outreach.
This is perhaps why we think the future of edge computing is quite vibrant. Edge computing can be regarded as the enabler for devices of all sorts generating, sharing and processing real time data at a much localized level, resulting in immense efficiencies.
Although the requisite infrastructure for edge computing is still catching up, this technology has displayed immense prowess especially in the wake of the recent Covid-19 pandemic. This crisis has put crippling stress on the existing information infrastructure.
The highly localized nature of edge computing makes it an ideal fit for such extraordinary circumstances that have otherwise choked the traditional information highways over which the normal network traffic flows.
The other factor that works greatly in favor of edge computing is that end point devices such as our smartphones are getting more powerful with every passing day. This in turn relieves the burden from centralized networks such as servers.
In times of crises, it has otherwise been observed that the more localized approach you adopt to combat them, the more efficient and effective is your response. Data that is processed right at its epicenter is likely to be most truly reflective of the situation.
Lastly, edge computing has immense potential to reduce our dependencies on centralized networks that have a very high tendency of getting choked when subjected to very high flow of network traffic.
The inherent agility of edge computing can also play an important role in crisis response efforts. Take the example of Covid-19, which is a highly contagious virus. By analyzing real time data of infected patients, high risk areas can be cordoned off to stop further spread.
Real time patient data can be broadcast to the nearest first responders and hospitals so that affected patients can not only receive medical attention with minimal lag, the probability of an infected person affecting others also reduces drastically.