Edge Computing Transformation in IoT: Technology evolves incredibly quickly than ever in the fast and connected world— a few years ago, many companies were struggling with the idea of moving their operations from their datacenter to the public cloud. Now, cloud adoption has become a primary driver of business disruption, allowing even the smallest company to have access to enormous processing power and data insights at the lowest possible costs. However, if there’s any lesson to derive from this transformative move to integrate cloud technologies, it’s that business won’t be the same again. Companies should prepare for the next renaissance motivated by edge computing. Like the transformation to the cloud, edge computing is creating vast new opportunities and dismantling entire approaches to business.
Gartner predicts, “About 10% of enterprise-generated data is created and processed outside an in-house centralized data center or cloud. By 2022, this figure will reach 75%.” The transformation to edge computing may have a profound impact on a company’s IT and OT systems, and how companies build new products.”
There are four critical drivers for the Edge Computing Transformation in IoT today.
1) Real-time processing:
The increasing need for real-time processing. Currently, there are several issues with processing times. They are fast, but not fast enough, especially if data needs to go back to a cloud every time for analysis. A network interaction can not delay the processing of human facial recognition by a sensor and the data processing of the decision to stop a machine. Delayed response time may cause human severe harm or damage to the computer systems. Similarly, autonomous vehicles and augmented reality applications need a response time below 20 to 30 ms. Communication with the cloud in every case of this scenario will only cause processing delays & potential harm.
2) Edge computing does local AI/machine learning based analytics:
In the past years, the cloud is providing the power of analytics to companies of every size, and companies that are utilizing these analytics are enjoying a significant advantage in the marketplace. However, the movement towards local, real-time analytics is already occurring. These Edge computing devices need to be intelligent to be able to aggregate data and deliver solutions in real-time. Living algorithms can work locally, thereby offering a higher level of performance and complex computations only previously available with the cloud.
3) Cost Efficiencies:
The bulk of the data that is generated by IoT sensors and actuators is likely irrelevant for the IoT solution. The fact a temperature sensor reports a 70ºF reading every two seconds might not be exciting until the sensor indicates a change in reading like 80ºF. Edge computing makes it possible to filter and process the data before it moves to the cloud. Edge computing significantly reduces the network bandwidth and cloud storage cost of data transmission between IoT devices and cloud.
4) Improves security and privacy of an IoT
Edge computing can enhance the security and privacy of an IoT solution. Edge computing reduces the number of sensors and actuators connected to the internet. Edge also lowers the potential vectors of security attacks. On-premise data processing and filtering by an edge computing device lessen the amount of sensitive and private information that Edge is sent through a network. Thereby, it addresses another critical issue of data privacy needs or regulations for the application.
It is critical to consider edge computing as complementary to cloud computing. It does not replace the cloud computing needs. This integral aspect is the reason many of the cloud players are starting to partner or acquire the companies in Edge computing space. Edge Computing Transformation in IoT is inevitable but transformative and beneficial for the companies to build innovative business models.
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