Edge and Internet of Things

(Image source: computervillage.org)

In applications for industrial IoT technology (IIoT) where instantaneous decision making is essential, edge computing is a better match, than cloud computing which is appropriate for big data analytics. Edge computing uses the processing power of IoT devices to aggregate, pre-process, and filter data at the source and enhances the capabilities of digital tools.  In IIoT applications such as power production, smart traffic lights, or manufacturing, the edge devices capture streaming data that can be used to prevent a machine part from failing, reroute traffic, optimize production, and prevent product defects.

The unprecedented amount of data generated by IoT devices is putting considerable strain on the internet architecture. Edge involves pushing data handling to the edge of the network, closer to the source of the data. In other words, instead of sending data to the cloud server or central data centre for processing, the device connects through a local gateway device. This allows faster analytics and reduces network pressure.

Numerous IoT use cases that require edge computing include smart buildings, drone-based delivery services, real-time subsurface imaging, traffic congestion management and video surveillance.

Edge computing provides new possibilities in IoT applications, particularly for those relying on machine learning for tasks such as object detection, face recognition, language processing, and obstacle avoidance.

In Fog computing, data gets collected and analyzed at most efficient and logical places between the source and the cloud.

As the Internet of Things evolves, the rise of edge computing becomes inevitable.