Comparing to the traditional cloud-based networks where information is handled and stored in a centralized location, this edge computing makes it possible to store and process data from a location closer to the device that produces or consumes that information.

 

This distributed computing paradigm enables the Internet of Things (IoT) devices to process the produced data from where it is created instead of carrying it to data centers or cloud computing environments. Automated vehicles, traffic management systems, smart homes, etc… are a few applications of edge computing that requires real-time data processing and faster response. With edge computing, only necessary data will be transferred to the centrally located cloud or data center.

 

For example, it will be more convenient for the security department of an apartment to store only activity visuals that are captured by dozens of CCTV cameras. For this, each IoT device will be connected to the network, and the edge computing paradigm process all the captured data using a motion detection algorithm and send only activity videos to the server.

 

This approach will reduce the traffic to the server and provide faster responses. If detailing any of the mentioned approaches, let us consider automated vehicles, you will understand how important edge computing is and why is it popular.

 

Automated vehicles prefer edge computing compared with the cloud.  The human brain has the capacity to take immediate actions and with Artificial Intelligence, these automated vehicles can also make such a decision. Here the difference is, whether these automated vehicles can make sudden decisions or not.

 

The operations of automated vehicles depend on real-time data produced by the IoT devices and their corresponding processing. If using a cloud computing network, the collected data will be transferred to a centralized location and after data processing, it will be sent back to the devices. But, this approach is not suitable for critical applications like automated vehicles and hence, edge computing is preferred.

 

This edge computing paradigm store and process the data closer to the IoT device and improves time to action and reduces response time to milliseconds. Also, it can conserve network resources. Some key benefits of edge computing are listed below:

Helps to minimize bandwidth usage

Eliminate the delay waiting for the response from cloud/data centers by minimizing response time to milliseconds

 

• Provides security to the network by preventing cyber-attacks
• The minimum usage of network resources will save costs.
• Reduces traffic to the cloud environment/data centers.
• Edge computing offers a faster and seamless experience
• Data can be rerouted if any node fails