Understanding Edge Computing | IOT Video Camera Use Case


Edge computing allows data produced by the internet of things (IoT) devices to be processed closer to where it is created instead of sending it across long routes to data centers or clouds.

Doing this computing closer to the edge of the network lets organizations analyze important data in near real-time – a need for organizations across many industries, including manufacturing, health care, telecommunications, and finance.

“In most scenarios, the presumption that everything will be in the cloud with a strong and stable fat pipe between the cloud and the edge device – that’s just not realistic,” says Helder Antunes, senior director of corporate strategic innovation at Cisco.

What exactly is edge computing?

Edge computing is a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet,” according to research firm IDC.

It is typically referred to in IoT use cases, where edge devices would collect data – sometimes massive amounts of it – and send it all to a data center or cloud for processing. Edge computing triages the data locally so some of it is processed locally, reducing the backhaul traffic to the central repository.

Normally, this is done by the IoT devices transferring the data to a local device that includes computing, storage and network connectivity in their smallest forms. Data is processed at the edge, and all or a portion of it is sent to the central processing or storage repository in a corporate data center, co-location facility or IaaS cloud.

What’s an example of edge computing?

Consider a building secured with dozens of high-definition IoT video cameras. These are ‘dumb’ cameras that simply output a raw video signal and continuously stream that signal to a cloud server. On the cloud server, the video output from all the cameras is put through a motion-detection application to ensure that only clips featuring activity are saved to the server’s database. This means there is a constant and significant strain on the building’s Internet infrastructure, as significant bandwidth gets consumed by the high volume of video footage being transferred. Additionally, there is very heavy load on the cloud server that has to process the video footage from all the cameras simultaneously.

Now imagine that the motion sensor computation is moved to the network edge. What if each camera used its own internal computer to run the motion-detecting application and then sent footage to the cloud server as needed? This would result in a significant reduction in bandwidth use, because much of the camera footage will never have to travel to the cloud server. Additionally, the cloud server would now only be responsible for storing the important footage, meaning that the server could communicate with a higher number of cameras without getting overloaded. This is what edge computing looks like.

Quick Recap

To recap, the key benefits of edge computing are:

  • Decreased latency
  • Decrease in bandwidth use and associated cost
  • Decrease in server resources and associated cost
  • Added functionality


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