#smoadnetworks

Before the age of data analytics, ML & AI, the monitoring and identifying process was done manually, and it was a time-consuming process. Closed Circuit TV (CCTV) cameras are a common feature where homes, offices and public places are fitted with a surveillance cameras for security purposes. This was followed by IP video that enabled video transmission across network cables, where IP cameras can be fitted easily to the network. This was the birth of digital video analysis using algorithms.

Video analytics

Earlier, analytics were processed at the centralized server, but the drawback was that the bandwidth was expensive, especially when massive data had to be transmitted to the video management software before processing and analysis. Thanks to edge computing, this has become an affordable game, where more and more analytics are taking place at the camera’s end itself. The good news is the camera sends images only when motion is detected. This saves on the bandwidth and the need for an operator can also be discounted.

Despite the large number of CCTV cameras placed across the length and breadth of the country, some gaps need to be filled for fool-proof security. Edge computing is the ideal choice to bridge this gap. Edge computing enables real-time surveillance and helps in collecting real-time data. This ensures blind spots get complete visibility with virtual service deployment at the edge.

Cloud or edge

Modern-day CCTV has evolved to handle video analytics. Cloud computing and edge surveillance systems allow real-time processing. The advantage of edge computing in surveillance has removed network latency. The edge cloud enhances the surveillance system responses to make it more cost-effective and faster.

The lifeline of a business thrives on information. Storing data in the cloud or holding information in a data centre far away will delay decision-making. Due to its offline nature of data-processing and the latency involved, and it could cost the company dearly.

The answer to this is opting for edge computing or 5G. The network traffic is massive, with data coming from IoT devices, Analytics, Artificial Intelligence (AI), and performance and machine language applications are valuable. It leads to delays in transmission as the requirement for bandwidth exceeds capacity. Modern businesses depend on WiFi, WAN architecture, or MPLS, and if your bandwidth is unable to keep up with the demand, you lose in the race.

Prevent latency by hopping onto the edge. Using edge for your organization helps to analyze, predict and learn. It improves IT and the visibility to manage the production environment. Failures can be identified by increasing monitoring potential by including intelligence at the edge. Save on resources by combining and reducing data at the source so that essential data only can be sent.