Edge computing describes a computing topology in which the acquisition, processing, and delivery of information is placed closer to the source of the information. This distributed model is more conducive to handling network connectivity and latency challenges as well as bandwidth constraints by embedding more functionality at the edge.
While cloud computing and edge computing are often thought of as two competing technologies, this is a fundamental misunderstanding of the concept.
Edge computing refers to a computing topology that places information storage, computation, and processing closer to the edge of a thing or network. Cloud in the strict sense is a system that utilizes Internet technology services, but a form of service that does not depend on whether it is centralized or decentralized.
When cloud computing and edge computing are implemented together, the cloud will be used to create a service-oriented model, and edge computing will allow cloud services to be executed disconnected.
From a technical point of view, edge computing is derived from cloud computing, which is a natural process, and this process has not appeared in recent years.
In the field of video surveillance, it has actually appeared ten years ago, but in recent years, with the continuous warming of the concept of the Internet of Things and the popularity of artificial intelligence technology, the processing and computing capabilities of the hardware chips at the data acquisition end have increased in recent years. With the growth of the data, people gradually summarize the data processing and calculation based on the data collection end into a model that is different from the traditional cloud computing.
Edge computing is an optimization algorithm for cloud computing. The use of edge computing may mean that the data collection terminal does not need to continuously establish a long connection with the cloud computing data center. We can regard cloud and edge computing as our two hands, a "side hand" and a "cloud hand". We will use one or both hands depending on the desired action.
In some cases, the "side hand" will play a more prominent role; in other cases, the "cloud hand" will take the lead; in still other cases, the two hands need to work together. Situations where "side-hand" dominates include low latency or limited bandwidth (especially wireless transmissions). Cloud computing will occupy a more dominant position when computing requires data collected from different cameras, or requires powerful computing power such as big data analysis.
Some people think that edge computing may replace cloud computing. In fact, not only one computing mode will dominate, but multiple computing modes will form an organic whole. In many practical solutions, edge computing, fog computing, and cloud computing cooperate with each other to coexist and prosper.
For security solution providers, it is necessary to fully realize the value of massive video image information data generated by cameras, and it is necessary to combine cloud computing and edge computing to provide a complete set of solutions. As data volumes and speeds increase, streaming all this information to the cloud or data center for processing becomes less efficient.
For IoT edge computing, in addition to the intelligent collection devices on the edge, there may also be edge servers. In security systems, edge servers are, for example, NVRs with intelligent functions or intelligent processing servers in various forms.
For intelligent NVR, it belongs to the intelligent computing edge of edge computing, and also belongs to the video image information forwarding gateway in fog computing.
