Edge computing can alleviate the difficulties of load bottleneck, delay, fault tolerance, etc. In the future, the application system should be "big intelligence" in the cloud, "small intelligence" on the edge, and data communication in the middle through MQTT or similar protocols. Edge computing has the characteristics of good real-time performance, strong stability, adapt to various network environments, and can make decisions on its own, which is very suitable for industrial production requirements.
Compared to cloud service objects in a broad sense, the needs of IoT devices are different. For example, networking of IoT devices is low power consumption, and terminals do not need too much computing power. Moreover, passing data to the data center can also affect the response speed of the device. At this point, is there a local solution that is more suitable? The answer is the edge calculation.
In fact, the concept of edge computing has been around for a long time, but it has just started in the field of application. As more and more workloads appear in the cloud, and new technologies such as the Internet of Things enter production and life, additional demands are placed on local processing capabilities. For cloud computing, it has advantages in dealing with large-scale business, but this centralized data processing method seems to be "outdated" in the era of Internet of Everything.
In the future, 79% of IoT traffic will be accessed through the gateway, 50% of the network traffic will come from the Internet of Things, and the Internet of Things will contribute more than 50 billion connections. The data shows that the economic value added by the Internet of Things will reach 1.9 trillion US dollars by 2020. Obviously, a large part of the massive data brought by the Internet of Things is high-value information. How to make good use of this part of the data is a potential profit point for solution vendors and service providers.
The characteristics of the Internet of Things are “small, fast, and spiritualâ€. Real-time interaction is the core of the experience. Data preprocessing, low energy consumption, and agile connections are prerequisites. From the architectural point of view, the edge computing IoT solution can be divided into sensing control layer, network layer, agile controller and application layer. The network layer mainly realizes convergence and interconnection. Its functions include network connection and management, including edge. Calculate, perform on-site processing, and ensure that the business is implemented locally. It can be said that edge computing not only provides an intelligent open platform for integrated computing, storage, and connectivity, but also reduces security risks. On the other hand, this computing model also reduces the burden on the cloud.
According to Metcalfe's Law, the network value is proportional to the square of the number of users. When more and more people and smart objects are connected to a network, the entire network will be added value. In IoT data, at least 23% of the data is analytically valuable.
Broadly speaking, in the process of evolution of edge computing, there are not only the construction of ecological platforms, but also the formulation of alliance standards. As for the technical level, each has its own advantages. The key is whether it can provide users with real convenience.
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