Edge computing is not a new word. As a provider of content distribution network CDN and cloud services, AKAMAI worked with IBM on "Edge Computing" in 2003. As one of the world's largest distributed computing service providers, it was responsible for 15-30% of global network traffic. In one of its internal research projects, the purpose and problem of "edge computing" was proposed, and AKAMAI and IBM provided Edge Edge-based services on their WebSphere. For the Internet of Things, breakthroughs in edge computing technology mean that many controls will be implemented through local devices without having to leave the cloud, and processing will be done at the local edge computing layer. This will undoubtedly greatly improve the processing efficiency and reduce the load on the cloud. As it gets closer to the user, it also provides a faster response to the user and resolves the need at the edge. The earliest understanding of edge computing was the inclusion of a layer of edge computing (Edge CompuTIng) in the 7-layer technical architecture seen in IBM's IoT ecosystem in 2012. At the time, although it did not understand what edge computing was, it also began to focus on edge computing. In the early days of the development of the Internet of Things, cloud computing has undoubtedly received widespread attention. After encountering some problems with the intelligence of cloud computing alone, the industry realized the importance of edge computing. So at the end of 2016, the establishment of the Edge Computing Alliance was established. Recently, I have studied the white paper of the Edge Computing Industry Alliance and found that the Edge Computing Industry Alliance is very unclear on the industry scope! In the white paper, the industry-related content mentioned has the following paragraphs: Today, from the predictive maintenance of the aviation industry, the intelligent operation of elevators in the public sector, the intelligent meter reading of the energy industry and the whole process tracking of the logistics industry, we can deeply feel that the intelligent interconnection of “objects†will Everywhere, manufacturing, energy, public utilities, transportation, health, agriculture and other industries will be affected and undergo profound changes. The current industrial planning represented by China's 2025, North American Industrial Internet and European Industry 4.0 is a direct manifestation of this trend in real time. Predictive maintenance, energy efficiency management, and intelligent manufacturing are typical industrial application scenarios. If edge computing is a layer of IoT technology from the IoT ecosystem, the industry scope of edge computing applications should be consistent with the Internet of Things. The Internet of Things includes both ToB and ToC scenarios. The industry scope of the Edge Computation Alliance is not clearly stated, but the examples are concentrated in the ToB scenario, and there is no ToC scenario, which makes it easy to misunderstand that edge computing is just a ToB scenario. Smart home industry: There are a lot of smart home devices in the home, and the definition of interactive scenes between different products in smart homes requires edge calculation. This edge calculation is either a gateway or a central control system. Interconnection and device control between devices requires cloud computing and edge computing. Wearable devices: In the future, wearable devices will become popular on a large scale. Everyone may carry several or dozens of wearable devices with them. Do they need to be linked between these wearable devices? What equipment does the linkage of these devices? How do you make these wearable devices work together? Everyone-centric scene also requires edge calculations. Internet of Vehicles: There is a large amount of equipment in each car, the synergy between the car equipment, the synergy between the car and other cars, the car-to-human collaboration; also needs to be realized by edge calculation. I suggest that the Edge Computing Alliance should improve the edge computing application industry when defining edge computing. On the other hand, it needs to elaborate on the relationship with the Internet of Things. The pain points that edge calculation needs to solve need to be more specific In the Edge Computing Alliance white paper, there are the following descriptions: Under the trend of digital transformation of the industry, the edge of the network of intelligent interconnection faces the following challenges: Massive and heterogeneous connections Real-time business Application intelligence Data optimization Security and privacy protection In fact, the challenges faced by the marginal side mentioned in the white paper need to be further summarized and embodied. For example, the massive and heterogeneous connections: network operation and maintenance management, flexible expansion and reliability guarantee for the industry is a great difficulty. But for home and wearable devices, if a person without an IT background uses a smart home system and then buys a device, how does the device fit into the smart home system? If the edge calculation of the smart home can be realized: the newly purchased system is installed at home, plugged in, and can be used without the user to configure the network, and does not require the user to configure the scene. Can this be achieved? To achieve this, edge computing needs to solve the following problems: 1. Automatic network link; 2. After the device is connected to the network, the device knows its own attributes. According to the attributes, you can find devices that can be used to set up the scene together. 3. It is necessary to automatically sense the location of the device that cooperates with itself, and configure the scenario according to the location; 4. The new device implements iteration of the scene configuration through intelligent learning. I feel that another problem with edge computing is the mechanism for establishing an edge device decision. Edge computing needs to further summarize the industry challenges that can be solved. 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