Edge computing enables real-time or faster data processing and analysis, bringing data processing closer to the source instead of external data centers or the cloud, thereby reducing latency time.
It can significantly reduce budget costs. The cost of enterprise data management solutions on local devices is much lower than on cloud and data center networks.
It reduces network traffic. As the number of IoT devices increases, data generation continues to grow at a record pace. As a result, network bandwidth becomes more limited and overwhelms the cloud, leading to greater data bottlenecks.
Edge computing can improve the efficiency of applications. By reducing latency levels, applications can run more efficiently and faster.
Personalization: Through edge computing, continuous learning can be achieved, and models can be adjusted based on individual needs, providing personalized interactive experiences.
Another critically important issue is security and privacy protection. Edge data involves personal privacy, and the traditional cloud computing model requires uploading this private data to the cloud computing center, which increases the risk of leaking user privacy data.
In the edge computing network, the research on identity authentication protocols should draw on the advantages of existing solutions, while combining the characteristics of distributed and mobile edge computing, and strengthen the research on unified authentication, cross-domain authentication, and switching authentication technologies to ensure the security of users' data and privacy in different trust domains and heterogeneous network environments.