An edge computing node deployment method based on user data hotspot distribution

Abstract

The present invention provides an edge computing node deployment method based on the hotspot distribution of user data. The present invention determines the divided area where the user is located according to the strength of the received signal of the user. When deploying the edge computing node, factors such as communication scenarios and connection costs are considered, so that The results are more realistic and reliable. The edge computing node deployment algorithm is implemented through the simulated annealing method to realize the optimal deployment of edge computing nodes, which can reasonably deploy edge computing nodes and improve the system capacity and operating efficiency of the Internet of Things system. The present invention can be applied to IoT scenarios where users with different task types communicate with edge computing nodes, such as IoT scenarios such as intelligent transportation, and has strong practicability.

Description

technical field [0001] The present invention relates to the technical field of wireless communication networks, in particular to an edge computing node deployment method based on user data hotspot distribution. Background technique [0002] With the high-density access of IoT devices, IoT puts forward higher requirements in terms of low-latency connections. IoT users have different service types and low-latency business requirements, and need to pay more attention to user experience while promoting technological change. Edge computing can bring network service services to the edge of the network, establish servers between service users and service hosts, and complete network tasks. It sinks the business functions on the network cloud to the edge of the network and is as close to the data source as possible. By setting the network service facilities at the edge of the network, the service delay of the communication channel can be minimized, thus becoming a solution to low la...

AI Technical Summary

Problems solved by technology

[0003] However, in IoT scenarios such as intelligent transportation, users have strong mobility, randomness, periodicity, non-stationarity, and spatial correlation, which will not only cause changes in the network topology, but also affect the low-time connectivity of the network. The extension and the type of service required impose more stringent requirements

For this IoT scenario, there is a lack of effective edge computing node deployment solutions in the prior art, which affects the system capacity and operating efficiency of the IoT system

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