Research on the Application of 5G Cloud-network-edge-device Convergence and Intelligent Video Technology in Smart Grid

Ding, Peng; Xue, Yunfei; Shen, Yun; Duan, Huibin

doi:10.1109/iwcmc51323.2021.9498953

Cited by 4 publications

(1 citation statement)

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“…Implementing edge computing through edge data centres enables more granular requirements for each edge node, resulting in more efficient resource utilisation [9]. Aiming at smart grid scenarios, edge data centres are well placed to improve resource management strategies [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Carrying Method for 5G Network Slicing in Smart Grid Communication Services Based on Neural Network

Hu¹,

Gong²,

et al. 2023

Future Internet

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When applying 5G network slicing technology, the operator’s network resources in the form of mutually isolated logical network slices provide specific service requirements and quality of service guarantees for smart grid communication services. In the face of the new situation of 5G, which comprises the surge in demand for smart grid communication services and service types, as well as the digital and intelligent development of communication networks, it is even more important to provide a self-intelligent resource allocation and carrying method when slicing resources are allocated. To this end, a carrying method based on a neural network is proposed. The objective is to establish a hierarchical scheduling system for smart grid communication services at the power smart gate-way at the edge, where intelligent classification matching of smart grid communication services to (i) adapt to the characteristics of 5G network slicing and (ii) dynamic prediction of traffic in the slicing network are both realized. This hierarchical scheduling system extracts the data features of the services and encodes the data through a one-dimensional Convolutional Neural Network (1D CNN) in order to achieve intelligent classification and matching of smart grid communication services. This system also combines with Bidirectional Long Short-Term Memory Neural Network (BILSTM) in order to achieve a dynamic prediction of time-series based traffic in the slicing network. The simulation results validate the feasibility of a service classification model based on a 1D CNN and a traffic prediction model based on BILSTM for smart grid communication services.

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Section: Introductionmentioning

confidence: 99%