Open Access

ARTICLE

Fine-Grained Bandwidth Estimation for Smart Grid Communication Network

by Jingtang Luo¹, Jingru Liao², Chenlin Zhang³, Ziqi Wang⁴, Yuhang Zhang², Jie Xu^2,*, Zhengwen Huang⁵

1 State Grid Sichuan Economic Research Institute, Chengdu, 610041, China
2 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
3 Science and Technology on Security Communication Laboratory, Institute of Southwestern Communication, Chengdu, 610093, China
4 State Grid Economic and Technological Research Institute CO., LTD, Beijing, 100055, China
5 Department of Electronic and Computer Engineering, Brunel University, Uxbridge, UB8 3PH, United Kingdom

* Corresponding Author: Jie Xu. Email:

Intelligent Automation & Soft Computing 2022, 32(2), 1225-1239. https://doi.org/10.32604/iasc.2022.022812

Received 19 August 2021; Accepted 11 October 2021; Issue published 17 November 2021

Abstract

Accurate estimation of communication bandwidth is critical for the sensing and controlling applications of smart grid. Different from public network, the bandwidth requirements of smart grid communication network must be accurately estimated in prior to the deployment of applications or even the building of communication network. However, existing methods for smart grid usually model communication nodes in coarse-grained ways, so their estimations become inaccurate in scenarios where the same type of nodes have very different bandwidth requirements. To solve this issue, we propose a fine-grained estimation method based on multivariate nonlinear fitting. Firstly, we use linear fitting to calculate the convergence weights of each node. Then, we use correlation to select the important characteristics. Finally, we use multivariate nonlinear fitting to learn the nonlinear relationship between characteristics and convergence weight, and complete the fine-grained bandwidth estimation. Our method exploits multiple node characteristics to reveal how different nodes affect bandwidth requirements differently, and it can learn multivariate estimation parameters from present network without human interference. We use NS2 to simulate a real-world regional smart grid. Simulation shows that our method outperforms existing works by up to 56.5% higher estimation accuracy.

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Keywords

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