Matching Learning-Based Relay Selection for Substation Power Internet of Things

Wei Wang,Ruiqiuyu Wang,Yanhua He,Hao Zhang,Zhenyu Zhou,Xingwang Li

doi:10.1155/2022/6795205

Abstract

Wireless sensor network (WSN) can effectively solve the problems of weak coverage, blind coverage, and low survivability of smart substation communication networks by deploying multiple relay nodes and adopting multihop transmission. However, there are still some challenges in the traditional relay selection strategy of WSN in substation, including incomplete information and the selection conflicts among multisource nodes. In this paper, we propose a matching learning-based relay selection mechanism for WSN-based substation power Internet of things (SPIoT). Firstly, considering the electromagnetic interference caused by the operation of high-voltage equipment, a multihop transmission model of SPIoT is built. Furthermore, based on the upper confidence bound (UCB) algorithm and matching theory, a matching learning-based relay selection (MLRS) algorithm is proposed to minimize the energy consumption of SPIoT devices. Simulation results demonstrate that MLRS outperforms existing algorithms in terms of energy consumption and optimal selection probability.

Highlights

Substation has great significance to ensure long-distance power transmission and stable power supply [1]
A relay selection model of substation power Internet of things (SPIoT) network considering electromagnetic interference in complex substation environment is shown in Figure 1. e SPIoT network consists of two parts, i.e., SPIoT devices and gateway. e gateway provides decision-making and computing services for SPIoT
To solve the problem of incomplete global information in preference lists’ construction of matching theory, matching learning-based relay selection (MLRS) utilizes upper confidence bound (UCB) to enable the implementation of iterative matching without precise global state information (GSI) and utilizes matching theory to avoid selection conflicts, which achieves a stable matching based on the learned preference lists

Summary

Introduction

Substation has great significance to ensure long-distance power transmission and stable power supply [1]. In [13], Mousavi et al proposed a relay subset selection method for two-hop WSN These works ignore the optimization of energy consumption, and it is infeasible for SPIoT devices with limited battery. Motivated by the aforementioned challenges, we propose a matching learning-based relay selection (MLRS) algorithm to minimize the energy consumption of SPIoT devices. (i) Learning-based matching preference construction without precise GSI: MLRS utilizes UCB to learn to construct preference lists based on historical observations of relay node selection times and empirical energy consumption performances. (iii) Low energy consumption: MLRS can dynamically learn the relay selection preference, i.e., the historical energy consumption, effectively reducing transmission energy consumption based on the optimal relay selection strategy.

System Model

Matching Learning-Based Relay Selection for SPIoT

Performance Analysis

Simulation Results

Conclusion