Application of improved QGA in WSNs node deployment

QIAN Cheng; CHEN Shu; WANG Fu-dong; XU Bao-guo

doi:10.13873/j.1000-97872013.11.021

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Application of improved QGA in WSNs node deployment

更新时间：2025-09-11

- Application of improved QGA in WSNs node deployment
- Vol. 32, Issue 11, Pages: 149-152(2013)
- 作者机构：
  
  江南大学物联网工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872013.11.021
  CLC： TN929.5;TP212.9
- Published：2013
- 稿件说明：
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[1]钱成,陈树,王夫栋,徐保国.改进QGA在WSNs节点部署中的应用[J].传感器与微系统,2013,32(11):149-152.

QIAN Cheng, CHEN Shu, WANG Fu-dong, et al. Application of improved QGA in WSNs node deployment[J]. 2013, 32(11): 149-152.
[1]钱成,陈树,王夫栋,徐保国.改进QGA在WSNs节点部署中的应用[J].传感器与微系统,2013,32(11):149-152. DOI： 10.13873/j.1000-97872013.11.021.

QIAN Cheng, CHEN Shu, WANG Fu-dong, et al. Application of improved QGA in WSNs node deployment[J]. 2013, 32(11): 149-152. DOI： 10.13873/j.1000-97872013.11.021.

摘要

对含有障碍区域的无线传感器网络（WSNs）节点部署问题进行研究。建立节点探测模型和网络覆盖率评价方法

基于概率传感器模型提出一种部署方式

即对障碍区域进行随机布撒节点

确定区域采用量子遗传算法（QGA）寻找最优节点部署位置

实现对同构WSNs节点构成的目标区域的高效覆盖。仿真结果与GA

QGA相比:改进QGA有效提高了算法整体的搜索能力和收敛速度。

Abstract

Aiming at problem of node deployment of wireless sensor networks（ WSNs） in areas with obstacle is researched. Node detection models and network coverage rate evaluation method are set up. Based on probabilistic model of sensor

a deployment method is proposed. Random deployment is employed for the obstacle areas. Then quantum genetic algorithm（ QGA） is used to find the optimal node deployment positions. Thus

the target area isomorphism WSNs nodes is efficiently covered is realized. The simulation result that modified QGA effectively improve overall searching ability and convergence speed of algorithm compared with GA and QGA.

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references

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