Coverage Optimization Strategy in Wireless Sensor Networks Based on Natural Selection Particle Swarm Optimization Algoritnm

CUI Li-zhen; LI Xiao-yu; HU Hai-dong; SHI Ming-quan

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Coverage Optimization Strategy in Wireless Sensor Networks Based on Natural Selection Particle Swarm Optimization Algoritnm

更新时间：2026-09-04

- Coverage Optimization Strategy in Wireless Sensor Networks Based on Natural Selection Particle Swarm Optimization Algoritnm
- Vol. 35, Issue 4, Pages: 73-78(2018)
- 作者机构：
  
  内蒙古科技大学机构信息工程学院,内蒙古,包头,014000
- 作者简介：
- 基金信息：
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- Published：2018
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崔丽珍, 李晓宇, 胡海东, 史明泉. 基于自然选择粒子群算法的WSN覆盖优化策略[J]. 微电子学与计算机, 2018, 35(4): 73-78.

CUI Li-zhen, LI Xiao-yu, HU Hai-dong, et al. Coverage Optimization Strategy in Wireless Sensor Networks Based on Natural Selection Particle Swarm Optimization Algoritnm[J]. 2018, 35(4): 73-78.
崔丽珍, 李晓宇, 胡海东, 史明泉. 基于自然选择粒子群算法的WSN覆盖优化策略[J]. 微电子学与计算机, 2018, 35(4): 73-78. DOI：

CUI Li-zhen, LI Xiao-yu, HU Hai-dong, et al. Coverage Optimization Strategy in Wireless Sensor Networks Based on Natural Selection Particle Swarm Optimization Algoritnm[J]. 2018, 35(4): 73-78. DOI：

摘要

在由静态节点和少量移动节点构成的混合无线传感器网络中

针对节点初始覆盖不均匀导致网络覆盖率低下的问题

提出了一种自然选择粒子群算法的覆盖优化方法.首先建立节点的Neyman-Pearson感知模型

将监测区域网格化并定义求解网络覆盖率的目标优化函数; 其次采用融入自然选择思想的粒子群算法对目标函数进行优化

实验结果表明

该算法可以有效提高网络覆盖率

改善网络监测质量

延长网络生存时间.

Abstract

In the hybrid wireless sensor networks composed of stationary nodes and mobile nodes

aim at the problem of low coverage in networks

due to the uneven initial deployment of nodes

proposed a coverage optimization algorithm based on natural selection particle swarm optimization. Firstly

build the Neyman-Pearson nodes sensor model

meshing the monitoring area and define the objective optimization function of networks coverage rate; Then

adopted the PSO algorithm blended in natural selection idea to optimize the objective function

Experimental results show that the algorithm can increase the coverage rate effectively

improve monitoring quality

prolong the survival time of networks.

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Keywords

references

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Related Institution

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