基于IPSO-MC融合算法的无线传感器节点定位

李勇燕; 吴坚平

doi:10.11959/j.issn.1000-0801.2020063

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基于IPSO-MC融合算法的无线传感器节点定位

专题：工业互联网平台及安全 | 更新时间：2024-06-05

- 基于IPSO-MC融合算法的无线传感器节点定位
- Wireless sensor node localization based on IPSO-MC
- 电信科学 2020年36卷第3期页码：11-18
- 作者机构：
  
  1. 绍兴文理学院上虞分院，浙江绍兴 312300
  2. 浙江大学，浙江杭州 310058
- 作者简介：
  
  [ "李勇燕（1981- ），女，绍兴文理学院上虞分院讲师，主要研究方向为无线传感器" ]
  [ "吴坚平（1983- ），男，浙江大学博士生，主要研究方向为复杂异构数据融合" ]
- 基金信息：
  
  浙江省重点研发计划基金资助项目;The Key Research and Development Projects of Zhejiang Province of China(2018C03052)
- DOI：10.11959/j.issn.1000-0801.2020063
  中图分类号： TP391
- 网络出版日期：2020-03，
  
  纸质出版日期：2020-03-20
- 稿件说明：
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李勇燕, 吴坚平. 基于IPSO-MC融合算法的无线传感器节点定位[J]. 电信科学, 2020,36(3):11-18.

Yongyan LI, Jianping WU. Wireless sensor node localization based on IPSO-MC[J]. Telecommunications science, 2020, 36(3): 11-18.
李勇燕, 吴坚平. 基于IPSO-MC融合算法的无线传感器节点定位[J]. 电信科学, 2020,36(3):11-18. DOI： 10.11959/j.issn.1000-0801.2020063.

Yongyan LI, Jianping WU. Wireless sensor node localization based on IPSO-MC[J]. Telecommunications science, 2020, 36(3): 11-18. DOI： 10.11959/j.issn.1000-0801.2020063.

摘要

针对无线传感器网络（WSN）中的节点定位精度不足的问题，提出一种基于优化粒子算法与膜计算（improved particle swarm optimization by membrane computing，IPSO-MC）的融合算法。采用Kent映射初始化种群和引入领域粒子提高粒子群全局优化能力，通过权重因子和非线性极值扰动提高粒子群的局部优化能力，利用Levy飞行机制优化个体位置，借助膜计算的进化规则最终获得粒子群算法的最优解。仿真实验表明，与鸡群算法、改进的粒子群算法和膜计算的定位算法相比，本文算法在参考节点比例指标对比中分别提高3.24%、5.12%和8.15%、在节点个数指标对比中分别提高2.26%、7.82%和9.81%以及在通信半径指标对比中分别提高2.15%、5.5%和7.5%，说明该算法在节点定位方面具有良好的效果。

Abstract

To solve the problem of insufficient node positioning accuracy in wireless sensor networks

an algorithm based on improved particle swarm optimization by membrane computing (IPSO-MC) was proposed.Kent mapping was used to initialize the population and domain particles were introduced to improve the global optimization of the particle swarm.The weight factor and nonlinear extreme value perturbation were used to improve the local optimization ability of the particle swarm

and the Levy flight mechanism was used to optimize the individual position.Finally

the optimal solution of the particle swarm algorithm was obtained by the evolutionary rules of the membrane computing.Simulation experiments show that compared with the chicken flock algorithm

the improved particle swarm algorithm and the membrane computing

the proposed algorithm improves 3.24%

5.12% and 8.15% in the comparison of reference node ratio indicators

and the increase in the number of nodes indicators by 2.26%

7.82% and 9.81%

and the comparison of communication radius indicators increased by 2.15%

5.5% and 7.5%

respectively.This indicates that the algorithm has a good effect in node localization.

关键词

Keywords

references

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