[1]周子昂,徐 坤,程 全,等.人工蜂群优化神经网络的无线传感器节点定位算法[J].南京理工大学学报(自然科学版),2017,41(04):466.[doi:10.14177/j.cnki.32-1397n.2017.41.04.011]
　Zhou Ziang,Xu Kun,Cheng Quan,et al.Node localization of wireless sensor network by using artificial bee colony algorithm optimizing neural network[J].Journal of Nanjing University of Science and Technology,2017,41(04):466.[doi:10.14177/j.cnki.32-1397n.2017.41.04.011]
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人工蜂群优化神经网络的无线传感器节点定位算法()
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《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年04期
页码:
466
栏目:
出版日期:
2017-08-31

文章信息/Info

Title:
Node localization of wireless sensor network by using artificial bee colony algorithm optimizing neural network
文章编号:
1005-9830(2017)04-0466-06
作者:
周子昂1; 徐 坤2; 程 全1; 刘玉春1
周口师范学院 1.机械与电气工程学院; 2.网络工程学院,河南 周口 466001
Author(s):
Zhou Ziang1; Xu Kun2; Cheng Quan1; Liu Yuchun1
1.College of Mechanical and Electrical Engineering; 2.College of Network Engineering,Zhoukou Normal University,Zhoukou 466001,China
关键词:
室内定位;  无线传感器网络;  人工蜂群优化算法;  神经网络;  锚节点;  仿真实验
Keywords:
indoor localization;  wireless sensor network;  artificial bee colony optimization algorithm;  neural network;  anchor nodes;  simulation experiment
分类号:
TP393
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.011
摘要:
为了改善传感器节点定位性能,提出了人工蜂群优化神经网络的无线传感器节点定位算法。首先测量3个锚节点与定位传感器节点之间的参数,然后采用人工蜂群优化神经网络对测距误差进行建模与预测,并根据检测结果确定权重,最后根据三边定位算法进一步提高定位精度,并采用仿真实验测试其有效性。结果表明,该文算法提高了定位的精度,加快了定位的速度,定位实时性优异。
Abstract:
In order to obtain the ideal results of node localization for wireless sensor network,a novel node localization of wireless sensor network based on an artificial bee colony algorithm optimizing neural network is proposed.Firstly,three anchor nodes are selected and parameters between localization sensor nodes are obtained according to the measurement model; secondly, an artificial bee colony algorithm optimizing neural network is used to predict ranging errors,and location errors are corrected to determine the weight; finally,node localization results are obtained according to the three-edge location algorithm.The performance is analyzed through the specific test experiment.The results show that the proposed model improves the node localization accuracy and has better node localization and real-time performance.

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备注/Memo

备注/Memo:
收稿日期:2016-11-15 修回日期:2017-01-09基金项目:国家自然科学基金(61401526); 河南省教育技术装备与实践教育研究项目(GZS309)
作者简介:周子昂(1981-),男,硕士,讲师,主要研究方向:嵌入式系统应用,数字集成电路设计,物联网技术,E-mail:zzang66@126.com。
引文格式:周子昂,徐坤,程全,等.人工蜂群优化神经网络的无线传感器节点定位算法[J].南京理工大学学报,2017,41(4):466-471.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-08-31