[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.

参考文献/References:

[1] 朱红松,孙利民.无线传感器网络技术发展现状[J].中兴通讯技术,2009,15(5):1-5.
Zhu Hongsong,Sun Liming.Development status of wireless sensor network[J].ZTE Communications,2009,15(5):1-5.
[2]Yick J,Mukherjee B,Ghosal D.Wireless sensor network survey[J].Computer Networks,2012,52(12):2292-2330.
[3]Garg V,Jhamb M.A review of wireless sensor network on localization techniques[J].International Journal of Engineering Trends and Technology,2013,4(4):1049-1053.
[4]Chaurasiya V K,Jain N,Nandi G C.A novel distance estimation approach for 3D localization in wireless sensor network using multidimensional scaling[J].Information Fusion,2014,15(1):5-18.
[5]Priyadarshini K J,Ganesh A B.Improvisation of localization algorithm for wireless sensor networks[J].Procedia Engineering,2012,38(6):1186-1191.
[6]Zhao Jijun,Zhao Qingwei,Li Zhihua,et al.An improved weighted centroid localization algorithm based on difference of estimated distances for wireless sensor networks[J].Telecommunication Systems,2013,53(1):25-31.
[7]田浩杉,李翠然,谢健骊,等.基于时序蒙特卡洛的WSN节点定位算法[J].传感技术学报,2016,29(11):1724-1731.
Tian Haoshan,Li Cuiran,Xie Jianli,et al.Node localization algorithm for WSN based on time sequence Monte Carlo[J].Chinese Journal of Sensors and Actuators,2016,29(11):1724-1731.
[8]夏少波,朱晓丽,邹建梅.基于跳数修正的DV-Hop改进算法[J].传感技术学报,2015,28(5):757-762.
Xia Shaoho,Zhu Xiaoli,Zou Jianmei.The improved DV-Hop algorithm based on hop count[J].Chinese Journal of Sensors and Actuators,2015,28(5):757-762.
[9]李津蓉,王万良,介婧,等.结合极大似然距离估计的MDS-MAP节点定位算法[J].传感技术学报,2016,29(4):572-577.
Li Jinrong,Wang Wanliang,Jie Jing,et al.Localization algorithm for wireless sensor networks based on MDS-MAP integrated with maximum likelihood estimating[J].Chine Journal of Sensor and Actuators,2016,29(4):572-577.
[10]曹世华,王琦晖,王李东.基于邻域旋跳迭代机制的无线传感器网络节点定位[J].计算机工程,2016,42(7):94-99.
Cao Shihua,Wang Qihui,Wang Lidong.Wireless sensor network node localization based on iterative mechanism of neighborhood rotation and hopping[J].Computer Engineering,2016,42(7):94-99.
[11]倪志伟,李蓉蓉,方清华,等.基于离散人工蜂群算法的云任务调度优化[J].计算机应用,2016,36(1):107-112,121.
Ni Zhiwei,Li Rongrong,Fang Qinghua,et al.Optimization of cloud task scheduling based on discrete artificial bee colony algorithm[J].Journal of Computer Applications,2016,36(1):107-112,121.
[12]刘士兴,黄俊杰,刘宏银,等.基于多通信半径的加权DV-Hop定位算法[J].传感技术学报,2015,28(6):883-887.
Liu Shixing,Huang Junjie,Liu Hongyin,et al.An improving DV-Hop algorithm based on multi communication radius[J].Chinese Journal of Sensors and Actuators,2015,28(6):883-887.
[13]魏祥麟,胡永扬,王晓波,等.基于度分布的多跳无线网络干扰节点部署方法[J].南京理工大学学报,2015,39(5):590-595.
Wei Xianglin,Hu Yongyang,Wang Xiaobo,et al.Jammer deployment in multi-hop wireless network based on degree distribution[J].Journal of Nanjing University of Science and Technology,2015,39(5):590-595.

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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