基于蝙蝠拟牛顿混合算法的无线传感器网络节点定位

doi:10.11772/j.issn.1001-9081.2015.05.1238

计算机应用 ›› 2015, Vol. 35 ›› Issue (5): 1238-1241.DOI: 10.11772/j.issn.1001-9081.2015.05.1238

基于蝙蝠拟牛顿混合算法的无线传感器网络节点定位

于泉¹, 孙顺远^1,2, 徐保国¹, 陈淑娟³, 黄艳丽⁴

1. 江南大学物联网工程学院, 江苏无锡 214122;
2. 轻工过程先进控制教育部重点实验室(江南大学), 江苏无锡 214122;
3. 恒启电子(苏州)有限公司, 江苏苏州 215000;
4. 山东师范大学传媒学院, 济南 250000

收稿日期:2014-12-15 修回日期:2015-02-04 发布日期:2015-05-14 出版日期:2015-05-10
通讯作者: 孙顺远
作者简介:于泉(1990-),男,江苏徐州人,硕士研究生,主要研究方向:无线传感器网络; 孙顺远(1984-),男,山东淄博人,博士研究生,主要研究方向:无线传感器网络; 徐保国(1951-),男,江苏淮安人,教授,主要研究方向:无线传感器网络、现场总线; 陈淑娟(1989-),女,江苏徐州人,工程师,主要研究方向:无线传感器网络; 黄艳丽(1990-),女,江苏徐州人,硕士研究生,主要研究方向:教育游戏.
基金资助:
江苏省研究生培养创新工程项目(CXZZ11_0465);江南大学博士研究生科学研究基金资助项目(JUDCF11003).

Node localization of wireless sensor networks based on hybrid bat-quasi-Newton algorithm

YU Quan¹, SUN Shunyuan^1,2, XU Baoguo¹, CHEN Shujuan³, HUANG Yanli⁴

1. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, (Jiangnan University), Wuxi Jiangsu 214122, China;
3. Henrich Electronic (Suzhou) Company Limited, Suzhou Jiangsu 215000, China;
4. School of Communication, Shandong Normal University, Jinan Shandong 250000, China

Received:2014-12-15 Revised:2015-02-04 Online:2015-05-14 Published:2015-05-10

摘要/Abstract

摘要：

针对距离矢量-跳数(DV-Hop)算法第三阶段中最小二乘法定位精度低的问题,提出一种蝙蝠-拟牛顿混合算法与DV-Hop算法融合的定位算法.首先对蝙蝠算法进行两点改进:1)根据蝙蝠个体的适应度值自适应调节随机向量β,使得脉冲频率具有自适应能力;2)利用当前迭代之前所有最优个体的平均位置来引导蝙蝠移动,使得速度具有变异性能;然后在DV-Hop算法第三阶段采用改进蝙蝠算法得出节点的估计位置,再利用拟牛顿算法以估计位置为初始点继续搜索节点位置.仿真结果表明:相比传统DV-Hop算法和基于蝙蝠算法的DV-Hop改进算法(BADV-Hop),该算法的定位精度大约提高了16.5%、5.18%,且稳定性更好,适用于定位精度和稳定性要求较高的场合.

关键词: 无线传感器网络, DV-Hop算法, 蝙蝠算法, 拟牛顿算法

Abstract:

Concerning the problem that the least square method in the third stage of DV-Hop algorithm has low positioning accuracy, a localization algorithm was proposed which is the fusion of hybrid bat-quasi-Newton algorithm and DV-Hop algorithm. First of all, the Bat Algorithm (BA) was improved from two aspects: firstly, the random vector β was adjusted adaptively according to bats' fitness so that the pulse frequency had the adaptive ability. Secondly, bats were guided to move by the average position of all the best individuals before the current iteration so that the speed had variable performance; Then in the third stage of DV-Hop algorithm the improved bat algorithm was used to estimate node location and then quasi-Newton algorithm was used to continue searching for the node location from the estimated location as the initial searching point. The simulation results show that, compared with the traditional DV-Hop algorithm and the improved algorithm of DV-Hop based on bat algorithm(BADV-Hop), positioning precision of the proposed algorithm increases about 16.5% and 5.18%, and the algorithm has better stability, it is suitable for high positioning precision and stability situation.

Key words: Wireless Sensor Networks (WSN), DV-Hop algorithm, Bat Algorithm (BA), quasi-Newton algorithm

中图分类号:

TP393

于泉, 孙顺远, 徐保国, 陈淑娟, 黄艳丽. 基于蝙蝠拟牛顿混合算法的无线传感器网络节点定位[J]. 计算机应用, 2015, 35(5): 1238-1241.

YU Quan, SUN Shunyuan, XU Baoguo, CHEN Shujuan, HUANG Yanli. Node localization of wireless sensor networks based on hybrid bat-quasi-Newton algorithm[J]. Journal of Computer Applications, 2015, 35(5): 1238-1241.

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基于蝙蝠拟牛顿混合算法的无线传感器网络节点定位

Node localization of wireless sensor networks based on hybrid bat-quasi-Newton algorithm

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