摘要
为降低无线传感器网络(WSNs)在节点众多时算法复杂度,提高定位精度,提出一种基于K—means聚类点密度的WSNs加权质心定位算法(KCPD—WCLA)。首先,对空间中随机大量布设的锚节点进行分组,利用三边测量定位法在二维平面上得到许多接近真实值的结果;然后将K—means聚类算法引入到WSNs的定位问题中,对K个聚类点密度加以考虑,利用加权质心定位算法(WCLA)得到定位结果。理论分析与仿真结果表明:计算复杂度明显降低,定位精度比多边定位算法(MLA)和WCLA有显著提高。
In order to reduce wireless sensor networks（ WSNs） algorithm complexity when there are lots of anchor nodes,and improve positioning precision,propose a weighted centroid localization algorithm based on K-means clustering point density（ KCPD-WCLA）. First,group a large number of anchor nodes which are randomly deployed in space,and use trilateral measurement positioning method to obtain many results close to real value in twodimensional（ 2D） plane; then,K-means clustering algorithm is introduced to WSNs localization problem,take K clustering point density into consideration that position results can be achieved by using the weighted centroid localization algorithm（ WCLA）. Theoretical analysis and simulation results show that computational complexity is significantly reduced,positioning precision is improved significantly compared with multilateral localization algorithm（ MLA） and WCLA.
出处
《传感器与微系统》
CSCD
2015年第7期125-127,131,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(41374039)
中国-波兰国际科技合作项目(35-14)
关键词
无线传感器网络
K—means聚类
加权质心定位算法
wireless sensor networks（WSNs）
K-means clustering
weighted centroid localization algorithm（WCLA）
