基于Voronoi图划分的节点模糊信息定位算法
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摘要
针对基于接收信号强度的无线传感器网络节点定位算法精度低的问题,提出一种基于Voronoi图划分的节点模糊信息定位算法。根据锚节点个数对定位区域进行Voronoi图划分,将整个定位区域划分为不同的Voronoi区域,同时获得各个Voronoi区域的顶点坐标。使用高斯滤波方法筛选出可以作为参考节点的顶点坐标,通过顶点坐标和锚节点联合定位未知节点。利用模糊信息定位方法计算出未知节点的最终位置。实验结果表明,相比M ANLFI算法和FINL-DT算法,该算法能够有效提高节点定位精度,降低网络能耗。
In order to solve the problem of low accuracy of node localization algorithm based on Received Signal Strength Indicator( RSSI) in wireless sensor network,a node fuzzy information location algorithm based on Voronoi graph partition is proposed named NFIL-VD. According to the number of anchor nodes,the location area is divided into different Voronoi cell,and the vertex coordinates of each Voronoi cell are obtained. The vertex nodes which can be used as reference nodes are selected by the Gaussian filtering method,and the unknown nodes are located jointly by vertex nodes and anchor nodes. The final location of unknow n nodes is calculated by fuzzy information location method.Experimental results show that compared with MANLFI algorithm and FINL-DT algorithm,this algorithm can effectively improve node localization accuracy and reduce network energy consumption.
In order to solve the problem of low accuracy of node localization algorithm based on Received Signal Strength Indicator( RSSI) in wireless sensor network,a node fuzzy information location algorithm based on Voronoi graph partition is proposed named NFIL-VD. According to the number of anchor nodes,the location area is divided into different Voronoi cell,and the vertex coordinates of each Voronoi cell are obtained. The vertex nodes which can be used as reference nodes are selected by the Gaussian filtering method,and the unknown nodes are located jointly by vertex nodes and anchor nodes. The final location of unknow n nodes is calculated by fuzzy information location method.Experimental results show that compared with MANLFI algorithm and FINL-DT algorithm,this algorithm can effectively improve node localization accuracy and reduce network energy consumption.
引文
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[2]LI J,ZHONG X,LU I T.Three-dimensional node localization algorithm for WSN based on differential rss irregular transmission model[J].Journal of Communications,2015,9(5):391-397.
[3]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.
[4]朱青青,杨玉斌,刘娜,等.无线传感器网络中基于RSSI一致性的安全定位方法[J].计算机工程,2016,42(10):151-157,163.
[5]钱志鸿,孙大洋,LEUNG V.无线网络定位综述[J].计算机学报,2016,39(6):1237-1256.(下转第页)
[6]张祎江,余金森,郝平.基于线性参数加权评估机制的无线传感器网络节点定位[J].计算机工程,2017,43(2):156-162.
[7]严长虹,马静.三维传感网空间RSS与AOA混合测量的精确定位方法[J].传感技术学报,2017,30(3):450-455.
[8]孙大洋,钱志鸿,韩梦飞,等.无线传感器网络中多边定位的聚类分析改进算法[J].电子学报,2014,42(8):1601-1607.
[9]李瑶怡,赫晓星,刘守印.基于路径损耗模型参数实时估计的无线定位方法[J].传感技术学报,2010,23(9):1328-1333.
[10]王瑞.基于模糊信息处理的传感器网络覆盖定位方法研究[D].西安:西安交通大学,2009.
[11]党小超,李芬芳,郝占军.Delaunay三角剖分的节点模糊信息三维定位方法[J].计算机工程与应用,2016,52(23):115-122.
[12]党小超,李芬芳,郝占军.基于移动锚节点的模糊信息三维定位算法[J].计算机工程,2015,41(5):77-82,88.
[13]刘刚,袁纪武,李磊,等.基于Delaunay三角网的任意多边形三角剖分算法研究[J].计算机与数字工程,2012,40(6):121-123.
[14]WANG J,HUANG L,XU H,et al.Novel range free localization scheme based on Voronoi diagrams in w ireless sensor netw orks[J].Journal of Computer Research and Development,2008,45(1):119-125.
[15]LARIOS D F,BARBANCHO J,MOLINA F J,et al.LIS:localization based on an intelligent distributed fuzzy system applied to a WSN[J].Ad Hoc Netw orks,2012,10(3):604-622.
[16]MAO S,ZHAO C L.Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO[J].The Journal of China Universities of Posts and Telecommunications,2011,18(6):89-97.
[17]钟阳晶,梁茹冰,黄小虎.基于RSSI高斯滤波的LSSVR无线传感网络定位算法[J].现代电子技术,2017,40(11):6-13.
[18]FANG X,JIANG Z H,NAN L,et al.Noise-aware localization algorithms for wireless sensor networks based on multidimensional scaling and adaptive Kalman filtering[J].Computer Communications,2017,101:57-68.
[19]曾碧,毛勤.改进的室内三维模糊位置指纹定位算法[J].山东大学学报,2015,45(3):22-27.