基于MOFTLBTV的WSNs定位算法  

WSNs localization algorithm based on MOFTLBTV

作　　者：陈业纲[1] 徐则同[2] Chen Yegang Xu Zetong(School of Mathematics ＆ Computer Science, Yangtze Normal University, Chongqing 408000, China Institute of Mathematics ＆ System Science, Chinese Academy of Sciences, Beifing 100190, China)

机构地区：[1]长江师范学院数学与计算机学院,重庆408000 [2]中国科学院数学与系统科学研究院,北京100190

出　　处：《计算机应用研究》2016年第12期3801-3804,共4页Application Research of Computers

基　　金：国家教育部春晖计划资助项目(Z2015148)

摘　　要：传感节点感测数据易受到干扰,导致传感节点获取的数据出错。为此,提出基于二值数据的多目标容错定位算法(MOFTLBTV)。该算法研究传感节点的差错概率的情况,利用传感节点的二值数据对目标源进行识别及定位。在识别过程中利用分布式竞争领导者(DCL)算法产生领导者(leader)节点。通过估计leader节点数实现对目标源的识别。随后定位阶段采用基于网格投票(GBV)机制对目标源进行定位。在条件下将MOFTLBTV与DNLEP算法在对两个或多个目标源的识别、定位性能进行了三方面进行了对比,MOFTLBTV算法在噪声和差错情况下,保持高的定位性能,在差错概率为0.25的环境,均方根误差小于8 m,其性能远优于DNLEP算法。The nodes in sensor networks are vulnerable to numerous factors when sensing data, which lead to the error of the data obtained from the node. For this purpose, this paper proposed a multi objective fault tolerant location algorithm based on two valued data （MOFTLBTV）. This algorithm first calculated the error probability of the node in the sensor network, and made use of the two value data of the sensor node to identify and locate the target source. During identifying the use of distributed competitive leader （DCL） algorithm to generate a header node. By calculating the number of leader nodes to achieve the identification of the target source. The positioning stage was based on the grid voting （GBV） mechanism to locate the target source. Under Rc = 15, Rs = 1.5 conditions, MOFTLBTV and DNLEP algorithms were compared in three aspects, with two target source identification, multiple target recognition and localization performance. In the case of noise and error MOFTLBTV algorithm maintaining high performance in error probability of 0.25, root mean square error is lower than 8 m of the environment, the performance is far better than DNLEP algorithm.

关 键 词：多目标源 定位算法 网格选举 二值数据 

分 类 号：TN915.07[电子电信—通信与信息系统]