摘要
大数据分析下研究基站异常节点信息定位方法对确定医疗事故、森林火灾等危险事件的具体发生地点,及时采取搜索和营救工作具有重要作用。传统DV-Hop定位方法设计的根据基站异常节点接收离其最近的锚节点的平均跳距值来实现定位,存在定位精度较低,平稳定位误差稳定性较差的问题,提出一种基于误差修正的定位方法,计算大数据分析下基站异常节点与传感器锚节点之间的最小跳数和基站异常节点与传感器锚节点之间的距离;利用三边测量法或者极大似然估计法计算基站异常节点位置坐标;在DV-Hop定位方法的第二阶段针对基站异常节点平均跳距以及跳数信息进行修正;第三阶段采用加权处理的平均跳距估值法对基站异常节点的坐标位置计算方法进行修正,明显提高了定位精度。仿真结果表明,所提方法大大降低了基站异常节点信息的平均定位误差,且平均定位误差波动范围较小。
In the big data analysis, it is very important to research the location method of the abnormal node in- formation. Traditionally, DV - Hop positioning method has low positioning accuracy and poor stability of stable posi- tioning error. Therefore, a localization method based on error correction was proposed. Firstly, this method calculated the minimum hop count and the distance between base station abnormal nodes and sensor anchor nodes under the big data analysis. Then, the method used the trilateration method or the maximum likelihood estimation method to calculate the location coordinate of anomaly node in base station. In the second phase of DV - Hop positioning meth- od, the average hop distance of anomaly node in base station and the hop information were corrected. In the third phase, our method used the weighted average hop distance estimation method to correct the calculation method of coordinate position of anomaly node in base station. Thus, the positioning accuracy was improved obviously. Simulation results show that the proposed method can greatly reduce the average location error of abnormal node information in base station. Meanwhile, the fluctuation range of average location error is small.
作者
常逢佳
CHANG Feng - jia(College of Computer Science and Technology,Nanjing University of Technology,Nanjing Jiangsu 211800,China)
出处
《计算机仿真》
北大核心
2018年第8期297-301,共5页
Computer Simulation
关键词
大数据分析
基站
异常节点
信息
定位
Big data analysis
Base station
Abnormal node
Information
Positioning
