基于大数据技术的无线传感网络异常节点深度挖掘方法

Deep Mining Method of Anomaly Nodes in Wireless Sensing Network Based on Big Data Technology

由于无线传感网络节点分布不规则,难以有效控制对异常节点的定位误差,为此提出基于大数据技术的无线传感网络异常节点深度挖掘方法。在节点均分布在目标区域范围内的前提下,构建无线传感网络组网模型,设置异常节点与网络中心节点之间的关系函数,之后采用大数据技术中的邻域搜索算法,对具体的网络异常节点位置进行深度挖掘定位。实验结果显示,基于大数据技术的无线传感网络异常节点深度挖掘方法在不同噪声状态下的异常节点定位误差始终稳定在2.0以内。

Due to the irregularity of node distribution in wireless sensor network,it is difficult to effectively control the positioning error of abnormal nodes.Therefore,the deep mining method of abnormal nodes in wireless sensor network based on big data technology is proposed.On the premise that all nodes are distributed within the scope of the target area,the wireless sensor network networking model is constructed,the relationship between the abnormal node and the network center node of the network is set up.Then,the neighborhood search algorithm in big data technology is adopted to conduct in-depth mining and positioning of the specific network abnormal node locations.The experimental results show that the abnormal node location error of the deep mining method of abnormal node in wireless sensor network based on big data technology is stable within 2.0 under different noise states.