无线传感器网络疑误数据节点自动诊断方法

为了提高无线传感器网络疑误数据检测能力,提出基于轮换调度的无线传感器网络疑误数据节点自动诊断方法。通过采用分块区域特征匹配的方法,得到无线传感器网络疑误数据传输的梯度模型,采用资源优化分配方案,进行数据传输信道的均衡调度,得到节点部署分布模型。通过传感信息跟踪采样方法,得到采样信息分布,建立无线传感器网络疑误数据信息特征分析,通过分组特征检测方法进行无线传感器网络疑误数据的信息融合和空间融合调度,提取无线传感器网络疑误数据的关联规则特征集,通过统计信息分析和融合调度的方法,进行无线传感器网络疑误数据的聚类挖掘,采用预算估计算法,得到疑误数据节点定位优化,结合自主学习算法,实现无线传感器网络疑误数据节点的优化定位和诊断检测。仿真结果表明,采用该方法进行无线传感器网络疑误数据节点检测的自适应性较好,特征辨识能力较强。

Automatic diagnosis method for suspicious data node in wireless sensor network

In order to improve the ability of false data detection in wireless sensor networks,an automatic diagnosis method of false data nodes in wireless sensor networks based on rotation scheduling is proposed.By adopting the method of block area feature matching,the gradient model of the wireless sensor network’s suspected data transmission is obtained,and the resource optimization allocation plan is adopted to perform the balanced scheduling of the data transmission channel,and the node deployment distribution model is obtained.Through the sensor information tracking sampling method,the sampling information distribution is obtained,the wireless sensor network suspected error data information feature analysis is established,and the wireless sensor network suspected error data information fusion and spatial fusion scheduling are carried out through the group feature detection method,and the wireless sensor network suspected errors are extracted the data association rule feature set,through statistical information analysis and fusion scheduling methods,cluster mining of suspected data in wireless sensor networks,budget estimation algorithm is used to optimize the location of suspected data nodes,and autonomous learning algorithms are used to realize wireless sensors Optimized location and diagnosis of network suspected data nodes.Simulation results show that this method has good adaptability and strong feature recognition ability in detecting suspected data nodes in wireless sensor networks.