融合判决门限和信任过滤机制的WSN异常节点定位方法  

WSN Abnormal Node Localization Method Integrating Judgment Threshold and Trust Filtering Mechanism

作　　者：贾文雅 杨红菊[2] JIA Wenya;YANG Hongju(Department of Cosmetics and Medical Aesthetics,Shanxi Pharmaceutical Vocational College,Taiyuan Shanxi 030031,China;College of Computer and Information Technology,Shanxi University,Taiyuan Shanxi 030006,China)

机构地区：[1]山西药科职业学院化妆品医美系,山西太原030031 [2]山西大学计算机与信息技术学院,山西太原030006

出　　处：《传感技术学报》2024年第10期1820-1826,共7页Chinese Journal of Sensors and Actuators

基　　金：山西省教育厅科技创新项目(2022L676)。

摘　　要：由于网络拓扑频繁变化使无线传感网络(Wireless Sensor Network,WSN)中的节点会移动或失效,从而造成传感器节点所产生数据具有不确定性,且节点异常具有多模态特性,易造成错定漏定的问题。因此,提出融合判决门限和信任过滤机制的WSN异常节点定位方法。通过传感器位置特征组建K-近邻图信号函数捕捉节点间的空间关系;利用低通滤波前后的平滑度差异设计判决门限来减少数据不确定性的影响,实现WSN异常节点检测;通过Beta分布初步信任评价锚点位置信息,调节信任更新权重来适应拓扑变化,引入信任过滤机制差异化处理WSN节点,并通过簇头节点判断锚点可信度,实现WSN异常节点精准定位。结果表明,所提方法异常节点定位虚警概率在1%以下,OPR值可达到99.12%,具有较高的定位精度。Due to frequent changes in network topology,nodes in wireless sensor networks(WSNs)may move or fail,resulting in uncer tainty in the data generated by sensor nodes.Node anomalies also exhibit multimodal characteristics,which can easily lead to issues of incorrect or missed detections.Therefore,a WSN anomaly node localization method that integrates decision threshold and trust filtering mechanism is proposed.A K-nearest neighbor graph signal function based on sensor location features is built to capture spatial relation ships between nodes.A decision threshold based on the difference in smoothness before and after low-pass filtering is design to reduce the impact of data uncertainty and achieve WSN anomaly node detection.By using Beta distribution to preliminarily evaluate anchor point location information,adjusting trust update weights to adapt to topology changes,and introducing trust filtering mechanisms to dif ferentiate WSN nodes,the credibility of anchor points is determined by cluster head nodes,achieving accurate localization of WSN ab normal nodes.The results show that the proposed method has a false alarm probability of less than 1%for abnormal node localization,and the OPR value can reach 99.12%,indicating high localization accuracy.

关 键 词：无线传感网络 异常节点定位 判决门限 信任过滤机制 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]