基于多属性关联决策的WSN故障检测方法

WSN Fault Detection Method Based on Correlative Multi-attribute Decision-making

针对无线传感器网络中节点单一属性表征能力差且阈值范围难以确定的问题,提出一种基于多属性关联决策的分布式故障检测方法。以非均匀分簇网络结构为基础,融合距离因子与节点间相关分析结果估计簇头置信区间。在可靠簇头条件下,通过显著性检验方法对簇内成员节点状态进行反馈式决策,并基于相对熵理论定义多属性关联度,用于分析异常数据来源。实验结果表明,面对不同故障类型,该方法均能有效提高网络节点的故障检测精度并确定异常来源,在节约能耗的基础上,确保网络稳定运行。

To address the problems that the single attribute characterization ability of the node in Wireless Sensor Network(WSN)is poor and the threshold range is difficult to determine,this paper proposes a distributed fault detection method based on correlative multi-attribute decision-making.Based on the non-uniform clustering network structure,the confidence interval of the cluster head is estimated by combining the distance factor with the correlation analysis result between nodes.Under the condition of reliable cluster head,it makes feedback decision on the state of the member nodes in the cluster through the significance test method,and analyzes the source of the abnormal data through the multi-attribute relevance degree defined by the relative entropy theory.Experimenal results show that,in the face of different fault types,the proposed method can improve the fault detection accuracy of the network nodes effectively and determine the source of abnormal data.On the basis of energy saving,it can ensure the stable operation of the network.