基于随机矩阵理论的WSN异常节点定位算法

Abnormal Node Location Algorithm for WSN Based on Random Matrix Theory

为提高无线传感器网络异常节点检测精度并减小高维数据下的计算开销,通过引入随机矩阵理论(RMT),设计一种新型无线传感器网络异常节点定位算法。根据原始数据的时空特性建立大数据矩阵,利用随机矩阵对其做降维处理。在此基础上,将平均谱半径作为评价指标判断网络是否出现异常情况,并结合RMT中的谱分布定理和协方差矩阵奇异值分解性质对异常节点进行定位。仿真结果表明,与分布式故障检测算法相比,该算法在异常检测和节点定位上具有较高的准确性。

Existing abnormal node detection methods for Wireless Sensor Network(WSN)have difficulty in obtaining the statistical model,and the computational cost of large-dimensional data is high.To address the problem,this paper introduces the Random Matrix Theory(RMT)and designs a new abnormal node location algorithm for WSN.The algorithm uses the spatial and temporal features of raw data to construct a big data matrix,and reduces its dimensions using random matrix.On this basis,the algorithm takes average spectral radius as an evaluation index to generally judge whether an exception occurs in the network.The abnormal node is precisely located by using the spectral distribution theorem in RMT and properties of singular value decomposition of covariance matrix.Simulation results show that the proposed algorithm has a high accuracy rate in outlier detection and node location compared with Distributed Fault Detection(DFD)algorithm.