Abnormal Node Location Algorithm for WSN Based on Random Matrix Theory
摘要
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.