Local failure detection and diagnosis in large sensor networks

Details Local failure detection and diagnosis in large sensor networks Local failure detection and diagnosis in large sensor networks

2011-07-07

arxiv.org/abs/1107.1409v1

Computer Science

Information Theory

Submitted to IEEE Transactions on Information Theory, 2011

Scientific paper

In this article, the joint fluctuations of the extreme eigenvalues and eigenvectors of a large dimensional sample covariance matrix are analyzed when the associated population covariance matrix is a finite-rank perturbation of the identity matrix, which corresponds to a so-called spiked model in random matrix theory. The asymptotic fluctuations, as the matrix size grows large, are shown to be intimately linked with matrices from the Gaussian unitary ensemble (GUE). When the spiked population eigenvalues have unit multiplicity, the fluctuations follow a central limit theorem. This result is used to develop an original framework for the detection and diagnosis of local failures in large sensor networks, for known or unknown failure magnitude. Simulations are carried out that show the strong performance of the derived failure localization algorithms.

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