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    Robust Distributed Estimation Using the Embedded Subgraphs Algorithm

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    Author
    Delouille, Veronique; Neelamani, Ramesh; Baraniuk, Richard G.
    Date
    2006-08-01
    Abstract
    We propose a new iterative, distributed approach for linear minimum mean-square-error (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a number of linked embedded subgraphs and applies the classical parallel block Jacobi iteration comprising local LMMSE estimation in each subgraph (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and subgraphs. Our primary application is sensor networks, where the model encodes the correlation structure of the sensor measurements, which are assumed to be Gaussian. The resulting LMMSE estimation problem involves a large matrix inverse, which must be solved in-network with distributed computation and minimal intersensor communication. By invoking the theory of asynchronous iterations, we prove that ESA is robust to temporary communication faults such as failing links and sleeping nodes, and enjoys guaranteed convergence under relatively mild conditions. Simulation studies demonstrate that ESA compares favorably with other recently proposed algorithms for distributed estimation. Simulations also indicate that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, sensor network energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually prove counterproductive. Our results can be replicated using MATLAB code from www.dsp.rice.edu/software.
    Description
    Journal Paper
    Citation
    V. Delouille, R. Neelamani and R. G. Baraniuk, "Robust Distributed Estimation Using the Embedded Subgraphs Algorithm," IEEE Transactions on Signal Processing, vol. 54, no. 8, 2006.
    Published Version
    http://dx.doi.org/10.1109/TSP.2006.874839
    Keyword
    Asynchronous iterations; Wiener filter; distributed estimation; graphical models; matrix splitting; More... sensor networks; DSP for Communications; Asynchronous iterations; Wiener filter; distributed estimation; graphical models; matrix splitting; sensor networks Less...
    Type
    Journal article
    Citable link to this page
    https://hdl.handle.net/1911/19856
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    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map