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Robust distributed estimation using the embedded subgraphs algorithm (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Delouille, Véronique Neelamani, Ramesh “neelsh Baraniuk, Richard G. |
| Abstract | 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. Index Terms—Asynchronous iterations, distributed estimation, graphical models, matrix splitting, sensor networks, Wiener filter. I. |
| File Format | |
| Journal | IEEE Trans. Signal Process |
| Language | English |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Embedded Subgraphs Algorithm Sensor Network Graphical Model Sensor Network Energy Conservation Strategy Energy Consumption Loopy Graphical Model Aggressive Sleep Schedule Matrix Splitting Temporary Communication Fault New Iterative Information Exchange Sensor Measurement Large Matrix Inverse Distributed Computation Linear Minimum Mean-square-error Asynchronous Iteration Minimal Intersensor Communication Matlab Code Wiener Filter Index Term Asynchronous Iteration Distributed Estimation Primary Application Low-powered Transmission Iterative Estimation Increase Embedded Subgraphs Classical Parallel Block Jacobi Iteration Simulation Study Local Lmmse Estimation Lmmse Estimation Problem Node Sleep Correlation Structure Small Matrix Mild Condition |
| Content Type | Text |
| Resource Type | Article |
