Distributed event-region detection in wireless sensor networks
EURASIP Journal on Advances in Signal Processing
Distributed field estimation with randomly deployed, noisy, binary sensors
IEEE Transactions on Signal Processing
Incremental distributed identification of Markov random field models in wireless sensor networks
IEEE Transactions on Signal Processing
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
A collaborative sensor-fault detection scheme for robust distributed estimation in sensor networks
IEEE Transactions on Communications
IEEE Transactions on Signal Processing
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
IEEE Transactions on Signal Processing
Estimation of target location via likelihood approximation in sensor networks
IEEE Transactions on Signal Processing
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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