We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network

which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed

unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood

estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length

principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective

for managing outliers in the data.

},
keywords = {compass, wsn},
author = {R. L{\'o}pez-Valcarce and Daniel Romero and Josep Sala and Alba Pag{\`e}s-Zamora}
}
@conference {812,
title = {Distributed AoA-based Source Positioning in NLOS with Sensor Networks},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2015},
month = {04/2015},
publisher = {IEEE},
organization = {IEEE},
address = {Brisbane, Australia},
abstract = {This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non-line-of-sight (NLOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.

},
keywords = {compass, wsn},
author = {Pere Gimenez-Febrer and Alba Pag{\`e}s-Zamora and Silvana Silva Pereira and R. L{\'o}pez-Valcarce}
}
@conference {811,
title = {Distributed TLS Estimation under Random Data Faults},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2015},
month = {04/2015},
publisher = {IEEE},
organization = {IEEE},
address = {Brisbane, Australia},
abstract = {This paper addresses the problem of distributed estimation of a parameter vector in the presence of noisy input and output data as well as data faults, performed by a wireless sensor network in which only local interactions among the nodes are allowed. In the presence of unreliable observations, standard estimators become biased and perform poorly in low signal-to-noise ratios. We propose two different distributed approaches based on the Expectation-Maximization algorithm: in the first one the regressors are estimated at each iteration,

whereas the second one does not require explicit regressor estimation. Numerical results show that the proposed methods approach the performance of a clairvoyant scheme with knowledge of the random data faults.

},
keywords = {compass, wsn},
author = {Silvana Silva Pereira and Alba Pag{\`e}s-Zamora and R. L{\'o}pez-Valcarce}
}
@conference {731,
title = {Distributed Total Least Squares Estimation over Networks},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
year = {2014},
address = {Florence, Italy},
abstract = {We consider Total Least Squares (TLS) estimation in a network in which each node has access to a subset of equations of an overdetermined linear system. Previous distributed approaches require that the number of equations at each node be larger than the dimension L of the unknown parameter. We present novel distributed TLS estimators which can handle as few as a single equation per node. In the first scheme, the network computes an extended correlation matrix via standard iterative average consensus techniques, and the TLS estimate is extracted afterwards by means of an eigenvalue decomposition (EVD). The second scheme is EVD-free, but requires that a linear system of size L be solved at each iteration by each node. Replacing this step by a single Gauss-Seidel subiteration is shown to be an effective means to reduce computational cost without sacrificing performance.

}, keywords = {dynacs, wsn}, doi = {10.1109/ICASSP.2014.6855074}, author = {R. L{\'o}pez-Valcarce and Silvana Silva Pereira and Alba Pag{\`e}s-Zamora} } @conference {684, title = {A Diffusion-based distributed EM algorithm for density estimation in wireless sensor networks}, booktitle = {Int. Conf. Acoust., Speech, Signal Process. (ICASSP)}, year = {2013}, abstract = {Distributed implementations of the Expectation-Maximization

(EM) algorithm reported in the literature have been proposed for

applications to solve specific problems. In general, a primary

requirement to derive a distributed solution is that the

structure of the centralized version enables the computation

involving global information in a distributed fashion. This

paper treats the problem of distributed estimation of Gaussian

densities by means of the EM algorithm in wireless sensor

networks using diffusion strategies, where the information

is gradually diffused across the network for the computation

of the global functions. The low-complexity implementation

presented here is based on a two time scale operation

for information averaging and diffusion. The convergence to

a fixed point of the centralized solution has been studied and

the appealing results motivates our choice for this model. Numerical

examples provided show that the performance of the

distributed EM is, in practice, equal to that of the centralized

scheme.

},
keywords = {dynacs, wsn},
doi = {10.1109/ICASSP.2013.6638501},
author = {Silvana Silva Pereira and Alba Pag{\`e}s-Zamora and R. L{\'o}pez-Valcarce}
}
@article {682,
title = {A Diffusion-Based EM Algorithm for Distributed Estimation in Unreliable Sensor Networks},
journal = {IEEE Signal Processing Letters},
volume = {20},
year = {2013},
month = {06/2013},
pages = {595-598},
chapter = {595},
abstract = {We address the problem of distributed estimation of a parameter from a set of noisy observations collected by a sensor network, assuming that some sensors may be subject to data failures and report only noise. In such scenario, simple schemes such as the Best Linear Unbiased Estimator result in an error floor in moderate and high signal-to-noise ratio (SNR), whereas previously proposed methods based on hard decisions on data failure events degrade as the SNR decreases. Aiming at optimal performance within the whole range of SNRs, we adopt a Maximum Likelihood framework based on the Expectation-Maximization (EM) algorithm. The statistical model and the iterative nature of the EM method allow for a diffusion-based distributed implementation, whereby the information propagation is embedded in the iterative update of the parameters. Numerical examples show that the proposed algorithm practically attains the Cramer{\textendash}Rao Lower Bound at all SNR values and compares favorably with other approaches.

}, keywords = {dynacs, wsn}, doi = {10.1109/LSP.2013.2260329}, author = {Silvana Silva Pereira and R. L{\'o}pez-Valcarce and Alba Pag{\`e}s-Zamora} }