@article {913, title = {Parameter estimation in wireless sensor networks with faulty transducers: A distributed EM approach}, journal = {Signal Processing}, volume = {144}, year = {2018}, month = {03/2018}, pages = {226-237}, abstract = {

We address the problem of distributed estimation of a vector-valued parameter performed by a wireless sensor network in the presence of noisy observations which may be unreliable due to faulty transducers. The proposed distributed estimator is based on the Expectation-Maximization (EM) algorithm and combines consensus and diffusion techniques: a term for information diffusion is gradually turned off, while a term for updated information averaging is turned on so that all nodes in the network approach the same value of the estimate. The proposed method requires only local exchanges of information among network nodes and, in contrast with previous approaches, it does not assume knowledge of the a priori probability of transducer failures or the noise variance. A convergence analysis is provided, showing that the convergent points of the centralized EM iteration are locally asymptotically convergent points of the proposed distributed scheme. Numerical examples show that the distributed algorithm asymptotically attains the performance of the centralized EM method.

}, keywords = {winter, wsn}, doi = {10.1016/j.sigpro.2017.10.012}, url = {https://authors.elsevier.com/a/1W90XbZX4rsob}, author = {Silvana Silva Pereira and R. L{\'o}pez-Valcarce 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 {733, title = {How to Implement Doubly-Stochastic Matrices for Consensus-Based Distributed Algorithms}, booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)}, year = {2014}, address = {A Coru{\~n}a, Spain}, abstract = {
Doubly-stochastic matrices are usually required by
consensus-based distributed algorithms. We propose a simple
and efficient protocol and present some guidelines for implementing
doubly-stochastic combination matrices even in noisy,
asynchronous and changing topology scenarios. The proposed
ideas are validated with the deployment of a wireless sensor
network, in which nodes run a distributed algorithm for robust
estimation in the presence of nodes with faulty sensors.
}, keywords = {compass, dynacs, wsn}, doi = {10.1109/SAM.2014.6882409}, author = {S. Valc{\'a}rcel-Macua and C. Moreno-Le{\'o}n and J. S. Romero and Silvana Silva Pereira and Javier Zazo and Alba Pag{\`e}s-Zamora and R. L{\'o}pez-Valcarce and S. Zazo} } @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} }