@inbook {956, title = {Expectation{\textendash}maximisation based distributed estimation in sensor networks}, booktitle = {Data Fusion in Wireless Sensor Networks: A statistical signal processing perspective}, year = {2019}, pages = {201-230}, publisher = {The Institution of Engineering and Technology (IET)}, organization = {The Institution of Engineering and Technology (IET)}, chapter = {9}, address = {London, UK}, abstract = {

Estimating the unknown parameters of a statistical model based on the observations collected by a sensor network is an important problem with application in multiple fields. In this setting, distributed processing, by which computations are carried out within the network in order to avoid raw data transmission to a fusion centre, is a desirable feature resulting in improved robustness and energy savings. In the presence of incomplete data, the expectation-maximisation (EM) algorithm is a popular means to iteratively compute the maximum likelihood (ML) estimate. It has found application in diverse fields such as computational biology, anomaly detection, speech segmentation, reinforcement learning, and motion estimation, among others. In this chapter we will review the formulation of the centralised EM estimation algorithm as a starting point and then discuss distributed versions well suited for implementation in sensor networks. The first class of these distributed versions requires specialised routing through the network in terms of a linear or circular path visiting all nodes, whereas the second class does away with this requirement by using the concept of network consensus to diffuse information through the network. Our focus will be on a relevant sensor network application, in which the parameter of a linear model is to be estimated in the presence of an unknown number of randomly malfunctioning sensors.

}, keywords = {winter, wsn}, isbn = {978-1-78561-584-9}, author = {R. L{\'o}pez-Valcarce and Alba Pag{\`e}s-Zamora} } @conference {960, title = {Low-rank data matrix recovery with missing values and faulty sensors}, booktitle = {European Signal Processing Conference (EUSIPCO)}, year = {2019}, month = {09/2019}, address = {A Coru{\~n}a, Spain}, abstract = {

In practice, data gathered by wireless sensor networks often belongs in a low-dimensional subspace, but it can present missing as well as corrupted values due to sensor malfunctioning and/or malicious attacks. We study the problem of Maximum Likelihood estimation of the low-rank factors of the underlying structure in such situation, and develop an Expectation-Maximization algorithm to this purpose, together with an effective initialization scheme. The proposed method outperforms previous schemes based on an initial faulty sensor identification stage, and is competitive in terms of complexity and performance with convex optimization-based matrix completion approaches.

}, keywords = {winter, wsn}, author = {R. L{\'o}pez-Valcarce and Josep Sala} } @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} } @article {871, title = {CDMA-based Acoustic Local Positioning System for Portable Devices with Multipath Cancellation}, journal = {Digital Signal Processing}, volume = {62}, year = {2017}, month = {03/2017}, pages = {38-51}, abstract = {
This paper presents an acoustic local positioning system (ALPS) suitable for indoor positioning
of portable devices such as smartphones or tablets, based on the transmission of high frequency
CDMA-coded signals from a fixed network of beacons. The main novelty of the proposed ALPS is
its capability to mitigate the effects of multipath propagation by performing an accurate estimation
of the Line-of-Sight Time-of-Flights (LOS-TOF) through the Matching Pursuit algorithm. Signal
detection, multipath cancellation and positioning estimation all take place within the portable device,
which provides a graphical representation of the updated position in less than a second. The
performance of the Matching Pursuit algorithm is analyzed in a real scenario and the results show
that the proposed method is capable to retrieve the multipath-free System Availability under strong
multipath conditions with SNR levels as low as 0 dB.
}, keywords = {compass, wsn}, issn = {1051-2004}, doi = {10.1016/j.dsp.2016.11.001}, author = {Fernando J. Alvarez and Teodoro Aguilera and R. L{\'o}pez-Valcarce} } @conference {903, title = {Defending Surveillance Sensor Networks Against Data-Injection Attacks via Trusted Nodes}, booktitle = {European Signal Processing Conference (EUSIPCO)}, year = {2017}, month = {08/2017}, address = {Kos Island, Greece}, abstract = {
By injecting false data through compromised sensors, an adversary can drive the probability of detection in a sensor network-based spatial field surveillance system to arbitrarily low values. As a countermeasure, a small subset of sensors may be secured. Leveraging the theory of Matched Subspace Detection, we propose and evaluate several detectors that add robustness to attacks when such trusted nodes are available. Our results reveal the performance-security tradeoff of these schemes and can be used to determine the number of trusted nodes required for a given performance target.
}, keywords = {winter, wsn}, author = {R. L{\'o}pez-Valcarce and Daniel Romero} } @article {878, title = {Learning Power Spectrum Maps from Quantized Power Measurements}, journal = {IEEE Transactions on Signal Processing}, volume = {65}, year = {2017}, month = {05/2017}, pages = {2547-2560}, abstract = {
Using power measurements collected by a network of low-cost sensors, power spectral density (PSD) maps are con-
structed to capture the distribution of RF power across space and frequency. Linearly compressed and quantized power measure-
ments enable wideband sensing at affordable implementation complexity using a small number of bits. Strengths of data- and model-
driven approaches are combined to develop estimators capable of incorporating multiple forms of spectral and propagation prior
information while fitting the rapid variations of shadow fading across space. To this end, novel nonparametric and semiparametric
formulations are investigated. It is shown that the desired PSD maps can be obtained using support vector machine-type solvers.
In addition to batch approaches, an online algorithm attuned to real-time operation is developed. Numerical tests assess the performance of the novel algorithms.\ 
}, keywords = {cognitive radio, compass, compressed sensing, spectrum sensing, winter, wsn}, doi = {10.1109/TSP.2017.2666775}, author = {Daniel Romero and Seung-Jun Kim and Georgios Giannakis and R. L{\'o}pez-Valcarce} } @conference {876, title = {Robust clustering of data collected via crowdsourcing}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2017}, month = {03/2017}, pages = {4014 - 4018}, address = {New Orleans}, abstract = {

Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all annotators is suitably modeled here as a mixture of Gaussian components plus a uniformly distributed random variable to capture outliers. The proposed algorithm is based on the expectation-maximization algorithm and allows for soft assignments of data to clusters, to rate annotators according to their performance, and to estimate the number of Gaussian components in the non-Gaussian/Gaussian mixture model, in a jointly manner.

}, keywords = {winter, wsn}, doi = {10.1109/ICASSP.2017.7952910}, author = {Alba Pag{\`e}s-Zamora and Georgios Giannakis and R. L{\'o}pez-Valcarce and Pere Gimenez-Febrer} } @conference {846, title = {Design of data-injection adversarial attacks against spatial field detectors}, booktitle = {IEEE Workshop on Statistical Signal Processing (SSP)}, year = {2016}, month = {06/2016}, address = {Palma de Mallorca, Spain}, abstract = {

Data-injection attacks on spatial field detection corrupt a subset of measurements to cause erroneous decisions. We consider a centralized decision scheme exploiting spatial field smoothness to overcome lack of knowledge on system parameters such as noise variance. We obtain closed-form expressions for system performance and investigate strategies for an intruder injecting false data in a fraction of the sensors in order to reduce the probability of detection. The problem of determining the most vulnerable subset of sensors is also analyzed.

}, keywords = {adversarial signal processing, compass, wsn}, author = {R. L{\'o}pez-Valcarce and Daniel Romero} } @conference {845, title = {Distributed multivariate regression with unknown noise covariance in the presence of outliers: a minimum description length approach}, booktitle = {IEEE Workshop on Statistical Signal Processing (SSP)}, year = {2016}, address = {Palma de Mallorca, Spain}, abstract = {
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 {847, title = {Online EM-based distributed estimation in sensor networks with faulty nodes}, booktitle = {European Signal Processing Conference (EUSIPCO)}, year = {2016}, month = {09/2016}, address = {Budapest, Hungary}, abstract = {
This paper focuses on the problem of the distributed estimation of a parameter vector based on noisy observations regularly acquired by the nodes of a wireless sensor network and assuming that some of the nodes have faulty sensors. We propose two online schemes, both centralized and distributed, based on the Expectation-Maximization (EM) algorithm. These algorithms are able to identify and disregard the faulty nodes, and provide a refined estimate of the parameters each time instant after a new set of observations is acquired. Simulation results demonstrate that the centralized versions of the proposed online algorithms attain the same estimation error as the centralized batch EM, whereas the distributed versions come very close to matching the batch EM.
}, keywords = {compass, wsn}, author = {Pere Gimenez-Febrer and Alba Pag{\`e}s-Zamora and R. L{\'o}pez-Valcarce} } @conference {815, title = {Attack detectors for data aggregation in clustered sensor networks}, booktitle = {European Signal Processing Conf. (EUSIPCO)}, year = {2015}, month = {accepted}, pages = {2098-2102}, publisher = {EURASIP}, organization = {EURASIP}, address = {Nice, France}, abstract = {
Among many security threats to sensor networks, compromised sensing is particularly challenging due to the fact that it cannot be addressed by standard authentication approaches. We consider a clustered scenario for data aggregation in which an attacker injects a disturbance in sensor readings. Casting the problem in an estimation framework, we systematically apply the Generalized Likelihood Ratio approach to derive attack detectors. The analysis under different attacks reveals that detectors based on similarity of means across
clusters are suboptimal, with Bartlett{\textquoteright}s test for homoscedasticity constituting a good candidate when lacking a priori knowledge of the variance of the underlying distribution.
}, keywords = {adversarial signal processing, compass, wsn}, author = {R. L{\'o}pez-Valcarce and Daniel Romero} } @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} } @mastersthesis {798, title = {Estimation, Detection, and Learning for Dynamic Spectrum Access}, volume = {Ph.D.}, year = {2015}, month = {05/2015}, school = {University of Vigo}, type = {Ph.D. Thesis}, address = {Vigo, Spain}, keywords = {cognitive radio, compass, dynacs, wsn}, author = {Daniel Romero} } @article {767, title = {A Greedy Topology Design to Accelerate Consensus in Broadcast Wireless Sensor Networks}, journal = {Information Processing Letters}, volume = {115}, year = {2015}, month = {03/2015}, pages = {408-413}, chapter = {408}, abstract = {

We present techniques to improve convergence speed of distributed average consensus algorithms in wireless sensor networks by means of topology design. A broadcast network is assumed, so that only the transmit power of each node can be independently controlled, rather than each individual link. Starting with a maximally connected configuration in which all nodes transmit at full power, the proposed methods successively reduce the transmit power of a chosen node in order to remove one and only one link; nodes are greedily selected either in order to yield fastest convergence at each step, or if they have the largest degree in the network. These greedy schemes provide a good complexity-performance tradeoff with respect to full-blown global search methods. As a side benefit, improving the convergence speed also results in savings in energy consumption with respect to the maximally connected setting.

}, keywords = {compass, wsn}, doi = {10.1016/j.ipl.2014.11.009}, author = {Massimo Vecchio and R. L{\'o}pez-Valcarce} } @article {756, title = {Improving Area Coverage of Wireless Sensor Networks Via Controllable Mobile Nodes: a Greedy Approach}, journal = {Journal of Network and Computer Applications}, volume = {48}, year = {2015}, month = {02/2015}, pages = {1-13}, abstract = {
Reliable wide-area monitoring with Wireless Sensor Networks (WSNs) remains a problem of
interest: simply deploying more nodes to cover wider areas is generally not a viable solution, due to
deployment and maintenance costs and the increase in radio interference. One possible solution
gaining popularity is based on the use of a reduced number of mobile nodes with controllable
trajectories in the monitored field. In this framework, we present a distributed technique for iteratively
computing the trajectories of the mobile nodes in a greedy fashion. The static sensor nodes actively
assist the mobile nodes in this task by means of a bidding protocol, thus participating towards the goal
of maximizing the area coverage of the monitored field. The performance of the proposed technique is
evaluated on various simulation scenarios with different number of mobile and static nodes in terms of
achieved coverage and mean time to achieve X\% coverage. Comparison with previous state-of-the-art
techniques reveals the effectiveness and stability of the proposed method.
}, keywords = {compass, wsn}, doi = {10.1016/j.jnca.2014.10.007}, author = {Massimo Vecchio and R. L{\'o}pez-Valcarce} } @conference {809, title = {Multipath cancellation in broadband acoustic local positioning systems}, booktitle = {IEEE 9th International Symposium on Intelligent Signal Processing (WISP)}, year = {2015}, month = {05/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Siena, Italy}, abstract = {

This work proposes a novel method to estimate the Line-of-Sight Time-of-Flights (LOS-TOFs) in a broadband acoustic local positioning system (ALPS) with strong multipath interference. The proposed method is based on the Matching Pursuit channel estimation algorithm that provides a low complexity approximation to the Maximum Likelihood solution for sparse channels. A multichannel version of this algorithm has been implemented to estimate a minimum of three coefficients in the channel responses of a particular ALPS, composed of four beacons that perform the simultaneous emission of BPSK modulated 255-bit Kasami codes. A statistical analysis of performance has been carried out by using a set of test signals synthetically generated to simulate different positions and reflection coefficients of the environment. The results of this analysis show the enhanced capability of the proposed method to estimate the LOS-TOFs under strong multipath interference, with respect to that of a classical system based on correlation + thresholding.

}, keywords = {compass, wsn}, doi = {10.1109/WISP.2015.7139174}, author = {Fernando J. Alvarez and R. L{\'o}pez-Valcarce} } @conference {762, title = {Cooperative compressive power spectrum estimation}, booktitle = {IEEE Sensor Array Multichannel Signal Process. Workshop (SAM)}, year = {2014}, keywords = {cognitive radio, wsn}, author = {Dyonisius D Ariananda and Daniel Romero and Geert Leus} } @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} } @article {676, title = {On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks}, journal = {Engineering Applications of Artificial Intelligence}, volume = {26}, year = {2013}, month = {02/2013}, pages = {669-676}, chapter = {669}, keywords = {dynacs, wsn}, doi = {10.1016/j.engappai.2012.06.002}, author = {Diana Manjarres and Javier Del Ser and Sergio Gil-Lopez and Massimo Vecchio and Itziar Landa-Torres and Sancho Salcedo-Sanz and R. L{\'o}pez-Valcarce} } @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} } @article {677, title = {A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks}, journal = {Soft Computing}, volume = {17}, year = {2013}, month = {01/2013}, pages = {17-28}, chapter = {17}, keywords = {dynacs, wsn}, doi = {10.1007/s00500-012-0897-2}, author = {Diana Manjarres and Javier Del Ser and Sergio Gil-Lopez and Massimo Vecchio and Itziar Landa-Torres and R. L{\'o}pez-Valcarce} } @article {Vecchio2011, title = {A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks}, journal = {Applied Soft Computing}, volume = {12}, year = {2011}, month = {07/2012}, pages = {1891-1901}, abstract = {

To know the location of nodes plays an important role in many current and envisioned wireless sensor network applications. In this framework, we consider the problem of estimating the locations of all the nodes of a network, based on noisy distance measurements for those pairs of nodes in range of each other, and on a small fraction of anchor nodes whose actual positions are known a priori. The methods proposed so far in the literature for tackling this non-convex problem do not generally provide accurate estimates. The difficulty of the localization task is exacerbated by the fact that the network is not generally uniquely localizable when its connectivity is not sufficiently high. In order to alleviate this drawback, we propose a two-objective evolutionary algorithm which takes concurrently into account during the evolutionary process both the localization accuracy and certain topological constraints induced by connectivity considerations. The proposed method is tested with different network configurations and sensor setups, and compared in terms of normalized localization error with another metaheuristic approach, namely SAL, based on simulated annealing. The results show that, in all the experiments, our approach achieves considerable accuracies and significantly outperforms SAL, thus manifesting its effectiveness and stability.

}, keywords = {dynacs, Multiobjective evolutionary algorithms, Node localization, Range measurements, Stochastic optimization, wsn}, issn = {1568-4946}, doi = {10.1016/j.asoc.2011.03.012}, author = {Massimo Vecchio and R. L{\'o}pez-Valcarce and Francesco Marcelloni} }