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Information fusion for wireless sensor networks
 Methods, models, and classifications, ACM Computing Surveys, Volume 39, Issue 3, Article 9
, 2007
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Distributed Kalman filtering based on consensus strategies
, 2007
"... In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman ..."
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Cited by 56 (1 self)
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In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalmanlike measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of exchanged messages per sampling time is small. Moreover, we showed that although the joint optimization of the consensus matrix and the Kalman gain is in general a nonconvex problem, it is possible to compute them under some important scenarios. We also provide some numerical examples to clarify some of the analytical results and compare them with alternative estimation strategies.
Channel aware distributed detection in wireless sensor networks
 IEEE Signal Processing Mag
, 2006
"... [The integration of wireless channel conditions in algorithm design] In a distributed detection (DD) system, multiple sensors/detectors work collaboratively to distinguish between two or more hypotheses, e.g., the absence or presence of a target. While DD can be traced back to the advent of democrac ..."
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Cited by 54 (9 self)
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[The integration of wireless channel conditions in algorithm design] In a distributed detection (DD) system, multiple sensors/detectors work collaboratively to distinguish between two or more hypotheses, e.g., the absence or presence of a target. While DD can be traced back to the advent of democracy and associated voting schemes, one of its earliest formal treatments can be found in the work of Radner in the early 1960s, [1] who considered the problem of decision making by a team of multiple persons. Each person has access to different information and independently makes his/her decision. The coupling (i.e., the concept of a “team”) lies in the fact that the payoff function of the decisionmaking process depends on all the decisions and the state of situation in an inseparable way.
Linear Coherent Decentralized Estimation
"... Abstract—We consider the distributed estimation of an unknown vector signal in a resource constrained sensor network with a fusion center. Due to power and bandwidth limitations, each sensor compresses its data in order to minimize the amount of information that needs to be communicated to the fusio ..."
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Cited by 47 (1 self)
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Abstract—We consider the distributed estimation of an unknown vector signal in a resource constrained sensor network with a fusion center. Due to power and bandwidth limitations, each sensor compresses its data in order to minimize the amount of information that needs to be communicated to the fusion center. In this context, we study the linear decentralized estimation of the source vector, where each sensor linearly encodes its observations and the fusion center also applies a linear mapping to estimate the unknown vector signal based on the received messages. We adopt the mean squared error (MSE) as the performance criterion. When the channels between sensors and the fusion center are orthogonal, it has been shown previously that the complexity of designing the optimal encoding matrices is NPhard in general. In this paper, we study the optimal linear decentralized estimation when the multiple access channel (MAC) is coherent. For the case when the source and observations are scalars, we derive the optimal power scheduling via convex optimization and show that it admits a simple distributed implementation. Simulations show that the proposed power scheduling improves the MSE performance by a large margin when compared to the uniform power scheduling. We also show that under a finite network power budget, the asymptotic MSE performance (when the total number of sensors is large) critically depends on the multiple access scheme. For the case when the source and observations are vectors, we study the optimal linear decentralized estimation under both bandwidth and power constraints. We show that when the MAC between sensors and the fusion center is noiseless, the resulting problem has a closedform solution (which is in sharp contrast to the orthogonal MAC case), while in the noisy MAC case, the problem can be efficiently solved by semidefinite programming (SDP). Index Terms—Distributed estimation, energy efficiency, multiple access channel, linear sourcechannel coding, convex optimization. I.
Giannakis, “Consensus in Ad Hoc WSNs with Noisy Links  Part I: Distributed Estimation of Deterministic Signals
 IEEE Transactions on Singal Processing
, 2008
"... Abstract—We deal with distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs). We cast the decentralized estimation problem as the solution of multiple constrained convex optimization subproblems. Using the method of multipliers in conjunction with a bl ..."
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Cited by 33 (3 self)
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Abstract—We deal with distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs). We cast the decentralized estimation problem as the solution of multiple constrained convex optimization subproblems. Using the method of multipliers in conjunction with a block coordinate descent approach we demonstrate how the resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation. Different from existing alternatives, our approach does not require the centralized estimator to be expressible in a separable closed form in terms of averages, thus allowing for decentralized computation even of nonlinear estimators, including maximum likelihood estimators (MLE) in nonlinear and nonGaussian data models. We prove that these algorithms have guaranteed convergence to the desired estimator when the sensor links are assumed ideal. Furthermore, our decentralized algorithms exhibit resilience in the presence of receiver and/or quantization noise. In particular, we introduce a decentralized scheme for leastsquares and best linear unbiased estimation (BLUE) and establish its convergence in the presence of communication noise. Our algorithms also exhibit potential for higher convergence rate with respect to existing schemes. Corroborating simulations demonstrate the merits of the novel distributed estimation algorithms. Index Terms—Distributed estimation, nonlinear optimization, wireless sensor networks (WSNs). I.
Distributed fusion in sensor networks  a graphical models perspective
 IEEE SIGNAL PROCESSING MAG
, 2006
"... Distributed inference methods developed for graphical models comprise a principled approach for data fusion in sensor networks. The application of these methods, however, requires some care due to a number of issues that are particular to sensor networks. Chief of among these are the distributed na ..."
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Cited by 21 (0 self)
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Distributed inference methods developed for graphical models comprise a principled approach for data fusion in sensor networks. The application of these methods, however, requires some care due to a number of issues that are particular to sensor networks. Chief of among these are the distributed nature of computation and deployment coupled with communications bandwidth and energy constraints typical of many sensor networks. Additionally, information sharing in a sensor network necessarily involves approximation. Traditional measures of distortion are not sufficient to characterize the quality of approximation as they do not address in an explicit manner the resulting impact on inference which is at the core of many data fusion problems. While both graphical models and a distributed sensor network have network structures associated with them, the mapping is not one to one. All of these issues complicate the mapping of a particular inference problem to a given sensor network structure. Indeed, there may be a variety of mappings with very different characteristics with regard to computational complexity and utilization of resources. Nevertheless, it is the case that many of the powerful distributed inference methods have a role in information fusion for sensor networks. In this article we present an overview of research conducted by the authors that has
Sensing Reality and Communicating Bits: A Dangerous Liaison
, 2006
"... [Is digital communication sufficient for sensor networks?] The successful design of sensor network architectures depends crucially on the structure of the sampling, observation, and communication processes. One of the most fundamental questions concerns the sufficiency of discrete approximations in ..."
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Cited by 20 (1 self)
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[Is digital communication sufficient for sensor networks?] The successful design of sensor network architectures depends crucially on the structure of the sampling, observation, and communication processes. One of the most fundamental questions concerns the sufficiency of discrete approximations in time, space, and amplitude. More explicitly, to capture the spatiotemporal variations of the underlying signals, when is it sufficient to build sensor network systems that work with discretetime andspace representations? And can the underlying amplitude variations of interest be observed at the highest possible fidelity if the sensors quantize their observations, assuming that quantization is done in the most sophisticated fashion, exploiting the principles of (ideal) distributed source coding? The former can be rephrased as the question of whether there is a spatiotemporal sampling theorem for typical data sets in sensor networks. This question has a positive answer in many cases of interest, based on the physics of the processes to be observed. The latter can be expressed as the question of whether there is a
Giannakis, “Distributed recursive leastsquares for consensusbased innetwork adaptive estimation
 IEEE Trans. Signal Process
, 2009
"... Abstract—The recursive leastsquares (RLS) algorithm has welldocumented merits for reducing complexity and storage requirements, when it comes to online estimation of stationary signals as well as for tracking slowlyvarying nonstationary processes. In this paper, a distributed recursive leastsqu ..."
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Cited by 16 (3 self)
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Abstract—The recursive leastsquares (RLS) algorithm has welldocumented merits for reducing complexity and storage requirements, when it comes to online estimation of stationary signals as well as for tracking slowlyvarying nonstationary processes. In this paper, a distributed recursive leastsquares (DRLS) algorithm is developed for cooperative estimation using ad hoc wireless sensor networks. Distributed iterations are obtained by minimizing a separable reformulation of the exponentiallyweighted leastsquares cost, using the alternatingminimization algorithm. Sensors carry out reducedcomplexity tasks locally, and exchange messages with onehop neighbors to consent on the networkwide estimates adaptively. A steadystate meansquare error (MSE) performance analysis of DRLS is conducted, by studying a stochasticallydriven ‘averaged ’ system that approximates the DRLS dynamics asymptotically in time. For sensor observations that are linearly related to the timeinvariant parameter vector sought, the simplifying independence setting assumptions facilitate deriving accurate closedform expressions for the MSE steadystate values. The problems of mean and MSEsense stability of DRLS are also investigated, and easilycheckable sufficient conditions are derived under which a steadystate is attained. Without resorting to diminishing stepsizes which compromise the tracking ability of DRLS, stability ensures that per sensor estimates hover inside a ball of finite radius centered at the true parameter vector, with highprobability, even when intersensor communication links are noisy. Interestingly, computer simulations demonstrate that the theoretical findings are accurate also in the pragmatic settings whereby sensors acquire temporallycorrelated data. Index Terms—Distributed estimation, performance analysis, RLS algorithm, wireless sensor networks (WSNs). I.
Decentralized sparse signal recovery for compressive sleeping wireless sensor networks
 IEEE Trans. Signal Process. 2010
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Decomposable Principal Component Analysis
"... Abstract—In this paper, we consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute PCA computation. For this purpose, we reformulate the PCA problem in the sparse inverse covariance (concentration) ..."
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Cited by 10 (6 self)
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Abstract—In this paper, we consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute PCA computation. For this purpose, we reformulate the PCA problem in the sparse inverse covariance (concentration) domain and address the global eigenvalue problem by solving a sequence of local eigenvalue problems in each of the cliques of the decomposable graph. We illustrate our methodology in the context of decentralized anomaly detection in the Abilene backbone network. Based on the topology of the network, we propose an approximate statistical graphical model and distribute the computation of PCA. Index Terms—Anomaly detection, graphical models, principal