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123
Distributed average consensus with leastmeansquare deviation
 Journal of Parallel and Distributed Computing
, 2005
"... We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and network synchronization. In this model, each node updates its local variable with a weighted averag ..."
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Cited by 82 (5 self)
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We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and network synchronization. In this model, each node updates its local variable with a weighted average of its neighbors ’ values, and each new value is corrupted by an additive noise with zero mean. The quality of consensus can be measured by the total meansquare deviation of the individual variables from their average, which converges to a steadystate value. We consider the problem of finding the (symmetric) edge weights that result in the least meansquare deviation in steady state. We show that this problem can be cast as a convex optimization problem, so the global solution can be found efficiently. We describe some computational methods for solving this problem, and compare the weights and the meansquare deviations obtained by this method and several other weight design methods.
Consensus propagation
 IEEE Transactions on Information Theory
"... Abstract — We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network. We establish convergence, characterize the convergence rate for regular graphs, and demonstrate that the protocol exhibits better scaling properties than pairwise averaging, an a ..."
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Cited by 61 (6 self)
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Abstract — We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network. We establish convergence, characterize the convergence rate for regular graphs, and demonstrate that the protocol exhibits better scaling properties than pairwise averaging, an alternative that has received much recent attention. Consensus propagation can be viewed as a special case of belief propagation, and our results contribute to the belief propagation literature. In particular, beyond singlyconnected graphs, there are very few classes of relevant problems for which belief propagation is known to converge. Index Terms — belief propagation, distributed averaging, distributed consensus, distributed signal processing, Gaussian Markov random fields, messagepassing algorithms, maxproduct algorithm, minsum algorithm, sumproduct algorithm. I.
Decentralized Compression and Predistribution via Randomized Gossiping
 in Proc. of the Fifth International Symposium on Information Processing in Sensor Networks (IPSN
, 2006
"... Developing energy efficient strategies for the extraction, transmission, and dissemination of information is a core theme in wireless sensor network research. In this paper we present a novel system for decentralized data compression and predistribution. The system simultaneously computes random pro ..."
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Cited by 60 (11 self)
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Developing energy efficient strategies for the extraction, transmission, and dissemination of information is a core theme in wireless sensor network research. In this paper we present a novel system for decentralized data compression and predistribution. The system simultaneously computes random projections of the sensor data and disseminates them throughout the network using a simple gossiping algorithm. These summary statistics are stored in an efficient manner and can be extracted from a small subset of nodes anywhere in the network. From these measurements one can reconstruct an accurate approximation of the data at all nodes in the network, provided the original data is compressible in a certain sense which need not be known to the nodes ahead of time. The system provides a practical and universal approach to decentralized compression and content distribution in wireless sensor networks. Two example applications, network health monitoring and field estimation, demonstrate the utility of our method.
Quantized consensus
, 2007
"... We study the distributed averaging problem on arbitrary connected graphs, with the additional constraint that the value at each node is an integer. This discretized distributed averaging problem models several problems of interest, such as averaging in a network with finite capacity channels and loa ..."
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Cited by 56 (0 self)
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We study the distributed averaging problem on arbitrary connected graphs, with the additional constraint that the value at each node is an integer. This discretized distributed averaging problem models several problems of interest, such as averaging in a network with finite capacity channels and load balancing in a processor network. We describe simple randomized distributed algorithms which achieve consensus to the extent that the discrete nature of the problem permits. We give bounds on the convergence time of these algorithms for fully connected networks and linear networks.
Information Fusion for Wireless Sensor Networks: Methods, Models, and Classifications
"... Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sens ..."
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Cited by 33 (2 self)
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Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current stateoftheart of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and
Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals
"... Abstract—Distributed algorithms are developed for optimal estimation of stationary random signals and smoothing of (even nonstationary) dynamical processes based on generally correlated observations collected by ad hoc wireless sensor networks (WSNs). Maximum a posteriori (MAP) and linear minimum me ..."
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Cited by 31 (5 self)
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Abstract—Distributed algorithms are developed for optimal estimation of stationary random signals and smoothing of (even nonstationary) dynamical processes based on generally correlated observations collected by ad hoc wireless sensor networks (WSNs). Maximum a posteriori (MAP) and linear minimum meansquare error (LMMSE) schemes, well appreciated for centralized estimation, are shown possible to reformulate for distributed operation through the iterative (alternatingdirection) method of multipliers. Sensors communicate with singlehop neighbors their individual estimates as well as multipliers measuring how far local estimates are from consensus. When iterations reach consensus, the resultant distributed (D) MAP and LMMSE estimators converge to their centralized counterparts when intersensor communication links are ideal. The DMAP estimators do not require the desired estimator to be expressible in closed form, the DLMMSE ones are provably robust to communication or quantization noise and both are particularly simple to implement when the data model is linearGaussian. For decentralized tracking applications, distributed Kalman filtering and smoothing algorithms are derived for anytime MMSE optimal consensusbased state estimation using WSNs. Analysis and corroborating numerical examples demonstrate the merits of the novel distributed estimators. Index Terms—Distributed estimation, Kalman smoother, nonlinear optimization, wireless sensor networks (WSNs).
Distributed detection in sensor networks with packet losses and finite capacity links
 IEEE Transactions on Signal Processing
, 2006
"... We consider a multiobject detection problem over a sensor network (SNET) with limited range multimodal sensors. Limited range sensing environment arises in a sensing field prone to signal attenuation and path losses. The general problem complements the widely considered decentralized detection pro ..."
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Cited by 28 (1 self)
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We consider a multiobject detection problem over a sensor network (SNET) with limited range multimodal sensors. Limited range sensing environment arises in a sensing field prone to signal attenuation and path losses. The general problem complements the widely considered decentralized detection problem where all sensors observe the same object. In this paper we develop a distributed detection approach based on recent development of the false discovery rate (FDR) and the associated BH test procedure. The BH procedure is based on rank ordering of scalar test statistics. We first develop scalar test statistics for multidimensional data to handle multimodal sensor observations and establish its optimality in terms of the BH procedure. We then propose a distributed algorithm in the ideal case of infinite attenuation for identification of sensors that are in the immediate vicinity of an object. We demonstrate communication message scalability to large SNETs by showing that the upper bound on the communication message complexity scales linearly with the number of sensors that are in the vicinity of objects and is independent of the total number of sensors in the SNET. This brings forth an important principle for evaluating the performance of an SNET, namely, the need for scalability of communications and performance with respect to the number of objects or events in an SNET irrespective of the network size. We then account for finite attenuation by modeling sensor observations as corrupted by uncertain interference arising from distant objects and developing robust extensions to our idealized distributed scheme. The robustness properties ensure that both the error performance and communication message complexity degrade gracefully with interference. 1
Distributed and collaborative estimation over wireless sensor networks
 in IEEE Conference on Decision and Control
, 2006
"... Abstract — A new distributed algorithm for cooperative estimation of a slowly timevarying signal using a wireless sensor network is presented. The estimate in each node is based on a so called consensus algorithm, which weights measurements and estimates of neighboring nodes. The algorithm is there ..."
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Cited by 24 (10 self)
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Abstract — A new distributed algorithm for cooperative estimation of a slowly timevarying signal using a wireless sensor network is presented. The estimate in each node is based on a so called consensus algorithm, which weights measurements and estimates of neighboring nodes. The algorithm is therefore scalable with the number of network nodes. It requires only limited information exchange between nodes and computations in each node. The weights are locally optimized based on a minimum variance criterion. Numerical results show that the proposed algorithm exhibits good performance compared to other distributed algorithms proposed in the literature. I.
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 21 (0 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.