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A scheme for robust distributed sensor fusion based on average consensus
 PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN
, 2005
"... We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximumlikelihoo ..."
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Cited by 250 (3 self)
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We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximumlikelihood estimate of the parameters. This scheme doesn’t involve explicit pointtopoint message passing or routing; instead, it diffuses information across the network by updating each node’s data with a weighted average of its neighbors ’ data (they maintain the same data structure). At each step, every node can compute a local weighted leastsquares estimate, which converges to the global maximumlikelihood solution. This scheme is robust to unreliable communication links. We show that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected.
A Survey of Consensus Problems in Multiagent Coordination
, 2005
"... As a distributed solution to multiagent coordination, consensus or agreement problems have been studied extensively in the literature. This paper provides a survey of consensus problems in multiagent cooperative control with the goal of promoting research in this area. Theoretical results regard ..."
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Cited by 148 (3 self)
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As a distributed solution to multiagent coordination, consensus or agreement problems have been studied extensively in the literature. This paper provides a survey of consensus problems in multiagent cooperative control with the goal of promoting research in this area. Theoretical results regarding consensus seeking under both timeinvariant and dynamically changing information exchange topologies are summarized. Applications of consensus protocols to multiagent coordination are investigated. Future research directions and open problems are also proposed.
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 63 (5 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
Belief consensus and distributed hypothesis testing in sensor networks
 Network Embedded Sensing and Control. (Proceedings of NESC’05 Worskhop), volume 331 of Lecture Notes in Control and Information Sciences
, 2006
"... Summary. In this paper, we address distributed hypothesis testing (DHT) in sensor networks and Bayesian networks using the averageconsensus algorithm of OlfatiSaber & Murray. As a byproduct, we obtain a novel belief propagation algorithm called Belief Consensus. This algorithm works for connec ..."
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Cited by 57 (1 self)
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Summary. In this paper, we address distributed hypothesis testing (DHT) in sensor networks and Bayesian networks using the averageconsensus algorithm of OlfatiSaber & Murray. As a byproduct, we obtain a novel belief propagation algorithm called Belief Consensus. This algorithm works for connected networks with loops and arbitrary degree sequence. Belief consensus allows distributed computation of products of n beliefs (or conditional probabilities) that belong to n different nodes of a network. This capability enables distributed hypothesis testing for a broad variety of applications. We show that this belief propagation admits a Lyapunov function that quantifies the collective disbelief in the network. Belief consensus benefits from scalability, robustness to link failures, convergence under variable topology, asynchronous features of averageconsensus algorithm. Some connections between smallword networks and speed of convergence of belief consensus are discussed. A detailed example is provided for distributed detection of multitarget formations in a sensor network. The entire network is capable of reaching a common set of beliefs associated with correctness of different hypotheses. We demonstrate that our DHT algorithm successfully identifies a test formation in a network of sensors with selfconstructed statistical models. Key words: distributed hypothesis testing, multitarget tracking, Bayesian networks, average consensus, belief propagation, sensor networks, smallworld networks 1
A spacetime diffusion scheme for peertopeer leastsquares estimation
 Proc. of IPSN
, 2006
"... We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (i.e., asynchronously). We propose a spacetime diffusion s ..."
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Cited by 29 (0 self)
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We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (i.e., asynchronously). We propose a spacetime diffusion scheme, that relies only on peertopeer communication, and allows every node to asymptotically compute the global maximumlikelihood estimate of the unknown parameters. At each iteration, information is diffused across the network by a temporal update step and a spatial update step. Both steps update each node’s state by a weighted average of its current value and locally available data: new measurements for the time update, and neighbors ’ data for the spatial update. At any time, any node can compute a local weighted leastsquares estimate of the unknown parameters, which converges to the global maximumlikelihood solution. With an infinite number of measurements, these estimates converge to the true parameter values in the sense of meansquare convergence. We show that this scheme is robust to unreliable communication links, and works in a network with dynamically changing topology.
Decentralized maximum likelihood estimation for sensor networks composed of nonlinearly coupled dynamical systems
 IEEE Trans. Signal Process
, 2007
"... Abstract—In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximumlikelihood (ML) estimate through selfsynchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a firstorder dynamical system ini ..."
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Cited by 28 (2 self)
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Abstract—In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximumlikelihood (ML) estimate through selfsynchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a firstorder dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value, and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network, and we validate our theoretical findings with simulation results. Index Terms—Distributed consensus, distributed estimation, dynamical systems, sensor networks. I.
Robust decentralized source localization via averaging
 in Proc. IEEE ICASSP ’05
, 2005
"... We present a new approach to localizing an isotropic energy source using measurements from distributed sensors based on kernel averaging techniques. The location estimate is easily and efficiently calculated in a decentralized fashion. Statistical properties are derived for a very general measuremen ..."
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Cited by 27 (10 self)
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We present a new approach to localizing an isotropic energy source using measurements from distributed sensors based on kernel averaging techniques. The location estimate is easily and efficiently calculated in a decentralized fashion. Statistical properties are derived for a very general measurement model. Experiments suggest that the proposed estimator is much more robust and exhibits better performance characteristics than the popular least squares estimator under a variety of conditions. 1. LOCALIZATION VIA AVERAGING The problem of localizing and tracking an energyemitting source encompasses many of the challenging issues which commonly arise in wireless sensor network applications [1]. Consequently, this problem has recently received a great deal of attention. In [2], Chen et al. present an approach to source localization using
Robust distributed estimation using the embedded subgraphs algorithm
 IEEE Trans. Signal Process
, 2006
"... Abstract—We propose a new iterative, distributed approach for linear minimum meansquareerror (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a number of linked embedded subgraphs and applies the classical parallel b ..."
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Cited by 20 (0 self)
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Abstract—We propose a new iterative, distributed approach for linear minimum meansquareerror (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a number of linked embedded subgraphs and applies the classical parallel block Jacobi iteration comprising local LMMSE estimation in each subgraph (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and subgraphs. Our primary application is sensor networks, where the model encodes the correlation structure of the sensor measurements, which are assumed to be Gaussian. The resulting LMMSE estimation problem involves a large matrix inverse, which must be solved innetwork with distributed computation and minimal intersensor communication. By invoking the theory of asynchronous iterations, we prove that ESA is robust to temporary communication faults such as failing links and sleeping nodes, and enjoys guaranteed convergence under relatively mild conditions. Simulation studies demonstrate that ESA compares favorably with other recently proposed algorithms for distributed estimation. Simulations also indicate that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, sensor network energy conservation strategies such as lowpowered transmission and aggressive sleep schedules could actually prove counterproductive. Our results can be replicated using MATLAB code from www.dsp.rice.edu/software. Index Terms—Asynchronous iterations, distributed estimation, graphical models, matrix splitting, sensor networks, Wiener filter. I.
Random Distributed Multiresolution Representations with Significance Querying
 IN IPSN
, 2006
"... We propose random distributed multiresolution representations of sensor network data, so that the most significant encoding coefficients are easily accessible by querying a few sensors, anywhere in the network. Less significant encoding coefficients are available by querying a larger number of senso ..."
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Cited by 15 (1 self)
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We propose random distributed multiresolution representations of sensor network data, so that the most significant encoding coefficients are easily accessible by querying a few sensors, anywhere in the network. Less significant encoding coefficients are available by querying a larger number of sensors, local to the region of interest. Significance can be defined in a multiresolution way, without any prior knowledge of the source data, as global summaries versus local details. Alternatively, significance can be defined in a dataadaptive way, as large differences between neighboring data values. We propose a distributed encoding algorithm that is robust to arbitrary wireless communication connectivity graphs, where links can fail or change with time. This randomized algorithm allows distributed computation that does not require strict global coordination or awareness of network connectivity at individual sensors. Because computations involve sensors in local neighborhoods of the communication graph, they are communicationefficient. Our framework uses local interaction among sensors to enable flexible information retrieval at the global level.
Randomized Sequential Algorithms for Data Aggregation
 in Sensor Networks, CISS 2006
"... Abstract — We consider distributed algorithms for data aggregation in sensor networks. The algorithms are admitted via message passing hence through pairwise computations. Under these algorithms a transmitting node becomes inactive until it is reactivated, yielding to substantial energy gains. We s ..."
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Cited by 10 (6 self)
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Abstract — We consider distributed algorithms for data aggregation in sensor networks. The algorithms are admitted via message passing hence through pairwise computations. Under these algorithms a transmitting node becomes inactive until it is reactivated, yielding to substantial energy gains. We study the pernode message and time complexities of the algorithms in explicit graphs as a function of the network size. The obtained complexities are compared to those of socalled gossip algorithms. The results suggest tradeoffs between the number of messages transmitted and the computation times. I.