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170
Distributed LMS for ConsensusBased InNetwork Adaptive Processing
"... Abstract—Adaptive algorithms based on innetwork processing of distributed observations are wellmotivated for online parameter estimation and tracking of (non)stationary signals using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least meansquare (DLMS) algorithm is dev ..."
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Cited by 44 (4 self)
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Abstract—Adaptive algorithms based on innetwork processing of distributed observations are wellmotivated for online parameter estimation and tracking of (non)stationary signals using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least meansquare (DLMS) algorithm is developed in this paper, offering simplicity and flexibility while solely requiring singlehop communications among sensors. The resultant estimator minimizes a pertinent squarederror cost by resorting to i) the alternatingdirection method of multipliers so as to gain the desired degree of parallelization and ii) a stochastic approximation iteration to cope with the timevarying statistics of the process under consideration. Information is efficiently percolated across the WSN using a subset of “bridge ” sensors, which further tradeoff communication cost for robustness to sensor failures. For a linear data model and under mild assumptions aligned with those considered in the centralized LMS, stability of the novel DLMS algorithm is established to guarantee that local sensor estimation error norms remain bounded most of the time. Interestingly, this weak stochastic stability result extends to the pragmatic setup where intersensor communications are corrupted by additive noise. In the absence of observation and communication noise, consensus is achieved almost surely as local estimates are shown exponentially convergent to the parameter of interest with probability one. Meansquare error performance of DLMS is also assessed. Numerical simulations: i) illustrate that DLMS outperforms existing alternatives that rely either on information diffusion among neighboring sensors, or, local sensor filtering; ii) highlight its tracking capabilities; and iii) corroborate the stability and performance analysis results. Index Terms—Distributed estimation, LMS algorithm, wireless sensor networks (WSNs).
Diffusion strategies for distributed Kalman filtering: formulation and performance analysis
 in Proceedings of the IAPR Workshop on Cognitive Information Processing
, 2008
"... We consider the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their individual measurements. Our focus is on diffusion strategies, where nodes communicate with their direct neighbors only, and the informa ..."
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Cited by 34 (5 self)
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We consider the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their individual measurements. Our focus is on diffusion strategies, where nodes communicate with their direct neighbors only, and the information is diffused across the network. We derive and analyze the mean and meansquare performance of the proposed algorithms and show by simulation that they outperform previous solutions. 1.
Asynchronous distributed averaging on communication networks
 IEEE/ACM Transactions on Networking
, 2007
"... Abstract—Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packetswitched communication networks such as the Internet. In this paper, we ..."
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Cited by 33 (0 self)
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Abstract—Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packetswitched communication networks such as the Internet. In this paper, we present several implementable algorithms that are robust to asynchronism and dynamic topology changes. The algorithms are completely distributed and do not require any global coordination. In addition, they can be proven to converge under very general asynchronous timing assumptions. Our results are verified by both simulation and experiments on Planetlab, a realworld TCP/IP network. We also present some extensions that are likely to be useful in applications. Index Terms—Asynchronous computation, distributed averaging. I.
Gossip along the way: Orderoptimal consensus through randomized path averaging
 IN PROC. 45TH ANNU. ALLERTON CONF. COMMUNICATION, CONTROL, COMPUTING
, 2007
"... Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information processing over networks. However for many topologies that are realistic for wireless adhoc and sensor networks (like grids and random geometric ..."
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Cited by 31 (2 self)
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Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information processing over networks. However for many topologies that are realistic for wireless adhoc and sensor networks (like grids and random geometric graphs), the standard nearestneighbor gossip converges very slowly. A recently proposed algorithm called geographic gossip improves gossip efficiency by a p n / log n factor for random geometric graphs, by exploiting geographic information of node locations. In this paper we prove that a variation of geographic gossip that averages along routed paths, improves efficiency by an additional p n / log n factor and is order optimal for grids and random geometric graphs. Our analysis provides some general techniques and can be used to provide bounds on the performance of randomized message passing algorithms operating over various graph topologies.
An overview of recent progress in the study of distributed multiagent coordination
, 2012
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Tracking and Activity Recognition Through Consensus in Distributed Camera Networks
, 2010
"... Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be pr ..."
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Cited by 25 (8 self)
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Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based on its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the KalmanConsensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.
A Distributed Minimum Variance Estimator for Sensor Networks
"... Abstract—A distributed estimation algorithm for sensor networks is proposed. A noisy timevarying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors’ measurements and estimates. The weights are adaptively up ..."
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Cited by 24 (4 self)
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Abstract—A distributed estimation algorithm for sensor networks is proposed. A noisy timevarying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors’ measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This bound decreases with the number of neighboring nodes. The estimation properties of the algorithm are illustrated via computer simulations, which are intended to compare our estimator performance with distributed schemes that were proposed previously in the literature. The results of the paper allow to tradingoff communication constraints, computing efforts and estimation quality for a class of distributed filtering problems.
Diffusion adaptation over networks
 in Academic Press Library in Signal Processing
, 2014
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Distributed Kalman filtering using consensus strategies
"... Abstract — 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 bein ..."
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Cited by 20 (0 self)
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Abstract — 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 message exchange per sampling time is small. Moreover, we prove that under certain conditions the optimal consensus matrix should be doubly stochastic. We also provide some numerical examples to clarify some of the analytical results. I.