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81
Gossip algorithms for distributed signal processing
 PROCEEDINGS OF THE IEEE
, 2010
"... Gossip algorithms are attractive for innetwork processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the co ..."
Abstract

Cited by 115 (29 self)
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Gossip algorithms are attractive for innetwork processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmittedmessages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
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.
AVERAGE CONSENSUS WITH PACKET DROP COMMUNICATION
, 2009
"... Average consensus consists in the problem of determining the average of some quantities by means of a distributed algorithm. It is a simple instance of problems arising when designing estimation algorithms operating on data produced by sensor networks. Simple solutions based on linear estimation a ..."
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Cited by 55 (8 self)
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Average consensus consists in the problem of determining the average of some quantities by means of a distributed algorithm. It is a simple instance of problems arising when designing estimation algorithms operating on data produced by sensor networks. Simple solutions based on linear estimation algorithms have already been proposed in the literature and their performance has been analyzed in detail. If the communication links which allow the data exchange between the sensors have some loss, then the estimation performance will degrade. In this contribution the performance degradation due to this data loss is evaluated.
Average consensus on networks with quantized communication
 Intern. Journ. on Nonlinear and Robust Control
, 2008
"... communication ..."
On synchronous robotic networks Part I: models, tasks, and complexity notions
 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference (CDCECC ’05
, 2005
"... This paper proposes a formal model for a network of robotic agents that move and communicate. Building on concepts from distributed computation, robotics and control theory, we define notions of robotic network, control and communication law, coordination task, and time and communication complexity. ..."
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Cited by 40 (18 self)
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This paper proposes a formal model for a network of robotic agents that move and communicate. Building on concepts from distributed computation, robotics and control theory, we define notions of robotic network, control and communication law, coordination task, and time and communication complexity. We illustrate our model and compute the proposed complexity measures in the example of a network of locally connected agents on a circle that agree upon a direction of motion and pursue their immediate neighbors. I.
Distributed control of robotic networks: a mathematical approach to motion coordination algorithms
, 2009
"... (i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 ..."
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Cited by 38 (1 self)
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(i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 “Distributed Control of Robotic Networks ” by F. Bullo, J. Cortés and S. Martínez
Gossip consensus algorithms via quantized communication
, 2009
"... This paper considers the average consensus problem on a network of digital links, and proposes a set of algorithms based on pairwise “gossip” communications and updates. We study the convergence properties of such algorithms with the goal of answering two design questions, arising from the literatur ..."
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Cited by 34 (5 self)
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This paper considers the average consensus problem on a network of digital links, and proposes a set of algorithms based on pairwise “gossip” communications and updates. We study the convergence properties of such algorithms with the goal of answering two design questions, arising from the literature: whether the agents should encode their communication by a deterministic or a randomized quantizer, and whether they should use, and how, exact information regarding their own states in the update.
Quantized average consensus via dynamic coding/decoding schemes
 International Journal of Robust and Nonlinear Control
"... Abstract — In the average consensus a set of linear systems has to be driven to the same final state which corresponds to the average of their initial states. This contribution presents a consensus strategy in which the systems can exchange information among themselves according to a fixed connected ..."
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Cited by 33 (3 self)
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Abstract — In the average consensus a set of linear systems has to be driven to the same final state which corresponds to the average of their initial states. This contribution presents a consensus strategy in which the systems can exchange information among themselves according to a fixed connected digital communication network. Beside the decentralized computational aspects induced by the choice of the communication network, we here have also to face the quantization effects due to the digital links. We here present and discuss two different encoding/decoding strategies with theoretical and simulation results on their performance. I.
On ergodicity, infinite flow and consensus in random models, available at: http://arxiv.org/abs/1001.1890
"... Abstract—We consider the ergodicity and consensus problem for a discretetime linear dynamic model driven by random stochastic matrices, which is equivalent to studying these concepts for the product of such matrices. Our focus is on the model where the random matrices have independent but timevari ..."
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Cited by 30 (15 self)
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Abstract—We consider the ergodicity and consensus problem for a discretetime linear dynamic model driven by random stochastic matrices, which is equivalent to studying these concepts for the product of such matrices. Our focus is on the model where the random matrices have independent but timevariant distribution. We introduce a new phenomenon, the infinite flow, and we study its fundamental properties and relations with the ergodicity and consensus. The central result is the infinite flow theorem establishing the equivalence between the infinite flow and the ergodicity for a class of independent random models, where the matrices in the model have a common steady state in expectation and a feedback property. For such models, this result demonstrates that the expected infinite flow is both necessary and sufficient for the ergodicity. The result is providing a deterministic characterization of the ergodicity, which can be used for studying the consensus and average consensus over random graphs. Index Terms—Ergodicity, random consensus, linear random model, product of random matrices, infinite flow. I.