76,376 research outputs found
Fast convergence for consensus in dynamic networks
LNCS v. 3756 entitled: Automata, languages and programming : 38th international colloquium, ICALP 2011 ... proceedingsWe study the convergence time required to achieve consensus in dynamic networks. In each time step, a node's value is updated to some weighted average of its neighbors' and its old values. We study the case when the underlying network is dynamic, and investigate different averaging models. Both our analysis and experiments show that dynamic networks exhibit fast convergence behavior, even under very mild connectivity assumptions. © 2011 Springer-Verlag.postprin
Gaussian Belief with dynamic data and in dynamic network
In this paper we analyse Belief Propagation over a Gaussian model in a
dynamic environment. Recently, this has been proposed as a method to average
local measurement values by a distributed protocol ("Consensus Propagation",
Moallemi & Van Roy, 2006), where the average is available for read-out at every
single node. In the case that the underlying network is constant but the values
to be averaged fluctuate ("dynamic data"), convergence and accuracy are
determined by the spectral properties of an associated Ruelle-Perron-Frobenius
operator. For Gaussian models on Erdos-Renyi graphs, numerical computation
points to a spectral gap remaining in the large-size limit, implying
exceptionally good scalability. In a model where the underlying network also
fluctuates ("dynamic network"), averaging is more effective than in the dynamic
data case. Altogether, this implies very good performance of these methods in
very large systems, and opens a new field of statistical physics of large (and
dynamic) information systems.Comment: 5 pages, 7 figure
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network 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 article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages 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.Comment: Submitted to Proceedings of the IEEE, 29 page
Consensus problems in networks of agents with switching topology and time-delays
In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results
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