9,665 research outputs found
Greedy Gossip with Eavesdropping
This paper presents greedy gossip with eavesdropping (GGE), a novel
randomized gossip algorithm for distributed computation of the average
consensus problem. In gossip algorithms, nodes in the network randomly
communicate with their neighbors and exchange information iteratively. The
algorithms are simple and decentralized, making them attractive for wireless
network applications. In general, gossip algorithms are robust to unreliable
wireless conditions and time varying network topologies. In this paper we
introduce GGE and demonstrate that greedy updates lead to rapid convergence. We
do not require nodes to have any location information. Instead, greedy updates
are made possible by exploiting the broadcast nature of wireless
communications. During the operation of GGE, when a node decides to gossip,
instead of choosing one of its neighbors at random, it makes a greedy
selection, choosing the node which has the value most different from its own.
In order to make this selection, nodes need to know their neighbors' values.
Therefore, we assume that all transmissions are wireless broadcasts and nodes
keep track of their neighbors' values by eavesdropping on their communications.
We show that the convergence of GGE is guaranteed for connected network
topologies. We also study the rates of convergence and illustrate, through
theoretical bounds and numerical simulations, that GGE consistently outperforms
randomized gossip and performs comparably to geographic gossip on
moderate-sized random geometric graph topologies.Comment: 25 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
Gossip Algorithms in Quantum Networks
Gossip algorithms is a common term to describe protocols for unreliable
information dissemination in natural networks, which are not optimally designed
for efficient communication between network entities. We consider application
of gossip algorithms to quantum networks and show that any quantum network can
be updated to optimal configuration with local operations and classical
communication. This allows to seed-up -- in the best case exponentially -- the
quantum information dissemination. Irrespective of the initial configuration of
the quantum network, the update requiters at most polynomial number of local
operations and classical communication.Comment: 5 pages, 1 figure, 15 reference
Geographic Gossip: Efficient Averaging for Sensor Networks
Gossip algorithms for distributed computation are attractive due to their
simplicity, distributed nature, and robustness in noisy and uncertain
environments. However, using standard gossip algorithms can lead to a
significant waste in energy by repeatedly recirculating redundant information.
For realistic sensor network model topologies like grids and random geometric
graphs, the inefficiency of gossip schemes is related to the slow mixing times
of random walks on the communication graph. We propose and analyze an
alternative gossiping scheme that exploits geographic information. By utilizing
geographic routing combined with a simple resampling method, we demonstrate
substantial gains over previously proposed gossip protocols. For regular graphs
such as the ring or grid, our algorithm improves standard gossip by factors of
and respectively. For the more challenging case of random
geometric graphs, our algorithm computes the true average to accuracy
using radio
transmissions, which yields a factor improvement over
standard gossip algorithms. We illustrate these theoretical results with
experimental comparisons between our algorithm and standard methods as applied
to various classes of random fields.Comment: To appear, IEEE Transactions on Signal Processin
Exploiting the Synergy Between Gossiping and Structured Overlays
In this position paper we argue for exploiting the synergy between gossip-based algorithms and structured overlay networks (SON). These two strands of research have both aimed at building fault-tolerant, dynamic, self-managing, and large-scale distributed systems. Despite the common goals, the two areas have, however, been relatively isolated. We focus on three problem domains where there is an untapped potential of using gossiping combined with SONs. We argue for applying gossip-based membership for ring-based SONs---such as Chord and Bamboo---to make them handle partition mergers and loopy networks. We argue that small world SONs---such as Accordion and Mercury---are specifically well-suited for gossip-based membership management. The benefits would be better graph-theoretic properties. Finally, we argue that gossip-based algorithms could use the overlay constructed by SONs. For example, many unreliable broadcast algorithms for SONs could be augmented with anti-entropy protocols. Similarly, gossip-based aggregation could be used in SONs for network size estimation and load-balancing purposes
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations
Consider a network of agents connected by communication links, where each
agent holds a real value. The gossip problem consists in estimating the average
of the values diffused in the network in a distributed manner. We develop a
method solving the gossip problem that depends only on the spectral dimension
of the network, that is, in the communication network set-up, the dimension of
the space in which the agents live. This contrasts with previous work that
required the spectral gap of the network as a parameter, or suffered from slow
mixing. Our method shows an important improvement over existing algorithms in
the non-asymptotic regime, i.e., when the values are far from being fully mixed
in the network. Our approach stems from a polynomial-based point of view on
gossip algorithms, as well as an approximation of the spectral measure of the
graphs with a Jacobi measure. We show the power of the approach with
simulations on various graphs, and with performance guarantees on graphs of
known spectral dimension, such as grids and random percolation bonds. An
extension of this work to distributed Laplacian solvers is discussed. As a side
result, we also use the polynomial-based point of view to show the convergence
of the message passing algorithm for gossip of Moallemi \& Van Roy on regular
graphs. The explicit computation of the rate of the convergence shows that
message passing has a slow rate of convergence on graphs with small spectral
gap
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