Gossip consensus algorithms via quantized communication

Carli, Ruggero; Fagnani, Fabio; Frasca, Paolo; Zampieri, Sandro

doi:10.1016/j.automatica.2009.10.032

Cited by 225 publications

(174 citation statements)

References 19 publications

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“…their states) that differ from the actual values, and thus they have a knowledge about the neighbors' states that is not precise. This behavior can be assimilated to a quantization scheme and Carli et al (2010a) have investigated the adaptation of the gossip algorithm to a network subject to quantized communication. In this scenario, our aim is to present and discuss what kind of quantization is convenient to adopt.…”

Section: Quantized Consensusmentioning

confidence: 99%

Channel Model Identification in Wireless Sensor Networks Using a Fully Distributed Quantized Consensus Algorithm

Cenedese

Zanella

2014

IFAC Proceedings Volumes

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In this paper, we consider the problem of designing a distributed strategy to estimate the channel parameters for a generic Wireless Sensor-Actor Network (WSAN). To this aim, we present a distributed least-square algorithm that complies with the constraint of transmitting only integer data through the wireless communication, which often characterizes WSAN embedded architectures. In this respect, we propose a quantized consensus strategy that mitigates the effects of the rounding operations applied to the wireless exchanged floating data. Moreover, the approach is based on a symmetric random gossip strategy, making it suitable for the actual deployment in multiagent networks. Finally, the effectiveness of the proposed algorithm and of its implementation as an open-source application is assessed and the employment of the procedure is illustrated through the application to radio-frequency localization experiments in a real world testbed.

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Section: Quantized Consensusmentioning

confidence: 99%

Channel Model Identification in Wireless Sensor Networks Using a Fully Distributed Quantized Consensus Algorithm

Cenedese

Zanella

2014

IFAC Proceedings Volumes

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“…Related are also gossip and broadcast-gossip schemes giving rise to a random consensus over a given connected bi-directional communication network [37,38,39,40]. On a broader basis, this work is related to the literature on the consensus over networks with noisy links [41,42,43,44] and the deterministic consensus in decentralized systems models [1,45,2,46,47,21,48,24,49], including the effects of quantization and delay [50,51,52,53,54,40].…”

Section: Past Workmentioning

confidence: 99%

Product of Random Stochastic Matrices and Distributed Averaging

Touri

2012

Springer Theses

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This thesis is mainly concerned with the study of product of random stochastic matrices and random weighted averaging dynamics. It will be shown that a generalization of a fundamental result in the theory of ergodic Markov chains not only holds for inhomogeneous chains of stochastic matrices, but also remains true for random stochastic matrices. To do this, the concept of infinite flow property will be introduced for a deterministic chain of stochastic matrices and it will be proven that it is necessary for ergodicity of any stochastic chain. This result will further be extended to ergodic classes, through the development of the concept of the infinite flow graph and ℓ 1 -approximation technique.For the converse implications, the product of stochastic matrices will be studied in the more general setting of random adapted stochastic chains. Using a result of A. Kolmogorov, it will be shown that any averaging dynamics admits infinitely many comparison functions including a quadratic one. By identifying the decrease of the quadratic comparison function along the trajectories of the dynamics, it will be proven that under general assumptions on a random chain, the chain is infinite flow stable, i.e. the product of random stochastic matrices is convergent almost surely and, also, the limiting matrices admit certain structures that can be deduced from the infinite flow graph of the chain. It will be shown that a general class of stochastic chains, the balanced chains with feedback property, satisfy the conditions of this result.Some implications of the developed results for products of independent random stochastic matrices will be provided. Furthermore, it will be proven that under general conditions, an independent random chain and its expected chain exhibit the same ergodic behavior. It will be proven that an extension of a well-known result in the theory of homogeneous Markov chains holds for a sequence of inhomogeneous stochastic matrices. Then, link-failure models for averaging dynamics will be introduced and it will be shown that under general conditions, link failure does not affect the limiting behavior of averaging dynamics. Then, the application of the developed methods to the study of Hegselmann-Krause model will be considered. Using the developed results, an upper bound O(m 4 ) will be established for the Hegselmann-Krause dynamics, which is an improvement to the previously known bound ii O(m 5 ). As a final application for the developed tools, an alternative proof for the second Borel-Cantelli lemma will be provided. Motivated by the infinite flow property, a stronger one, the absolute infinite flow property will be introduced. It will be shown that this stronger property is in fact necessary for ergodicity of any stochastic chain. Moreover, the equivalency of the absolute infinite flow property with ergodicity of doubly stochastic chains will be proven. These results will be driven by introduction and study of the rotational transformation of a stochastic chain.Finally, motivated by the study of ...

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“…• if the elements of F are equally spaced then the previous operations f i (τ +1) = 1 2 f i (τ )+f j (τ ) in F can be refined so that every f i (τ )'s converges in finite time to f = f + e = f , with e an error smaller than the spacing of the elements in F Carli et al (2010).…”

Section: Implementation Under Finite-precision Arithmeticsmentioning

confidence: 99%

A variation of the Newton–Pepys problem and its connections to size-estimation problems

Varagnolo

Schenato

Pillonetto

2013

Statistics & Probability Letters

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This paper considers a variation of the 17 th century problem commonly known as the Newton-Pepys problem, or the John Smith's problem. We provide its solution and interpret the result in terms of maximum likelihood estimation. In addition, we illustrate the practical relevance of these findings for solving size-estimation problems, and in particular for determining the number of agents in a wireless sensor network.

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Gossip consensus algorithms via quantized communication

Cited by 225 publications

References 19 publications

Channel Model Identification in Wireless Sensor Networks Using a Fully Distributed Quantized Consensus Algorithm

Channel Model Identification in Wireless Sensor Networks Using a Fully Distributed Quantized Consensus Algorithm

Product of Random Stochastic Matrices and Distributed Averaging

A variation of the Newton–Pepys problem and its connections to size-estimation problems

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