Performance analysis of a distributed Robbins-Monro algorithm for sensor networks

Pascal Bianchi; Gersende Fort; Walid Hachem; Jérémie Jakubowicz

Performance analysis of a distributed Robbins-Monro algorithm for sensor networks

Pascal Bianchi
, Gersende Fort
, Walid Hachem
, Jérémie Jakubowicz

CNRS

Résultats de recherche: Contribution à un journal › Article de conférence › Revue par des pairs

Résumé

This paper investigates the rate of convergence of a distributed Robbins-Monro algorithm for sensor networks. The algorithm under study consists of two steps: a local Robbins-Monro step at each sensor and a gossip step that drives the network to a consensus. Under verifiable sufficient conditions, we give an explicit rate of convergence for this algorithm and provide a conditional Central Limit Theorem. Our results are applied to distributed source localization.

langue originale	Anglais
Pages (de - à)	1030-1034
Nombre de pages	5
journal	European Signal Processing Conference
état	Publié - 1 déc. 2011
Evénement	19th European Signal Processing Conference, EUSIPCO 2011 - Barcelona, Espagne Durée: 29 août 2011 → 2 sept. 2011

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title = "Performance analysis of a distributed Robbins-Monro algorithm for sensor networks",

abstract = "This paper investigates the rate of convergence of a distributed Robbins-Monro algorithm for sensor networks. The algorithm under study consists of two steps: a local Robbins-Monro step at each sensor and a gossip step that drives the network to a consensus. Under verifiable sufficient conditions, we give an explicit rate of convergence for this algorithm and provide a conditional Central Limit Theorem. Our results are applied to distributed source localization.",

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AU - Bianchi, Pascal

AU - Fort, Gersende

AU - Hachem, Walid

AU - Jakubowicz, Jérémie

PY - 2011/12/1

Y1 - 2011/12/1

N2 - This paper investigates the rate of convergence of a distributed Robbins-Monro algorithm for sensor networks. The algorithm under study consists of two steps: a local Robbins-Monro step at each sensor and a gossip step that drives the network to a consensus. Under verifiable sufficient conditions, we give an explicit rate of convergence for this algorithm and provide a conditional Central Limit Theorem. Our results are applied to distributed source localization.

AB - This paper investigates the rate of convergence of a distributed Robbins-Monro algorithm for sensor networks. The algorithm under study consists of two steps: a local Robbins-Monro step at each sensor and a gossip step that drives the network to a consensus. Under verifiable sufficient conditions, we give an explicit rate of convergence for this algorithm and provide a conditional Central Limit Theorem. Our results are applied to distributed source localization.

UR - https://www.scopus.com/pages/publications/84863756321

M3 - Conference article

AN - SCOPUS:84863756321

SN - 2219-5491

SP - 1030

EP - 1034

JO - European Signal Processing Conference

JF - European Signal Processing Conference

T2 - 19th European Signal Processing Conference, EUSIPCO 2011

Y2 - 29 August 2011 through 2 September 2011

ER -

Performance analysis of a distributed Robbins-Monro algorithm for sensor networks

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