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Peer-to-peer Estimation over Wireless Sensor Networks via Lipschitz Optimization
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9810-3478
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2009 (English)In: 2009 INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN 2009), NEW YORK: IEEE , 2009, p. 241-252Conference paper, Published paper (Refereed)
Abstract [en]

Motivated by a peer-to-peer estimation algorithm in which adaptive weights are optimized to minimize the estimation error variance, we formulate and solve a novel non-convex Lipschitz optimization problem that guarantees global stability of a large class of peer-to-peer consensus-based algorithms for wireless sensor network. Because of packet. losses, the solution of this optimization problem cannot be achieved efficiently with either traditional centralized methods or distributed Lagrangian message passing. The prove that the optimal solution can be obtained by solving a set of nonlinear equations. A fast distributed algorithm, which requires only local computations, is presented for solving these equations. Analysis and computer simulations illustrate the algorithm and its application to various network topologies.

Place, publisher, year, edition, pages
NEW YORK: IEEE , 2009. p. 241-252
Keywords [en]
Lipschitz Optimization, Parallel and Distributed Computation, Wireless Sensor Networks, Distributed Estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-26514ISI: 000275711800021Scopus ID: 2-s2.0-71049143081ISBN: 978-142445108-1 (print)OAI: oai:DiVA.org:kth-26514DiVA, id: diva2:385811
Conference
8th International Symposium on Information Processing Sensor Networks San Francisco, CA, APR 13-16, 2009
Note
QC 20110112Available from: 2011-01-12 Created: 2010-11-25 Last updated: 2022-06-25Bibliographically approved

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Fischione, CarloJohansson, Karl Henrik

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CiteExportLink to record
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Citation style
  • apa
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