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Network cardinality estimation using max consensus: the case of Bernoulli trials
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Université Catholique de Louvain.
Université Catholique de Louvain.
2015 (English)In: IEEE 54th Annual Conference on Decision and Control (CDC): Osaka, Japan, 15-18 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, 895-901 p., 7402342Conference paper (Refereed)
Abstract [en]

Interested in scalable topology reconstruction strategies with fast convergence times, we consider network cardinality estimation schemes that use, as their fundamental aggregation mechanism, the computation of bit-wise maxima over strings. We thus discuss how to choose optimally the parameters of the information generation process under frequentist assumptions on the estimand, derive the resulting Maximum Likelihood (ML) estimator, and characterize its statistical performance as a function of the communications and memory requirements. We then numerically compare the bitwise-max based estimator against lexicographic-max based estimators, and derive insights on their relative performances in function of the true cardinality.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015. 895-901 p., 7402342
Series
, I E E E Conference on Decision and Control. Proceedings, ISSN 0743-1546
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-34143DOI: 10.1109/CDC.2015.7402342Local ID: 842e74f7-bfaf-4be6-9fb4-bfba308dc423ISBN: 978-1-4799-7884-7OAI: oai:DiVA.org:ltu-34143DiVA: diva2:1007393
Conference
IEEE Conference of Decision and Control : 16/12/2015 - 18/12/2015
Note
Godkänd; 2015; 20150725 (ricluc)Available from: 2016-09-30 Created: 2016-09-30Bibliographically approved

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Lucchese, RiccardoVaragnolo, Damiano
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