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Average consensus via max consensus
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0002-7443-8174
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0002-4310-7938
Rekke forfattare: 22015 (engelsk)Inngår i: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 48, nr 22, s. 58-63Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Since intuition states that it is simple and fast to compute maxima over networks, we aim at understanding the limits of computing averages over networks through computing maxima. We thus build on top of max-consensus based networks’ cardinality estimation protocols a novel estimation strategy that infers averages through computing maxima of opportunely and locally generated random initial conditions. We motivate the max-consensus strategy explaining why it satisfies practical requirements, we characterize completely its statistical properties, and we analyze when and under which conditions it performs favorably against classical linear consensus strategies in static Cayley graphs

sted, utgiver, år, opplag, sider
Elsevier, 2015. Vol. 48, nr 22, s. 58-63
Emneord [en]
Information technology - Automatic control
Emneord [sv]
Informationsteknik - Reglerteknik
HSV kategori
Forskningsprogram
Reglerteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-30245DOI: 10.1016/j.ifacol.2015.10.307Scopus ID: 2-s2.0-84992521919Lokal ID: 402a528a-8fc5-488f-8f91-576376428abdOAI: oai:DiVA.org:ltu-30245DiVA, id: diva2:1003472
Konferanse
IFAC Workshop on Distributed Estimation and Control in Networked Systems : 10/09/2015 - 11/09/2015
Merknad

Konferensartikel i tidskrift

Tilgjengelig fra: 2016-09-30 Laget: 2016-09-30 Sist oppdatert: 2020-01-27bibliografisk kontrollert
Inngår i avhandling
1. Distributed Estimation of Network Cardinalities
Åpne denne publikasjonen i ny fane eller vindu >>Distributed Estimation of Network Cardinalities
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[sv]
Distribuerad skattning av nätverkskardinalitet
Abstract [en]

In distributed applications knowing the topological properties of the underlying communication network may lead to better performing algorithms. For instance, in distributed regression frameworks, knowing the number of active sensors allows to correctly weight prior information against evidence in the data. Moreover, continuously estimating the number of active nodes or communication links corresponds to monitoring the network connectivity and thus to being able to trigger network reconfiguration strategies. It is then meaningful to seek for estimators of the properties of the communication graphs that sense these properties with the smallest possible computational/communications overheads.

Here we consider the problem of distributedly counting the number of agents in a network. This is at the same time a prototypical summation problem and an essential task instrumental to evaluating more complex algebraic expressions such as products and averages which are in turn useful in many distributed control, optimization and estimation problems such as least squares, sensor calibration, vehicle coordination and Kalman filtering.

Being interested in generality, we consider computations in anonymous networks, i.e., in frameworks where agents are not ensured to have unique IDs and the network lacks a centralized authority. This setting implies that the set of distributedly computable functions is limited, that there is no size estimation algorithm with uniformly bounded computational complexity that can provide correct estimates with probability one, and thus that scalable size estimators are non-deterministic functions of the true network size. Natural questions are then: which one is the scheme that leads to topology estimators that are optimal in Mean Squared Error (MSE) terms? And what are the fundamental limitations of information aggregation for topology estimation purposes, i.e., what can be estimated and what not?

Our focus is then to understand how to distributedly estimate cardinalities given devices with bounded resources (e.g., battery/energy constraints, communication bandwidth, etc.) and how considering different assumptions and trade-offs leads to different optimal strategies. We specifically consider the case of peer-to-peer networks where all the participants are required to: i) share the same final result (and thus the same view of the network) and ii) keep the communication and computational complexity at each node uniformly bounded in time.

To this aim, we study four different estimation strategies that consider different tradeoffs between accuracy and convergence speed and characterize their statistical performance in terms of bias and MSE.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2017
Serie
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
HSV kategori
Forskningsprogram
Reglerteknik
Identifikatorer
urn:nbn:se:ltu:diva-62460 (URN)978-91-7583-843-4 (ISBN)978-91-7583-844-1 (ISBN)
Presentation
2017-05-17, A1545, Luleå, 13:00
Tilgjengelig fra: 2017-03-15 Laget: 2017-03-13 Sist oppdatert: 2017-12-18bibliografisk kontrollert

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