Publication detail

The Distributed Convergence Classifier Using the Finite Difference

KENYERES, M. KENYERES, J. ŠKORPIL, V.

Original Title

The Distributed Convergence Classifier Using the Finite Difference

Czech Title

Distribuovaný klasifikátor konvergencie využívajúci konečnú diferenciu

English Title

The Distributed Convergence Classifier Using the Finite Difference

Type

journal article

Language

en

Original Abstract

The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality.

Czech abstract

V článku je prezentovaný nový spôsob klasifikácie konvergencie distribuovaným spôsobom, navrhnutý za účelom distribuovane detekovať konvergenciu/divergenciu konvergujúcich distribuovaných algoritmov. Klasifikátor je založený na porovnávaní doprednej diferencie z dvoch po sebe idúcich iterácií. Navrhnutý mechanismus pre správnu funcionalitu nepotrebuje redundanciu správ a využíva len informácie o zmene vnutorného stavu.

English abstract

The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality.

Keywords

Distributed computing, wireless sensor networks, average consensus, distributed classifier

Released

01.04.2016

Pages from

148

Pages to

155

Pages count

9

BibTex


@article{BUT122510,
  author="Martin {Kenyeres} and Jozef {Kenyeres} and Vladislav {Škorpil}",
  title="The Distributed Convergence Classifier Using the Finite Difference",
  annote="The paper presents a novel distributed classifier
of the convergence, which allows to detect the
convergence/the divergence of a distributed converging
algorithm. Since this classifier is supposed to be primarily
applied in wireless sensor networks, its proposal makes
provision for the character of these networks. The classifier
is based on the mechanism of comparison of the forward
finite differences from two consequent iterations. The
convergence/the divergence is classifiable only in terms of
the changes of the inner states of a particular node and
therefore, no message redundancy is required for its proper
functionality.",
  chapter="122510",
  number="1",
  volume="25",
  year="2016",
  month="april",
  pages="148--155",
  type="journal article"
}