Publication detail

Comparative Study of Distributed Estimation Precision by Average Consensus Weight Models

KENYERES, M. KENYERES, J.

Original Title

Comparative Study of Distributed Estimation Precision by Average Consensus Weight Models

Type

journal article in Scopus

Language

English

Original Abstract

Distributed algorithms for an aggregate function estimation are an important complement of many real-life applications based on wireless sensor networks. Achieving a high precision of an estimation in a shorter time can optimize the overall energy consumption. Therefore, the choice of a proper distributed algorithm is an important part of an application design. In this study, we focus our attention on the average consensus algorithm and evaluate six weight models appropriate for the implementation into real-life applications. Our aim is to find the most suitable model in terms of the estimation precision in various phases of the algorithm. We examine the deviation of the least precise estimate over iterations for a Gaussian, a Uniform and a Bernoulli distribution of the initial states in strongly and weakly connected networks with a randomly generated topology. We examine which model is the most and the least precise in various phases. Based on these findings, we determine the most suitable model for real-life applications.

Keywords

Distributed computing, wireless sensor networks, average consensus algorithm, estimation precision

Authors

KENYERES, M.; KENYERES, J.

Released

21. 12. 2017

Publisher

Croatian Communications and Information Society

ISBN

1845-6421

Periodical

Journal of Communications Software and Systems

Year of study

13

Number

4

State

Republic of Croatia

Pages from

165

Pages to

177

Pages count

13

URL

https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/405

Full text in the Digital Library

http://hdl.handle.net/11012/84113

BibTex

@article{BUT142576,
  author="Martin {Kenyeres} and Jozef {Kenyeres}",
  title="Comparative Study of Distributed Estimation Precision by Average Consensus Weight Models",
  journal="Journal of Communications Software and Systems",
  year="2017",
  volume="13",
  number="4",
  pages="165--177",
  doi="10.24138/jcomss.v13i4.405",
  issn="1845-6421",
  url="https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/405"
}