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
English Title
Comparative Study of Distributed Estimation Precision by Average Consensus Weight Models
Type
journal article in Scopus
Language
en
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.
English 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
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
HR
Pages from
165
Pages to
177
Pages count
13
URL
Full text in the Digital Library
Documents
BibTex
@article{BUT142576,
author="Martin {Kenyeres} and Jozef {Kenyeres}",
title="Comparative Study of Distributed Estimation Precision by Average Consensus Weight Models",
annote="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.",
address="Croatian Communications and Information Society",
chapter="142576",
doi="10.24138/jcomss.v13i4.405",
institution="Croatian Communications and Information Society",
number="4",
volume="13",
year="2017",
month="december",
pages="165--177",
publisher="Croatian Communications and Information Society",
type="journal article in Scopus"
}