Publication detail

Evaluation of Natural Robustness of Best Constant Weights to Random Communication Breakdowns

KENYERES, M. KENYERES, J. BURGET, R.

Original Title

Evaluation of Natural Robustness of Best Constant Weights to Random Communication Breakdowns

English Title

Evaluation of Natural Robustness of Best Constant Weights to Random Communication Breakdowns

Type

journal article in Scopus

Language

en

Original Abstract

One of the most crucial aspects of an algorithm design for the wireless sensors networks is the failure tolerance. A high natural robustness and an effectively bounded execution time are factors that can significantly optimize the overall energy consumption and therefore, a great emphasis is laid on these aspects in many applications from the area of the wireless sensor networks. This paper addresses the robustness of the optimized Best Constant weights of Average Consensus with a stopping criterion (i.e. the algorithm is executed in a finite time) and their five variations with a lower mixing parameter (i.e. slower variants) to random communication breakdowns modeled as a stochastic event of a Bernoulli distribution. We choose three metrics, namely the deviation of the least precise final estimates from the average, the convergence rate expressed as the number of the iterations for the consensus, and the deceleration of each initial setup, in order to evaluate the robustness of various initial setups of Best Constant weights under a varying failure probability and over 30 random geometric graphs of either a strong or a weak connectivity. Our contribution is to find the most robust initial setup of Best Constant weights according to numerical experiments executed in Matlab. Finally, the experimentally obtained results are discussed, compared to the results from the error-free executions, and our conclusions are compared with the conclusions from related papers.

English abstract

One of the most crucial aspects of an algorithm design for the wireless sensors networks is the failure tolerance. A high natural robustness and an effectively bounded execution time are factors that can significantly optimize the overall energy consumption and therefore, a great emphasis is laid on these aspects in many applications from the area of the wireless sensor networks. This paper addresses the robustness of the optimized Best Constant weights of Average Consensus with a stopping criterion (i.e. the algorithm is executed in a finite time) and their five variations with a lower mixing parameter (i.e. slower variants) to random communication breakdowns modeled as a stochastic event of a Bernoulli distribution. We choose three metrics, namely the deviation of the least precise final estimates from the average, the convergence rate expressed as the number of the iterations for the consensus, and the deceleration of each initial setup, in order to evaluate the robustness of various initial setups of Best Constant weights under a varying failure probability and over 30 random geometric graphs of either a strong or a weak connectivity. Our contribution is to find the most robust initial setup of Best Constant weights according to numerical experiments executed in Matlab. Finally, the experimentally obtained results are discussed, compared to the results from the error-free executions, and our conclusions are compared with the conclusions from related papers.

Keywords

Distributed computing, Average Consensus algorithm, Best Constant weights, communication breakdowns, failure analysis

Released

01.09.2018

ISBN

1845-6421

Periodical

Journal of Communications Software and Systems

Year of study

14

Number

3

State

HR

Pages from

201

Pages to

210

Pages count

10

Documents

BibTex


@article{BUT149500,
  author="Martin {Kenyeres} and Jozef {Kenyeres} and Radim {Burget}",
  title="Evaluation of Natural Robustness of Best Constant Weights to Random Communication Breakdowns",
  annote="One of the most crucial aspects of an algorithm design for the wireless sensors networks is the failure tolerance.
A high natural robustness and an effectively bounded execution time are factors that can significantly optimize the overall energy consumption and therefore, a great emphasis is laid on these aspects in many applications from the area of the wireless sensor networks. This paper addresses the robustness of the optimized
Best Constant weights of Average Consensus with a stopping criterion (i.e. the algorithm is executed in a finite time) and their five variations with a lower mixing parameter (i.e. slower variants) to random communication breakdowns modeled as a stochastic event of a Bernoulli distribution. We choose three metrics, namely the deviation of the least precise final estimates from the average, the convergence rate expressed as the number
of the iterations for the consensus, and the deceleration of each initial setup, in order to evaluate the robustness of various initial setups of Best Constant weights under a varying failure probability and over 30 random geometric graphs of either a strong or a weak connectivity. Our contribution is to find the most robust initial setup of Best Constant weights according to numerical experiments executed in Matlab. Finally, the
experimentally obtained results are discussed, compared to the results from the error-free executions, and our conclusions are compared with the conclusions from related papers.",
  chapter="149500",
  doi="10.24138/jcomss.v14i3.487",
  number="3",
  volume="14",
  year="2018",
  month="september",
  pages="201--210",
  type="journal article in Scopus"
}