Detail publikace

Influence of random leader appointment on convergence rate of network size estimation

Originální název

Influence of random leader appointment on convergence rate of network size estimation

Anglický název

Influence of random leader appointment on convergence rate of network size estimation

Jazyk

en

Originální abstrakt

The information about the network size is crucial for many real life applications. It can be obtained with the distributed average consensus algorithm, whose implementation requires the proper leader appointment, which is an energy demanding process. The lack of the papers concerned with this aspect motivates us to verify the influence of a random leader appointment on the convergence rates of different weight models of average consensus. We examine the range of the achieved convergence rates in 30 randomly generated networks for different leaders and show the maximal possible deceleration of the algorithm due to an inappropriate leader appointment.

Anglický abstrakt

The information about the network size is crucial for many real life applications. It can be obtained with the distributed average consensus algorithm, whose implementation requires the proper leader appointment, which is an energy demanding process. The lack of the papers concerned with this aspect motivates us to verify the influence of a random leader appointment on the convergence rates of different weight models of average consensus. We examine the range of the achieved convergence rates in 30 randomly generated networks for different leaders and show the maximal possible deceleration of the algorithm due to an inappropriate leader appointment.

BibTex


@article{BUT141242,
  author="Martin {Kenyeres} and Jozef {Kenyeres}",
  title="Influence of random leader appointment on convergence rate of network size estimation",
  annote="The information about the network size is crucial for many real life applications. It can be obtained with the distributed average consensus algorithm, whose implementation requires the proper leader appointment, which is an energy demanding process. The lack of the papers concerned with this aspect motivates us to verify the influence of a random leader appointment on the convergence rates of different weight models of average consensus. We examine the range of the achieved convergence rates in 30 randomly generated networks for different leaders and show the maximal possible deceleration of the algorithm due to an inappropriate leader appointment.",
  chapter="141242",
  number="4",
  volume="79",
  year="2017",
  month="december",
  pages="57--68",
  type="journal article"
}