Publication detail

Distributed Aggregate Function Estimation by Biphasically Configured Metropolis-Hasting Weight Model

KENYERES, M. KENYERES, J. ŠKORPIL, V. BURGET, R.

Original Title

Distributed Aggregate Function Estimation by Biphasically Configured Metropolis-Hasting Weight Model

English Title

Distributed Aggregate Function Estimation by Biphasically Configured Metropolis-Hasting Weight Model

Type

journal article in Web of Science

Language

en

Original Abstract

An energy-efficient estimation of an aggregate function can significantly optimize a global event detection or monitoring in wireless sensor networks. This is probably the main reason why an optimization of the complementary consensus algorithms is one of the key challenges of the lifetime extension of the wireless sensor networks on which the attention of many scientists is paid. In this paper, we introduce an optimized weight model for the average consensus algorithm. It is called the Biphasically configured Metropolis-Hasting weight model and is based on a modification of the Metropolis-Hasting weight model by rephrasing the initial configuration into two parts. The first one is the default configuration of the Metropolis-Hasting weight model, while, the other one is based on a recalculation of the weights allocated to the adjacent nodes’ incoming values at the cost of decreasing the value of the weights of the inner states. The whole initial configuration is executed in a fully-distributed manner. In the experimental section, it is proven that our optimized weight model significantly optimizes the MetropolisHasting weight model in several aspects and achieves better results compared with other concurrent weight models.

English abstract

An energy-efficient estimation of an aggregate function can significantly optimize a global event detection or monitoring in wireless sensor networks. This is probably the main reason why an optimization of the complementary consensus algorithms is one of the key challenges of the lifetime extension of the wireless sensor networks on which the attention of many scientists is paid. In this paper, we introduce an optimized weight model for the average consensus algorithm. It is called the Biphasically configured Metropolis-Hasting weight model and is based on a modification of the Metropolis-Hasting weight model by rephrasing the initial configuration into two parts. The first one is the default configuration of the Metropolis-Hasting weight model, while, the other one is based on a recalculation of the weights allocated to the adjacent nodes’ incoming values at the cost of decreasing the value of the weights of the inner states. The whole initial configuration is executed in a fully-distributed manner. In the experimental section, it is proven that our optimized weight model significantly optimizes the MetropolisHasting weight model in several aspects and achieves better results compared with other concurrent weight models.

Keywords

Distributed computing, aggregate function, average consensus algorithm, metropolis-hasting weight model, wireless sensor networks

Released

30.06.2017

Pages from

479

Pages to

495

Pages count

17

BibTex


@article{BUT134675,
  author="Martin {Kenyeres} and Jozef {Kenyeres} and Vladislav {Škorpil} and Radim {Burget}",
  title="Distributed Aggregate Function Estimation by Biphasically Configured Metropolis-Hasting Weight Model",
  annote="An energy-efficient estimation of an aggregate function can significantly optimize a global event detection
or monitoring in wireless sensor networks. This is probably the main reason why an optimization of the complementary consensus algorithms is one of the key challenges of the lifetime extension of the wireless sensor networks on which  the attention of many scientists is paid. In this paper, we introduce an optimized weight model for the average consensus algorithm. It is called the Biphasically configured Metropolis-Hasting weight model and is based on a modification of the Metropolis-Hasting weight model by rephrasing the initial configuration into two parts. The first one is the default configuration of the Metropolis-Hasting weight model, while, the other one is based on a recalculation of the weights allocated to the adjacent nodes’ incoming values at the cost of decreasing the value of the weights of the inner states. The whole initial configuration is executed in a fully-distributed manner. In the experimental section, it is proven that our optimized weight model significantly optimizes the MetropolisHasting
weight model in several aspects and achieves better results compared with other concurrent weight models.",
  chapter="134675",
  doi="10.13164/re.2017.0479",
  number="2",
  volume="26",
  year="2017",
  month="june",
  pages="479--495",
  type="journal article in Web of Science"
}