A robust approach for clock offset estimation in wireless sensor networks

Erchin Serpedin, Jang Sub Kim, Jaehan Lee, Khalid Qaraqe

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The maximum likelihood estimators (MLEs) for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF) to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS) is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures.

Original languageEnglish
Article number132381
JournalEurasip Journal on Advances in Signal Processing
Volume2010
DOIs
Publication statusPublished - 2010
Externally publishedYes

Fingerprint

Maximum likelihood
Clocks
Wireless sensor networks
Composite materials
Sampling
Synchronization
Computer simulation

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

A robust approach for clock offset estimation in wireless sensor networks. / Serpedin, Erchin; Kim, Jang Sub; Lee, Jaehan; Qaraqe, Khalid.

In: Eurasip Journal on Advances in Signal Processing, Vol. 2010, 132381, 2010.

Research output: Contribution to journalArticle

@article{39bd64187fbc41c983b4bab314182540,
title = "A robust approach for clock offset estimation in wireless sensor networks",
abstract = "The maximum likelihood estimators (MLEs) for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF) to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS) is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures.",
author = "Erchin Serpedin and Kim, {Jang Sub} and Jaehan Lee and Khalid Qaraqe",
year = "2010",
doi = "10.1155/2010/132381",
language = "English",
volume = "2010",
journal = "Eurasip Journal on Advances in Signal Processing",
issn = "1687-6172",
publisher = "Springer Publishing Company",

}

TY - JOUR

T1 - A robust approach for clock offset estimation in wireless sensor networks

AU - Serpedin, Erchin

AU - Kim, Jang Sub

AU - Lee, Jaehan

AU - Qaraqe, Khalid

PY - 2010

Y1 - 2010

N2 - The maximum likelihood estimators (MLEs) for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF) to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS) is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures.

AB - The maximum likelihood estimators (MLEs) for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF) to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS) is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures.

UR - http://www.scopus.com/inward/record.url?scp=77955297048&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955297048&partnerID=8YFLogxK

U2 - 10.1155/2010/132381

DO - 10.1155/2010/132381

M3 - Article

VL - 2010

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

SN - 1687-6172

M1 - 132381

ER -