Effectiveness of Bootstrap Bias Correction in the Context of Clock Offset Estimators

Jeske, Daniel R.; Chakravartty, Arunava

doi:10.1198/004017006000000110

Cited by 12 publications

(11 citation statements)

References 11 publications

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“…This is an important problem that we do not discuss here due to space limitations. Adhikari, Denby, Mallows, and Meloche (2003), Paxson (1998), Moon, Skelly, and Towsley (1999), Zhang, Liu, and Xia (2002), Jeske and Sampath (2003), and Jeske and Chakravartty (2006) have provided relevant algorithms.…”

Section: Datamentioning

confidence: 97%

Statistical Aspects of the Analysis of Data Networks

et al. 2007

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Assessing and monitoring the performance of computer and communications networks is an important problem for network engineers. A considerable amount of work has been done on tools and techniques for data collection, modeling, and analysis within the network research community. This article presents an overview of the engineering problems and statistical issues, describes recent research developments, and summarizes ongoing work and areas for further research. Although there are many interesting issues related to network analysis, our focus here is on estimating and monitoring network quality-of-service parameters. We discuss methods for estimating edge-level parameters from end-to-end path-level measurements, an important engineering problem that raises interesting statistical modeling issues. Other topics include network monitoring, network visualization, and discovery of network topology. Data from a corporate network are used to illustrate the problems and techniques. As in any overview, the discussion is likely to be slanted toward the authors' own research interests.

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Section: Datamentioning

confidence: 97%

Statistical Aspects of the Analysis of Data Networks

et al. 2007

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“…It was shown in Jeske (2005) thatˆ n is the maximum likelihood estimator (MLE) of under the assumption that the network delays follow exponential distributions. The robustness of alternative estimators of , including bootstrap bias-corrected versions ofˆ , using mean squared error as the criterion was investigated in Jeske and Chakravartty (2006), and fixed sample size confidence intervals procedures for were developed in Li et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Sequential Fixed Width Confidence Intervals for the Offset Between Two Network Clocks

Jeske

2009

Sequential Analysis

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Estimation of the offset between two network clocks has received a lot of attention in the literature, with the motivating force being data networking applications that require synchronous communication protocols. Statistical modeling techniques have been used to develop improved estimation algorithms, with a recent development being the construction of a confidence interval based on a fixed sample size. Lacking in the fixed sample size confidence interval procedures is a useable relationship between sample size and the width of the resulting confidence interval. Were that available, an optimum sample size could be determined to achieve a specified level of precision in the estimator and thereby improve the efficiency of the estimation procedure by reducing unnecessary overhead in the network that is associated with collecting the data used by the estimation schemes. A fixed sample size confidence interval that has a prescribed width is not available for this problem. However, in this paper we develop and compare alternative sequential intervals with fixed width and demonstrate that an effective solution is available.

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“…The work in [41] investigated the BLUE and its corresponding bias-corrected estimator under a Pareto distribution: a heavy-tailed distribution that was adopted to model network delays for recent applications [42][43][44]. The authors in [41] examined the effectiveness of bootstrap bias correction of different estimators under varying assumptions for network delays.…”

Section: Bootstrap Bias Correctionmentioning

confidence: 99%

“…The authors in [41] examined the effectiveness of bootstrap bias correction of different estimators under varying assumptions for network delays. Some interesting examples were reported where bootstrap bias correction fails in the sense that the MSE increases or even the absolute bias increases.…”

Section: Bootstrap Bias Correctionmentioning

confidence: 99%

“…For instance, a somewhat surprising result is that the bootstrap bias correction of the exponential BLUE leads to a large absolute bias under Pareto network delays. Among these estimators and their corresponding bootstrap bias corrections, the bias-corrected estimator in Equation (35) was considered to be a significantly more robust estimator in [41].…”

Section: Bootstrap Bias Correctionmentioning

confidence: 99%

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An Overview of a Class of Clock Synchronization Algorithms for Wireless Sensor Networks: A Statistical Signal Processing Perspective

Jeske

Serpedin

2015

Algorithms

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Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal) exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs.

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Effectiveness of Bootstrap Bias Correction in the Context of Clock Offset Estimators

Cited by 12 publications

References 11 publications

Statistical Aspects of the Analysis of Data Networks

Statistical Aspects of the Analysis of Data Networks

Sequential Fixed Width Confidence Intervals for the Offset Between Two Network Clocks

An Overview of a Class of Clock Synchronization Algorithms for Wireless Sensor Networks: A Statistical Signal Processing Perspective

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