Effects of channel delays on underflow events of compressed video over the Internet

Chakravartty

2006

Technometrics

Estimating and correcting the offset between two or more clocks is an important problem in data communication networks. For example, Internet telephony depends on network routers having a common notion of time, and cellular networks provide a higher quality of service by using transmission protocols that depend on neighboring base stations knowing the offset that exists between their local clocks. In previous work it was shown that bootstrap bias correction of Paxson's well-known estimator of clock offset produces an estimator with improved bias and mean squared error (MSE) properties. In addition, the ordered-BLUE (o-BLUE) under an exponential distribution for network delays was derived, and its bootstrap bias-corrected form was shown to have lower bias and MSE than the bootstrap bias-corrected form of Paxson's estimator when network delays follow lognormal, gamma, and Weibull distributions. The inferred robustness of the bias-corrected o-BLUE to the assumed distribution of network delays is an attractive property, because no single family of distributions can consistently characterize network delays. Recent Internet traffic modeling research has suggested that the Pareto distribution is an applicable distribution for network delays. That finding motivates the work in this article concerned with clock offset estimation and the effectiveness of bootstrap bias-corrected estimators in the context of heavy-tailed network delays. An important result is the demonstrated robustness of the bias-corrected form of Paxson's estimator in the presence of heavy-tailed network delays. An additional and somewhat surprising result is that if network delays follow a Pareto distribution, then bootstrap bias correction of the exponential o-BLUE fails in the sense that the absolute bias increases. The same finding is observed in an alternative class of heavy-tailed distributions, where the success or failure of bootstrap bias correction is traceable to a parameter that reflects the weight of the tail. Although it is well known that bias correction can increase MSE, to the best of our knowledge no practical application in which it increases the absolute bias has been identified up to this point.

Section: Introductionmentioning

confidence: 99%

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

Chakravartty

2006

Technometrics

“…Deriving estimators for and will depend on the underlying models for the distribution of network delays. Several alternative distributions have been proposed, including exponential, gamma, lognormal, Weibull, and Pareto distributions (see, for example, Claffy et al [5], Mukherjee [6], Fujimoto et al [7], Loguinov and Radha [8], Bovy et al [9]).…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimators of clock offset and skew under exponential delays

Appl Stoch Models Bus & Ind

2009

SUMMARYAccurate clock synchronization is essential for many data network applications. Various algorithms for synchronizing clocks rely on estimators of the offset and skew parameters that describe the relation between times measured by two different clocks. Maximum likelihood estimation (MLE) of these parameters has previously been considered under the assumption of exponentially distributed network delays with known means. We derive the MLEs under the more common case of exponentially distributed network delays with unknown means and compare their mean-squared error properties to a recently proposed alternative estimator. We investigate the robustness of the derived MLE to the assumption of non-exponential network delays, and demonstrate the effectiveness of a bootstrap bias-correction technique.

“…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%

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

Serpedin

2015

Algorithms

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.