Least Squares-Based Estimation of Relative Clock Offset and Frequency in Sensor Networks with High Latency

Pettyjohn, Jeff; Jeske, Daniel R.; Li, Jun

doi:10.1109/tcomm.2010.092810.090352

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…Therefore, the performance of estimators particularly designed for a certain distribution may degenerate greatly under another type of distribution, and there is a need for developing efficient estimation methods that are robust to the unknown random delays of WSNs. Based on the classic two-way message exchange mechanism, we describe several robust estimation methods in this section, namely the bootstrap bias correction [30], the composite particle filtering [38] and the least squares estimator [39].…”

Section: Pairwise Clock Synchronization Under Unknown Random Delaysmentioning

confidence: 99%

“…The least squares approach in [39] provides a general framework for joint estimation of clock offset and skew, irrespective of the network delay distribution type. The model adopted by the least squares approach is obtained by adding two equations in Equation (2) together and reduces to:…”

Section: Least Squares Estimatorsmentioning

confidence: 99%

“…Therefore, several alternative least squares estimators were proposed in [39] to further improve the estimation accuracy of clock offset and skew. An example of an enhanced least squares estimator is referred to as the constrained least squares (CLS) estimator, and it is found by minimizing the sum of squared residuals subject to a set of constraints on θ and f formed by considering the time stamp model in Equation (2):…”

Section: Least Squares Estimatorsmentioning

confidence: 99%

“…Therefore, two additional least squares estimators, referred to as the feasibility checked least squares (FLS) estimator and the Paxson-based estimator, respectively, were proposed in [39] to reduce the computational complexity. Experimental results showed that the FLS estimator and the Paxson-based estimator perform comparably and sometimes better than the CLS estimator, while exhibiting the advantage of much less computational complexity.…”

Section: Least Squares Estimatorsmentioning

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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Section: Pairwise Clock Synchronization Under Unknown Random Delaysmentioning

confidence: 99%

Section: Least Squares Estimatorsmentioning

confidence: 99%

Section: Least Squares Estimatorsmentioning

confidence: 99%

Section: Least Squares Estimatorsmentioning

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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“…Jeff S. Pettyjohn, et. [6] proposed a least squares based estimation of relative clock offset and frequency for high latency sensor networks. The research result shows that the combination of a feasible checked least square estimator with the Paxsion-base estimator performs comparable and sometimes better than the constrained least square based estimator.…”

Section: Introductionmentioning

confidence: 99%

Genetic Based Time Synchronization Optimize Algorithm for Wireless Sensor Networks

Chen¹,

Wang²,

Xiao³

2014

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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Abstract-Time synchronization provides uniform timescale for distributed sensor networks. In this paper a genetic based time synchronization optimize algorithm is proposed. It first generated the nondeterministic transmitting delay and then adopted the genetic algorithm to simulating these delays. Minimal linear fit residual error is selected as fitness function to evaluate the simulating effect. After finding the best fitness delay sequence, the time skew and offset is estimated by linear regression. The experiment result shows that the synchronization precision could be improved from 11.8 s  to 3.5 s  .

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Low-complexity time synchronisation for 3-dimensional underwater communications environment using estimated instantaneous node velocity

Jeong

Kim

2018

Digital Signal Processing

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Least Squares-Based Estimation of Relative Clock Offset and Frequency in Sensor Networks with High Latency

Cited by 7 publications

References 13 publications

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

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

Genetic Based Time Synchronization Optimize Algorithm for Wireless Sensor Networks

Low-complexity time synchronisation for 3-dimensional underwater communications environment using estimated instantaneous node velocity

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