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Clock Offset Estimation in Wireless Sensor Networks Using Bootstrap Bias Correction

机译:使用自举偏置校正的无线传感器网络中的时钟偏移估计

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摘要

Wireless sensor networks have become an important and promising research area during the last recent years. Clock synchronization is one of the areas that play a crucial role in the design, implementation, and operation of wireless sensor networks. Under the assumption that there is no clock skew between sensor nodes, the Maximum Likelihood Estimate (MLE) of clock offset was proved by [1] for clock synchronization protocols that assume exponential random delays and a two-way message exchange mechanism such as TPSN (Timing-sync Protocol for Sensor Networks [2]). This MLE is asymptotically unbiased. However, the estimator is biased in the presence of a finite number of samples and much more biased in asymmetric random delay models, where the upstream delay characteristics are different from the downstream delay characteristics, and thus its performance is deteriorated. This paper proposes clock offset estimators based on the bootstrap bias correction approach, which estimates and corrects the bias of the MLE in the exponential delay model, and hence it results in better performances in mean squared error (MSE).
机译:在最近几年中,无线传感器网络已成为重要且有前途的研究领域。时钟同步是在无线传感器网络的设计,实施和操作中起关键作用的领域之一。在传感器节点之间不存在时钟偏斜的假设下,[1]对于采用指数随机延迟和双向消息交换机制(例如TPSN)的时钟同步协议,[1]证明了时钟偏移的最大似然估计(MLE)。传感器网络的时序同步协议[2]。该MLE渐近无偏。然而,估计器在有限数量的样本的存在下存在偏见,在非对称随机延迟模型中则存在更大的偏见,在该模型中上游延迟特性与下游延迟特性不同,因此其性能会下降。本文提出了一种基于自举偏差校正方法的时钟偏移估计器,该方法可估计和校正指数延迟模型中MLE的偏差,从而在均方误差(MSE)中产生更好的性能。

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