HU Bing and SUN Zhixin, “On Least Squares Weighted Recursive Estimation of Clock Skew and Offset in Wireless Sensor Networks,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 1041-1047, 2017, doi: 10.1049/cje.2017.08.023
Citation: HU Bing and SUN Zhixin, “On Least Squares Weighted Recursive Estimation of Clock Skew and Offset in Wireless Sensor Networks,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 1041-1047, 2017, doi: 10.1049/cje.2017.08.023

On Least Squares Weighted Recursive Estimation of Clock Skew and Offset in Wireless Sensor Networks

doi: 10.1049/cje.2017.08.023
Funds:  This work is supported by the National Natural Science Foundation of China (No.61373135, No.61672299), the Research Project of Jiangsu Province (No.BY2013011), and the Natural Science Foundation of Jiangsu Province of China (No.BK20140883).
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  • Corresponding author: SUN Zhixin (corresponding author) was born in Anhui province. He is now a professor and Ph.D. supervisor of the college of Internet of Things in Nanjing University of Posts and Telecommunications. His research interests include computer networks and security, multimedia communication and mobile Internet, etc. (Email:Sunzx@njupt.edu.cn.)
  • Received Date: 2017-01-12
  • Rev Recd Date: 2017-02-24
  • Publish Date: 2017-09-10
  • Three clock synchronization algorithms for Wireless sensor networks (WSNs) in Pairwise broadcast synchronization (PBS) mechanism are derived. They include the joint Least squares estimator (LS), joint Least squares weighted estimator (LSW) and joint Least squares weighted Recursive estimator (R-LSW). For these estimators, the corresponding algorithms are derived and described by assuming a Gaussian random delay model. Unlike PBS, these estimators can achieve the Cramer-Rao lower bound (CRLB) for both listening node and active node without knowledge of the deterministic delay. The purpose of considering R-LSW is to reduce the use of storage space with the method of estimating while observing. Simulation and analytical results verify that the estimators are efficient.
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