Abstract
Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.
Highlights
Clock synchronization is a very important and critical component in wireless Internet of things (IoT) [1]
Known that U 1,1 is a function about the sampling period τ, denoted U 1,1 = f (τ ), where U 1,1 represents the elements of the first row and the first column of the matrix U, which is, the upper bound of the 2 variance of clock offset error
Based on the observation model under incomplete measurement, the statistical error quantitative analysis of the variance boundary is performed on the synchronization error covariance in the case of packet loss
Summary
Clock synchronization is a very important and critical component in wireless Internet of things (IoT) [1]. The design of this paper is to analyze [13] the upper and lower bounds of error variance with the unreliable link clock by Kalman filter, and introduce the uncertainty driving mechanism to adjust the synchronous sampling period of clock adaptively. This is not the traditional periodic synchronization, but the communication overhead is minimized when the clock accuracy is reached. To the best of our knowledge, we are the first to investigate the adaptive clock synchronization for wireless IoT in underground mines, which resolves the packet loss and possible outliers based on Kalman filtering.
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