ARS: Adaptive Robust Synchronization for Underground Coal Wireless Internet of Things - Kuiyuan Zhang; Mingzhi Pang; Yuqing Yin; Shouwan Gao; Pengpeng Chen (Sensors, 2020)
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.