AdaSynch: A General Adaptive Clock Synchronization Scheme Based on Kalman Filter for WSNs

被引:11
作者
Liu, Qiang [1 ,2 ]
Liu, Xue [2 ]
Zhou, Jing Lun [1 ]
Zhou, Gang [3 ]
Jin, Guang [1 ]
Sun, Quan [1 ,4 ]
Xi, Min [2 ,5 ]
机构
[1] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha 410073, Hunan, Peoples R China
[2] McGill Univ, Sch Comp Sci, Montreal, PQ, Canada
[3] Coll William & Mary, Dept Comp Sci, Williamsburg, VA 23185 USA
[4] Georgia Inst Technol, Sch Ind & Syst Engn, Atlanta, GA 30332 USA
[5] Xi An Jiao Tong Univ, Dept Comp Sci, Xian 710049, Peoples R China
关键词
Wireless sensor networks; Clock synchronization; Kalman filter; Hypothesis testing; TIME;
D O I
10.1007/s11277-010-0116-3
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Efficient and accurate clock synchronization is a challenge for wireless sensor networks (WSNs). Unlike previous works on clock synchronization in WSNs that consider communication delay as the main cause of clock inaccuracy, we propose a new adaptive synchronization scheme, AdaSynch, which considers the principium of the clock. We aim to overcome the challenges posed by WSNs' resource constraints such as limited energy and bandwidth, low precision oscillators and random factors. By implementing some experiments on TelosB platform, we find that the clock system switches between multiple clock models. Motivated by experiment results, we establish a general clock model which describes the clock offset in terms of the oscillators. We then design two kinds of basic Kalman filter models to achieve clock synchronization. In order to execute Kalman filtering, we propose a recursion method based on the Expectation-Maximization (EM) algorithm to access the parameters of the Kalman filter model adaptively. To describe alternation in the clock model, we propose a Multimodel Kalman filter, and put forward an adaptive method based on hypothesis testing to describe these complexities in the clock model. We demonstrate the performance gains of our scheme through experiments using different Kalman filter models based on experiment data.
引用
收藏
页码:217 / 239
页数:23
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