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

  • Authors:

  • Qiang Liu;Xue Liu;Jing Lun Zhou;Gang Zhou;Guang Jin;Quan Sun;Min Xi

  • Affiliations:

  • College of Information System and Management, National University of Defense Technology, Changsha, China 410073 and School of Computer Science, McGill University, Montreal, Canada;School of Computer Science, McGill University, Montreal, Canada;College of Information System and Management, National University of Defense Technology, Changsha, China 410073;Department of Computer Science, College of William and Mary, Williamsburg, USA;College of Information System and Management, National University of Defense Technology, Changsha, China 410073;College of Information System and Management, National University of Defense Technology, Changsha, China 410073 and School of Industrial and Systems Engineering, Georgia Institute of Technology, A ...;School of Computer Science, McGill University, Montreal, Canada and Department of Computer Science, Xi'an Jiaotong University, Xi'an, China

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2012

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Abstract

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.