Dynamic Clock Skew Estimation and Fusion Acquainted with Environmental Factors

Abstract

Clock skew reflects the drift rate of a clock w.r.t. the nominal or reference clock frequency, which is the root cause of clock drifting. However, as the output of clock is largely affected by some environmental factors. Therefore, clock skew estimation is particularly difficult in wireless sensor networks (WSNs), as the working environments of WSN are usually dynamic, unpredictable or even hazard. Besides, sensors are usually powered by batteries with limited communication and computation capacity. The clock skew is found to be non-stationary containing severe measurement and process noises. Thus, we attempt to jointly consider the environmental factors into the clock skew estimation using Kalman filter. We propose to use the change of temperature and/or voltage to enhance the clock skew estimation performance. Besides, in multi-hop wireless networks, where the synchronization is done in a hierarchical procedure, one node may have the chance to receive more than one timestamps. Therefore, we propose to use the information fusion technique to dynamically combine the information contained in different timestamps to promote the clock skew estimation accuracy. Besides, we further derive the statistic lower bound of estimation errors, which can serve as a benchmark. The performance of the proposed schemes have been verified by extensive simulation results, where the root mean square error (RMSE) can be reduced by around 60% when compared with the previous solutions.