Abstract
Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.
Highlights
Wireless Sensor Networks (WSNs) have broad applications in environmental monitoring, military reconnaissance, health care, and other fields [1,2,3]
In order to deal with the effects of crystal oscillator frequency deviation and the relative motion between nodes, a random delay modeling and synchronous tracking technology are introduced to improve the accuracy of time synchronization in WSNs, which will benefit the subsequent applications, such as the node location and tracking applications [5,6,7]
Comparing the four algorithms—Maximum Likelihood Estimation (MLE), ASCTS, Iterative Gaussian Mixture Kalman Particle Filter (IGMKPF) and Dirichlet process mixture (DPM)-Rao-Blackwellised particle filter (RBPF)—DPM-RBPF is better than MLE, ASCTS and IGMKPF in Mean Square Error (MSE) of the clock offset estimation
Summary
Wireless Sensor Networks (WSNs) have broad applications in environmental monitoring, military reconnaissance, health care, and other fields [1,2,3]. In order to deal with the effects of crystal oscillator frequency deviation and the relative motion between nodes, a random delay modeling and synchronous tracking technology are introduced to improve the accuracy of time synchronization in WSNs, which will benefit the subsequent applications, such as the node location and tracking applications [5,6,7]. Traditional synchronization technologies, such as Network Time Protocol (NTP) or Global.
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