采用双向消息交换机制, 有效地解决了无线传感网络中的同步问题. 利用非同步本地时钟的仿射模型, 建立同步最大似然估计器, 得到最佳一致解. 但是, 直接实现最大似然估计器通常是困难的, 因为最大似然成本函数是高度非线性和非凸的. 为了有效地获得最大似然估计器, 提出一种新的双迭代算法, 该算法通过搜索传感器源节点信息和时钟参数(频率偏移和相位偏移)来获得最大似然估计器. 从而证明算法的收敛性, 并分析了该算法对传感器及时钟参数的估计精度在低噪声条件下近似达到克拉美罗界. 与已有方法相比, 该算法具有计算效率更高、锚节点更少、通信开销更小等优点.

This paper presents a solution to the problem of synchronization in Wireless Sensor Networks (WSN) by adopting the two-way message exchange mechanism. Using the affine model of the unsynchronized local clock, a Maximum Likelihood (ML) estimator is established to obtain the best consistent solution. However, directly implementing an ML estimator is often difficult because the ML cost function is highly nonlinear and non-convex. In order to effectively obtain the ML estimator, a new double iterative algorithm is proposed, which obtains the ML estimator by searching the sensor source node information and clock parameters (clock skew and clock offset). The convergence of the proposed algorithm is proved, and the estimation accuracy of the sensor and clock parameters of the node is adjusted close to Cramer-Rao Lower Bound (CRLB) under low noise conditions. Compared with the existing methods, the algorithm has the advantages of higher computational efficiency, fewer anchor nodes and lower communication cost.