Bletsas Aggelos
MIT Media Laboratory, Cambridge, MA 02139, USA.
IEEE Trans Ultrason Ferroelectr Freq Control. 2005 Sep;52(9):1452-60. doi: 10.1109/tuffc.2005.1516016.
Time information is critical for a variety of applications in distributed environments that facilitate pervasive computing and communication. This work describes and evaluates a novel Kalman filtering algorithm for end-to-end time synchronization between a client computer and a server of "true" time [e.g., a Global Positioning System (GPS) source] using messages transmitted over packet-switched networks, such as the internet. The messages exchanged have the network time protocol (NTP) format, and the algorithm evaluated, is performed only at the client side. The Kalman filtering algorithm is compared to two other techniques widely used, based on linear programming and statistical averaging, and the experiments involve independent consecutive measurements (Gaussian case) or measurements exhibiting long-range dependence (self-similar case). Performance is evaluated according to the estimation error of frequency offset and time offset between client and server clock, the standard deviation of the estimates and the number of packets used for a specific estimation. The algorithms could exploit existing NTP infrastructure, and a specific example is presented.
时间信息对于分布式环境中促进普适计算和通信的各种应用至关重要。这项工作描述并评估了一种新颖的卡尔曼滤波算法，该算法用于通过诸如互联网之类的分组交换网络传输的消息，在客户端计算机与“真实”时间服务器（例如全球定位系统（GPS）源）之间进行端到端时间同步。交换的消息具有网络时间协议（NTP）格式，并且所评估的算法仅在客户端执行。将卡尔曼滤波算法与另外两种广泛使用的技术（基于线性规划和统计平均）进行了比较，实验涉及独立的连续测量（高斯情况）或表现出长程相关性的测量（自相似情况）。根据客户端和服务器时钟之间的频率偏移和时间偏移的估计误差、估计的标准差以及用于特定估计的数据包数量来评估性能。这些算法可以利用现有的NTP基础设施，并给出了一个具体示例。
