计算机工程与应用 ›› 2013, Vol. 49 ›› Issue (21): 70-74.

• 网络、通信、安全 • 上一篇    下一篇

无线传感器网络层次参考时间同步算法的研究

王玉秀,黄  剑,石  欣   

  1. 重庆大学 自动化学院,重庆 400030
  • 出版日期:2013-11-01 发布日期:2013-10-30

Research on hierarchy referencing time synchronization for Wireless Sensor Networks

WANG Yuxiu, HUANG Jian, SHI Xin   

  1. School of Automation, Chongqing University, Chongqing 400030, China
  • Online:2013-11-01 Published:2013-10-30

摘要: 无线传感器在网络应用中要求节点间保持时间同步,但现存的经典时间同步算法,因节点的接收时间受时钟偏差和传输延迟的影响,其同步精度不高。为提高网络时间同步精度,均衡节点能耗,提出了一种改进的层次参考时间同步算法(Improved Hierarchy Referencing Time Synchronization,IHRTS)。该算法基于节点在层次结构中唯一物理位置的时间特性,采用贝叶斯估计对节点接收时间进行估算,缩小时间偏差的误差范围,获得比较精确的同步偏移量,从而改善时间同步精度;同时采用无线信道的广播特性与双向同步机制的同步思想,最小化了通信负载,均衡了节点能耗。通过仿真结果表明将贝叶斯估计方法应用到时间同步算法中,在均衡节点能量消耗同时有效地提高了网络同步精度。

关键词: 无线传感器网络, 时间同步算法, 同步精度, 贝叶斯估计

Abstract: The applications of Wireless Sensor Network need clock of sensors nodes to be synchronized. Due to the impact of node clock skew and the delay in information transmission, the receipt time of commonly used classic time synchronization algorithm is inaccurate, which will reduce the time synchronization accuracy to a certain extent. In order to improve time synchronization precision and balance node energy consumption of the network, this paper proposes an improved hierarchy referencing time synchronization algorithm, which is based on the time characteristics of each node in the hierarchy having the unique physical location. This paper uses Bayesian estimation methods to compute the receipt time of each node, narrows the error scope of time deviation, and finally obtains more precise synchronization time offset. It adopts wireless channel broadcast nature and bi-directional synchronization mechanisms, minimizes communication load and balances node energy consumption. The simulation results show that the use of Bayesian estimation method can significantly improve the accuracy of synchronization algorithm and balance node energy consumption.

Key words: Wireless Sensor Networks(WSN), time synchronization, synchronization precision, Bayesian estimation