东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (2): 168-171.DOI: -

• 论著 • 上一篇    下一篇

一种基于HMM的WSN先验事件分析模型

李传文;谷峪;李芳芳;于戈;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-02-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60773220)

Study on analysis model of HMM-Based WSN prior event

Li, Chuan-Wen (1); Gu, Yu (1); Li, Fang-Fang (1); Yu, Ge (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-02-15 Published:2013-06-20
  • Contact: Li, C.-W.
  • About author:-
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摘要： 为了充分利用仿真过程中产生的有用数据,研究了利用隐马尔可夫模型对节点状态的识别方法.针对无线传感网络的特点,通过对Baum-Welch算法进行扩展,设计了一种节点事件识别算法.详细论述了该算法中状态约束、观察窗口的处理方法.深入分析了事件识别算法中节点数据获取、状态建模、隐状态推导等关键问题,并对该算法的时间、空间复杂度进行了解释.设计实现了一种无线传感网络仿真平台,验证了算法的有效性和实用性.

关键词: 无线传感网络, 先验事件, 隐马尔可夫模型, Baum-Welch算法, SnSim仿真平台

Abstract: To make full use of the effective data produced from simulation process, the hidden Markov model is applied to identifying the status of simulated sensor nodes. Taking account of the characteristics of WSN (wireless sensor network), the Baum-Welch algorithm is extended to design a complex event recognition algorithm that is discussed in detail for the state constraints and the way to process the observation window. Such key problems as data acquisition, state modeling and hidden-state inference in the event recognition algorithm are analyzed in depth, with the temporal and spatial complexities of the algorithm explained. A WSN simulation platform is therefore designed and implemented to verify the effectiveness of the algorithm and the practicality of the system.

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