北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2013, Vol. 36 ›› Issue (1): 101-104.doi: 10.13190/jbupt.201301.101.153

• 研究报告 • 上一篇    下一篇

基于感知数据的无线传感网被动诊断方法

莫路锋1,2,毛方杰2,聂江武2,宣子蔚3   

  1. 1. 西安交通大学 计算机系, 西安 710049;<br>2. 浙江农林大学 低碳与物联网联合实验室, 浙江 临安 311300;<br>3. 浙江大学 信息与电子工程学系, 杭州 310058
  • 收稿日期:2012-07-22 修回日期:2012-09-03 出版日期:2013-02-28 发布日期:2013-01-19
  • 通讯作者: 莫路锋 E-mail:44760199@qq.com
  • 作者简介:莫路锋(1979-),男,副教授,硕士生导师,E-mail:molufeng@gmail.com
  • 基金资助:

    国家自然科学重大基金(61190114);国家重点基础研究发展规划项目(2011CB302705);国家自然科学青年科学基金项目(61100236);浙江农林大学青年创新基金项目(2009RC11)

Passive Diagnosis for WSN Using Sensing Data

MO Lu-feng1,2, MAO Fang-jie2, NIE Jiang-wu2, XUAN Zi-wei3   

  1. 1. Computer Science Department, Xian Jiaotong University, Xian 710049, China;<br>2. Joint Laboratory On Internet of Things and Global Climate Change, Zhejiang A&F University, Zhejiang Lin’an 311300, China;<br>3. Department of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
  • Received:2012-07-22 Revised:2012-09-03 Online:2013-02-28 Published:2013-01-19

摘要:

由于硬件与网络资源的极度受限,无线传感器网络(WSN)的故障诊断成为该领域内的一个研究难点.针对现有诊断方法网络开销大、方法复杂等缺陷,提出了一种基于感知数据的故障诊断(DSD)方法.以部署在实际森林环境中的GreenOrbs系统收集的大量感知数据为基础,通过分析感知数据特征分类的方法,建立与网络故障之间的对应关系,以自主学习的方式不断演化故障知识库,确定故障类型.实验结果表明,与其他诊断方法相比,DSD具有网络通信负担小、资源消耗低、诊断效率高等优点,并支持在大规模WSN的实际部署.

关键词: 无线传感器网络, 故障诊断, 感知数据, 粗糙集

Abstract:

Diagnosis for wireless sensor network (WSN) is difficult, for the constraint of sensor node hardware and limit of network resources. Many existing approaches mainly aim at gathering a large amount of status information from individual sensor nodes, which brings additional communication on the network. Diagnosis based on sensing data (DSD) is designed and implemented,a passive diagnosis method using sensing date in WSN. GreenOrbs is a long-term deployment WSN system in the forest, which releases a large number of actual monitoring data on the Internet (http: //www.greenorbs.org). By studying those public sensing data and network status, a knowledge library to store those related regular patterns is established. Based on this self-improvement system, failures and traffic overhead can both be deduced. The experiment carried out in Testbed and realistic environment show that DSD can improve diagnosis efficiency, decrease the burden of network traffic, and have the flexibility with the arrangement of large-scale WSN.

Key words: wireless sensor network, diagnosis, sensing data, rough set

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