研究生: |
郇志翔 Hsun, Chih-Hsiang |
---|---|
論文名稱: |
在考慮現實因素的大範圍無線感測器網路上的合作式無線感測器網路診斷法 Cooperative Diagnosis for Realistic Large-Scale Wireless Sensor Networks |
指導教授: |
蔡明哲
Tsai, Ming Jer |
口試委員: |
趙禧綠
Chao, Hsi-Lu 蔡明哲 Tsai, Ming Jer 彭文志 Peng, Wen-Chih |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 資訊工程學系 Computer Science |
論文出版年: | 2012 |
畢業學年度: | 100 |
語文別: | 英文 |
論文頁數: | 26 |
中文關鍵詞: | 錯誤偵測 、無線感測器網路 |
外文關鍵詞: | Fault Diagnosis, wireless sensor networks |
相關次數: | 點閱:2 下載:0 |
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無線感測器網路的偵錯,是個非常重要的議題,因為網路中一但出現故障的點或是連結,就會降低感測的準確性,並縮短系統維持時間。為了將故障所造成的損害降到最低,網路維護者必須儘快地偵測出故障的位置,並將故障排除。現存的無線感測器網路偵錯法,大部分都是集中式的方法,或是只能在雙向連結的網路中運行的方法。集中式的偵錯法通常會需要網路中的感測器定期地發送節點或連結的資訊
給特定的點,這種作法不但會對網路中的節點造成很大的負擔,同時也會產生巨大的流量。另外,在真實的無線感測器網路中,點與點之間的連結情況有可能是雙向或單向。我們在本文中提出了一個合作式的偵錯法,並命名為CDM。CDM不但可以在單向連結網路中運行,還能夠縮減偵錯所需的訊息量。我們使用網路模擬器NS-2來評估CDM和知名偵錯法TinyD2的效能。我們評估的項目有偵錯率、錯誤警示率以及偵錯成本。模擬結果顯示,CDM在偵錯率及偵錯成本的效能都優於TinyD2,且和TinyD2擁有相近的錯誤警示率。
Fault diagnosis is an important issue in wireless sensor networks (WSNs) because the node/link failures not only decrease the accuracy of surveillance result, but also
shorten the lifetime of network. In order to minimize damages caused by node/link failures, the node/link failures must be detected and recovered by system supervisors as soon as possible. Although many diagnosis methods for WSNs are proposed, these methods are works either in a centralized manner or in the networks with bidirectional links. The centralized methods often need all sensors in the network to periodically report the node/link information to a specic node, resulting in heavy burdens of
sensors and generating a large amount of overhead in networks. In addition, in actual practice, the links in WSNs are either unidirectional or bidirectional. In this thesis, a distributed cooperative diagnosis method, termed CDM, is proposed to work in networks with unidirectional links and minimize the message overhead required for
diagnosis. Using NS-2 simulations, we evaluate the performance of the proposed method (CDM) and a well-known diagnosis method (TinyD2) in terms of detection ratio, fault-alarm ratio, and diagnosis overhead. The simulations demonstrate that our diagnosis method has good performance in terms of detection ratio and diagnosis overhead and provides comparable fault-alarm ratio to TinyD2.
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