無線網格網路上廣域式異常診斷技術之研究｜國立臺灣科技大學博碩士論文系統

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研究生：	黃嘉威 Chia-wei Huang
論文名稱：	無線網格網路上廣域式異常診斷技術之研究 Network-Wide Fault Diagnosis in Wireless Mesh Networks
指導教授：	陳郁堂 Yie-tarng Chen
口試委員:	林銘波 Ming-bo Lin 方文賢 Wen-hsien Fang 徐俊傑 Chiun-chieh Hsu
學位類別：	碩士 Master
系所名稱：	電資學院 - 電子工程系 Department of Electronic and Computer Engineering
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	41
中文關鍵詞：	主成分分析、隱馬可夫模型、異常診斷、無線網格網路
外文關鍵詞：	Principal Component Analysis, Hidden Markov Model, Fault Diagnosis, Wireless Mesh Networks
相關次數：	點閱：127 下載：1
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無線網格網路技術的出現成為了未來無線骨幹網路的重要技術，但是對於無線網格網路上異常診斷的研究卻很有限。除此之外，無線環境的不穩定特性以及網格網路的多跳躍特性更增加異常診斷的挑戰性。在本論文中，我們研究用應於無線網格網路上的廣域式異常診斷技術。本篇論文將異常診斷分為三個步驟，首先偵測出異常時間點，接著識別出異常網路節點，最後診斷異常網路節點的異常原因。我們運用主成分分析的方式將某個時間槽內的觀測資料區分出正常成分與異常成分，依此偵測異常時間點與識別異常網路節點，最後利用隱馬可夫模型進行異常原因分類。模擬結果顯示我們的方法能正確及有效的找出異常時間點，識別異常網路節點以及診斷異常原因。

Wireless mesh networks have emerged a key technology for next-generation broadband wireless access. However, research on fault diagnosis in wireless mesh network is limited. On the other hand, the unreliable characteristic of wireless channels poses fault diagnosis in wireless mesh networks a challenge issue. In this research, we investigate network-wide fault diagnosis schemes for wireless mesh network. The fault diagnosis can be divided into three steps: The first step is detecting abnormal time points; the second step is identifying abnormal mesh nodes; the final step is diagnosing the fault type of each abnormal node. We use subspace approach to separate normal space and abnormal space and use Hidden Markov Model for fault modeling. The simulation result shows that our fault diagnosis can achieve high accuracy in detection, identification and diagnosis.

INTRODUCTION	1
1.	Wireless Mesh Networks	1
2.	Motivation	2
3.	Problem Statement	3
4.	Goal	4
5.	Related Work	4
6.	Contribution	5
FAULT DIAGNOSIS ARCHITECTURE IN WIRLESS MESH NETWORKS	7
1.	Centralized Fault Diagnosis Architecture	8
2.	Distributed Fault Diagnosis Architecture	9
FAULT DIAGNOSIS APPROACH	11
1.	Major Concept	11
2.	Features for Fault Diagnosis	12
3.	Subspace Construction via Principal Component Analysis	14
4.	The Detection Step	16
5.	The Identification Step	17
6.	Fault Modeling by Hidden Markov Model	20
7.	The Diagnosis Step	21
PERFORMANCE EVALUATION	23
1.	Methodology	23
2.	Performance Metrics	25
3.	Experimental Result	26
CONCLUSION	29
REFERENCE	    30

                                

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