臺灣博碩士論文加值系統

論文摘要
由於現今半導體製造技術逐年的呈指數性成長，以及無線網路技術的普遍使用，使得具有相當的計算能力、儲存空間和無線通訊能力的感測節點能夠被製造出來。如今，無線感測網路(wireless sensor networks)的實際應用已經具有相當的可行性。我們可以把這些感測節點放置在我們想監測的環境或事物，然後透過這些節點感測事件狀況合作性的聚集資料。我們從這個無線感測網路的決策整合回報就可以把我們沒有親眼目睹的事件狀況具體呈現，並得以依此訊息來跟真實的環境互動。
由於無線感測網路常常被使用在人們所不能到達或者沒有任何人力在其周圍的環境，我們必須依賴無線感測網路的回報來得知真正環境的狀況。因此，這些感測節點所回報的資料的正確性便具有相當的重要性。正確的節點量測資料可以讓觀察者得知真實的現象，使得我們得以作出正確關於這個事件的決策。相反的，錯誤的感測節點量測資料可能會造成觀察者的誤解，使得無線感測網路不能依據真正環境的狀況作出處理，並且往往因此會造成相當的財力或物力的損失。為了解決這個問題，我們為感測節點設計一個錯誤偵測(fault detection)的方法來增加無線感測網路決策融合（decision fusion）的效果。
在這一篇論文中，首先簡單介紹一下無線感測網路的以及決策融合機制的運作情形，而後我們提出一個合作式的錯誤節點偵測機制。透過這個錯誤節點偵測機制的執行，我們可以把錯誤的節點（壞掉的節點或跟正常的節點的回報資料不一樣的節點）所回報的資料拿掉。然後我們使用沒有任何錯誤節點（或存在較少錯誤節點）所回報的資料來決策融合觀察的事件。如此我們所得到的決策融合的結果在效果上會比沒有使用錯誤節點偵測機制的效果來得好。此外，本論文還會進行一些模擬，把我們所提出的錯誤節點偵測機制應用在某些不同節點錯誤的模型之下，並且列出這個錯誤節點偵測機制挑錯的效果。

Abstract
Because of the exponential growth in the underlying semiconductor technology and the wide use of the wireless networking, many sensor nodes with computing ability, storage capability, and the power of communication are manufactured. The application of wireless sensor networks, WSNs for short, has been quite practicable nowadays. We can place the sensor nodes near to the things or environments which we want to monitor. Through the sensor nodes’ sensing and the collaborative decision fusion of the sensor network, the true condition of the event can be known according to the data reported from the sensor nodes and does not need to be seen or sensed by ourselves. The message reported from each sensor node can be used to make decision fusion about the condition of the event.
Sensor nodes are often deployed in harsh and inaccessible environments, and we must depend on the sensor nodes’ replies to know the true condition of the monitored environment. Therefore, the correctness of the data which is replied from the sensor nodes is very important. If the measurements replied from the sensor nodes are correct, these will help us to understand the real situation of the environment and pertinent decisions can be made as soon as possible. If the measurements replied from the sensor node are wrong, these will make us misunderstand the situation of the environment. In this way, the event can not be handled properly and this may cause a great damage to us. In order to solve the problem, there is a need to develop a means to detect the faulty sensor nodes or provide the fault-tolerance capability without the nearby involvement of human beings when utilizing WSNs to perform decision fusion of event detection.
This thesis first introduces the operation of WSNs and the decision fusion. Then, a collaborative sensor fault detection scheme in WSNs is proposed. With the aid of the scheme, the faulty nodes can be easily identified and they are removed from the computation of the decision fusion. In such a way, WSNs will have better performance of the decision fusion than that without the fault detection scheme. After that, the proposed fault detection scheme is used to do a series of simulations of some kinds of different fault models, and its performance is also shown.

Contents
論文摘要 i
Abstract iii
Contents v
List of Figures vii
List of Tables ix
1 Introduction 1
1.1 Background and Motivation 1
1.2 The Previous Work of Fault Detection and Decision Fusion 2
1.3 Contents of the Thesis 3
2 Wireless Sensor Networks 5
2.1 Introduction of WSNs 5
2.2 Some Important Research of WSNs 6
3 Sensor Network Operation 8
3.1 The Sensor Network Model 8
3.2 Likelihood Ratio 9
4 On-Line Sensor Faults Detection 11
4.1 The Purpose of Doing Fault Detection 11
4.2 The Process of Fault Detection 11
4.3 Computing the Values of P(ui|H1) and P(ui|H0) 12
4.4 Computing the probability of m Faulty Sensor Nodes 13
4.5 The Sensor Fault Detection at Sensor Nodes 15
4.6 The Decision of the Number of Faulty Sensors at the Fusion Center 17
5 Simulation Results 19
5.1 The Common Faulty Models of Sensors 19
5.2 Simulation Results in Different Scenarios 20
5.3 Performance of Decision Fusion 32
6 Conclusion and Future Work 35
6.1 Conclusion of the Thesis 35
6.2 Future Work 36
References 38

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