無線感測網路分散式估計之節能循序訊息累積方法研究

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研究生：	陳建佑 Chen, Chien-Yu
論文名稱：	無線感測網路分散式估計之節能循序訊息累積方法研究 Energy-Efficient Sequential Information Accumulation Schemes for Distributed Estimation in Wireless Sensor Networks
指導教授：	蔡育仁 Tsai, Yuh-Ren
口試委員:	洪樂文 Hong, Yao-Win 林澤 Lin, Che
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 通訊工程研究所 Communications Engineering
論文出版年：	2013
畢業學年度：	101
語文別：	英文
論文頁數：	61
中文關鍵詞：	估計、無線感測網路、量化
外文關鍵詞：	estimation, wireless sensor networks, quantization
相關次數：	點閱：4 下載：0
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在無線感測網路作分散式估計時，使用連續訊號累積方法使得節省能量變得更有可能。如何去減少能量的消耗以及如何去增加估計的效能對於在無線感測網路作分散式估計時，是很重要的議題。我們藉由把感測點分成兩組，一組是比較可靠的，我們把連續訊號累積方法稍作修改使其變成分組式連續訊號累積方法。接著，我們在提出的方法中加入權重向量的概念，因為此項設計，我們可能達到能量的節省或者增加估計的效能。於是我們分別再設計了兩種方法，節能取向分組式連續訊號累積方法與提升效能取向分組式連續訊號累積方法。

Sequential information accumulation (SIA) is a technique that makes energy conservation possible for distributed estimation in wireless sensor networks. How to reduce energy consumption and how to improve estimation performance are the main topics for distributed estimation in wireless sensor networks. By separating sensor nodes into two groups, one is more reliable than the other, we modify SIA scheme into SIA with group separation (SIA-GS) scheme. Then, by introducing the weighting vector to the SIA-GS scheme, we can achieve both targets, energy saving and performance improvement. Thus, we propose two schemes, SIA-GS for performance improvement (SIA-GS-PI) scheme and SIA-GS for energy saving (SIA-GS-ES) scheme, just by adjusting the weighting factors.

CONTENTS

摘要    ii
ABSTRACT    iii
誌謝    iv
CONTENTS    v
LIST OF FIGURES    vii
LIST OF TABLES    ix
Chapter 1    Introduction    1
Chapter 2    System Model and Background Knowledge    4
2.1    System Model    4
2.2    Sequential Information Accumulation (SIA) Strategy    9
Chapter 3    Tentative Testing (TT) Scheme    14
3.1    TT Scheme    14
3.1.1    Operation of the TT Scheme    15
3.2    Average Conditional Error Probability of the TT scheme    18
3.3    Analysis on the TT Scheme    22
Chapter 4    SIA with Group Separation (SIA-GS) Scheme    26
4.1    Average Conditional Error Probability of the Sensor Node    26
4.2    Probe into the BRGE Mapping Table    32
4.3    SIA-GS scheme    32
4.3.1    Operation of the SIA-GS Scheme    33
Chapter 5    Analysis on the SIA-GS Scheme    38
5.1    Optimization for SIA-GS scheme    38
5.1.1    Optimization for Performance Improvement    38
5.1.2    Optimization for Energy Saving    40
5.2    Decision Error Probability    42
5.3    SIA-GS for Performance Improvement (SIA-GS-PI) Scheme    46
5.4    SIA-GS for Energy Saving (SIA-GS-ES) Scheme    46
Chapter 6    Simulation Results and Discussion    47
6.1    Different Crossover Probabilities    47
6.2    Different Number of Sensor Nodes    54
Chapter 7    Conclusion    60
REFERENCES    61

                                

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