臺灣博碩士論文加值系統

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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