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研究生:陳建佑
研究生(外文):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
論文名稱(外文):Energy-Efficient Sequential Information Accumulation Schemes for Distributed Estimation in Wireless Sensor Networks
指導教授:蔡育仁蔡育仁引用關係
指導教授(外文):Tsai, Yuh-Ren
學位類別:博士
校院名稱:國立清華大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
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:estimationwireless sensor networksquantization
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  • 收藏至我的研究室書目清單書目收藏:1
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

[1] Y. Li, M. T. Thai, and W. Wu, Wireless Sensor Networks and Applications, Springer, 2008, pp.331–347.
[2] K. Sohraby, D. Minoli, and T. Znati, Wireless Sensor Networks: Technology, Protocols, and Applications, John Wiley & Sons, Hoboken, New Jersey, 2007.
[3] R. Madan, S. Cui, S. Lall, and A. Goldsmith, “Modeling and optimization of transmission schemes in energy-constrained wireless sensor networks,” IEEE/ACM Trans. Networking, vol. 15, no. 6, pp. 1359–1372, 2007.
[4] J. Li and G. AlRegib, “Function-Based Network Lifetime for Estimation in Wireless Sensor Networks,” IEEE Signal Processing Lett., vol. 15, pp. 533–536, 2008.
[5] I. Akyildiz, W. Su, Y. Sankarsubramaniam, and E. Cayirci, “Wireless sensor networks: A survey,” Comput. Netw., vol. 38, pp. 393–422, Mar. 2002.
[6] Wald, Sequential Analysis. New York: Wiley, 1947.
[7] J. Li and G. AlRegib, “Rate-constrained distributed estimation in wireless sensor networks,” IEEE Trans. Signal Process., vol. 55, no. 5, pp. 1634–1643, May 2007.
[8] Z.-Q. Luo and J.-J. Xiao, “Decentralized estimation in an inhomogeneous sensing environment,” IEEE Trans. Inf. Theory, vol. 51, no. 10, pp. 3564–3575, Oct. 2005.
[9] F. Gray, “Pulse code communication,” U.S. patent no. 2,632,058, March 17, 1953.
[10] S. M. Kay, Fundamentals of Statistical Signal Process.: Estimation Theory. Upper Saddle River, NJ: Prentice-Hall, 1993.

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