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博碩士論文 etd-0527114-165201 詳細資訊
Title page for etd-0527114-165201
論文名稱
Title
最佳化感測節點佈建以重建隨機訊號場之研究
Study of Optimization of Sensor Node Deployment to Reconstruct Random Signal Field
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
37
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2014-06-06
繳交日期
Date of Submission
2014-06-27
關鍵字
Keywords
均方差、估計、融合中心、過濾、消耗功率、高斯隨機場、傳感器
Censoring, Mean square error, Power Consumption, Fusion center, Estimate, Gaussian random field, Sensor
統計
Statistics
本論文已被瀏覽 5703 次,被下載 47
The thesis/dissertation has been browsed 5703 times, has been downloaded 47 times.
中文摘要
過去的文獻中已探討使用有限個數的感測器節點去估計整個高斯隨機場。
在此問題上, 當佈建完成後, 有佈署的感測器節點, 將負責回報給融合中心該位置上的感測測量值, 而量測值除了用來估計這些有放置感測器位置上的真實狀態值, 也用來估計沒有放置感測器節點上的狀態值。
由此概念, 以有限感測器節點個數去估測整個高斯隨機場就可以實現, 但如需要再加強對整個高斯隨機場的估測效能, 是必要增加感測器數量、傳輸頻寬和總體的消耗功率。
本論文提供一種新的的感測器節點佈建方式, 利用過濾數據的方法, 在需要增加微量的感測器數量和所佔用的通道頻寬保持不變的情況下, 就能得到較低的均方差也能降低所需的消耗功率。
Abstract
The problem of the reconstruction of a Gaussian random field using a finite number of sensor nodes has been considered in the literature.
In this problem, after the sensor deployment, each deployed sensor node will report its sensor observation taken at its location to a fusion center, in which the reported measurements are used to reconstruct the whole random field, i.e., not only estimate the statuses present at the deployed sensor locations, but also estimate the statuses at the spatial points without sensor nodes.
Therefore, it is possible to reconstruct the whole Gaussian random field using a finite number of sensor nodes.
However, to improve the reconstruction performance, it is necessary to increase the number of sensor nodes, transmission bandwidth, and the power consumption.
This thesis, however, proposes a new method of sensor deployment.
Specifically, we incorporate the censoring sensors approach into the sensor deployment strategy.
The results shows that the proposed scheme can achieve a lower mean square error (MSE) and reduce the power consumption by only slightly increasing the number of sensor nodes.
目次 Table of Contents
摘要 i
Abstract ii
1 序論 1
1.1 研究動機 1
1.2 研究方法 2
1.3 論文貢獻 3
2 系統模型 4
2.1 系統模型和性能指標 4
2.2 考慮有功率消耗的位置放置問題 7
3 隨機傳送 10
3.1 感測方式 10
3.2 感測效能和消耗功率的比較 13
4 過濾數據 16
4.1 過濾數據的制定 17
4.2 使用過濾數據的效能 19
5 結果和分析 20
5.1 新感測方法的效能與功耗分析 21
5.2 代入真實實驗室感測數據 25
6 結論 28
參考文獻 29
參考文獻 References
[1] K. J. Worsley, ”Gaussian random field,” in Encyclopedia of Environmetrics,A. H. El-Shaarawi and W. W. Piegorsch, Eds. New York:Wiley, 2002.
[2] C. Ko, J. Lee, and M. Queyranne, ”An exact algorithm for maximum entropy sampling,” Oper. Res., vol. 43, pp. 684-691, Jul.-Aug 1995.
[3] C. Guestrin, A. Krause, and A. P. Singh, ”Near-optimal sensor placements in Gaussian processes,” in Proc. Int. Conf. Machine Learning,Bonn, Germany, Aug 2005, pp. 265-272.
[4] S. Seo, M. Wallat, T. Graepel, and K. Obermayer, ”Gaussian process regression: active data selection and test point rejection,” in Proc. Int. Joint Conf. Neural Networks, pp. 241-246, 2000.
[5] Yang Yang, Student Member, IEEE, and Rick S. Blum, Fellow, IEEE, ”Sensor Placement in Gaussian Random Field Via Discrete Simulation Optimization,” IEEE Signal Processing Letters, vol. 15, 2008.
[6] Eric J. Msechu, Member, IEEE, and Georgios B. Giannakis, Fellow, IEEE, ”Sensor-Centric Data Reduction for Estimation With WSNs via Censoring and Quantization,” IEEE Transactions on Signal Processing, vol. 60, no. 1, January 2012.
[7] D. P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods. Belmont,MA: Athena Scientific, 1996.
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