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研究生： 蔡佑權
You-chiuan Tsai
論文名稱： 強健式回歸分析在無線感測網路上資料清理之研究
A Robust Regression Approach for Sensor Network Data Cleansing
指導教授： 陳郁堂
Yie-Tarng Chen
口試委員: 吳乾彌
Chen-Mie Wu
林銘波
Ming-Bo Lin
方文賢
Wen-Hsien Fang
學位類別： 碩士
Master
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 40
中文關鍵詞： 無限感測網路 、數據整理 、強健式回歸
外文關鍵詞： Wirless Sensor Networks, Data Cleansing, Robust Regression
相關次數： 點閱：187  下載：3 
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    • 無線感測網路的感測節點易受到環境影響，造成在感測網路收集到錯誤資料，如果這些異常資料未能被修復，可能導致後續程序的錯誤，由於無線環境的不穩定性及感測節點有限的記憶體，更增加異常資料修復的挑戰性。當錯誤資料的比例增加，感測網路將無法正確的運作。因此，資料修復是在無線網路中是一重要的議題。在本論文中，我們提出了統計的方法作空間相關感測資料的修復。明確的說，我們的方法包含貝式(Bayesian)非監督式分類及強健式回歸分析來修復感測網路資料。經由實驗模擬，我們的方法在輸入的資料不乾淨的情況，仍能正確及有效地修復異常節點的資料。

    Wireless sensor networks suffer from resource constraints of sensors: limited battery power and memory storage. Therefore, sensors are prone to fail and provide inaccurate values or missing values. If the percentage of faulty readings grows up, wireless sensor networks cannot operate accurately. Hence, data cleansing is an important issue for wireless sensor networks. In this research, we present a statistical approach to clean spatially-correlated sensor data. Specifically, this scheme combines a Bayesian unsupervised clustering scheme and a robust regression to capture spatial correlation in sensor network data. Some experimental results of the proposed data clean scheme are also provided.

    摘要 IV Abstract V 致謝 VI List of Figure VIII List of table X Chapter 1. Introduction 1 1.1. Wireless Sensor Networks 1 1.2. Motivation 1 1.3. Problem Statement 2 1.4. Goal 2 1.5. Summary of the proposed approach 2 1.6. Contribution 3 Chapter 2. Related work 4 2.1. Robust estimation 4 Chapter 3. A Robust Data Cleansing Scheme for Sensor Network Data 6 3.1. Data classification 7 3.1.1. Optimal Partition with a given 7 3.1.2. Choice of , the number of distinct classes 8 3.2. Sensor Data Modeling 9 3.3. Robust Data Cleaning 11 3.3.1. Choice of the Outlier Boundary Function in the Robust Regression Model 11 3.3.2. Estimation of Model Parameters and Contaminated Data 13 Chapter 4. Performance Evaluation 16 4.1. Performance Metrics 19 4.2. Experiment Results 20 4.2.1. Performance Comparison of Two Outlier Boundary Functions 20 4.2.2. Comparisons of different model order 23 4.3. Discussion 36 Chapter 5. Conclusions 38 REFERENCE 39

    [1] F. Wilcoxon, “Individual Comparisons by Ranking Methods”, Biometrics Bulletin, vol. 1, no. 6, pp. 80-83, 1945.
    [2] G.R. Arce, Nonlinear Signal Processing: A Statistical Approach, Wiley, New Jersey, USA, 2005
    [3] I. Daubechies, R. DeVore, M. Fornasier, C. S. Gunturk, “Iteratively Reweighted Least Squares Minimization for Sparse Recovery”, Communications on Pure and Applied Mathematics, vol. 63, no. 1, pp. 1-38, 2010.
    [4] “Intel Lab Data”, [online] Available: http://db.csail.mit.edu/labdata/labdata.html
    [5] J. W. Tukey, Exploratory Data Analysis, Addison-Wesley, 1977.
    [6] J. Wolberg, Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments, Springer, 2005
    [7] P. J. Huber, Robust Statistics, Wiley, New York, 1981.
    [8] P. J. Rousseeuw, A.M. Leroy, Robust Regression and Outlier Detection, Wiley, 1987.
    [9] P. J. Rousseeuw, "Least Median of Squares Regression" Journal of the American Statistical Association, vol. 79, no. 388, pp. 871-880, Dec,1984
    [10] R. Chartrand and W. Yin, “Iteratively reweighted algorithms for compressive sensing”, 2008 IEEE international conference on acoustics, speech, and signal processing , pp. 3869 - 3872, April, 2008.
    [11] R. L. Kashyap, and K.B. Eom, “Robust Image Modeling Techniques with an Image Restoration Application”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 36, no. 8, pp. 1313-1325, Aug 1988.
    [12] R. L. Kashyap and S. Sista, “Bayesian Framework for Unsupervised Classification with Application to Target Tracking”, IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1745-1748, 1999.
    [13] T. S. Ferguson, “An Inconsistent Maximum Likelihood Estimate”, Journal of the American Statistical Association, vol. 77, no 380, pp. 831-834, 1982.
    [14] W. J. J. Rey, Introduction to Robust and Quasi-Robust Statistical Methods, Springer-Verlag, New York, 1983.

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