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研究生： 楊源誠
Yuan-Cheng Yang
論文名稱： 無線感測資訊儲存系統上的資料快取技術
Data Caching Techniques for Information Storage on WSNs
指導教授： 王家祥
Jia-Shung Wang
口試委員:
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年： 2008
畢業學年度： 96
語文別： 英文
論文頁數： 85
中文關鍵詞： 資料快取 、資訊儲存 、無線感測網路 、階層式快取系統 、快取節點 、線性預測參數
外文關鍵詞： data caching, information storage, sensor networks, hierarchical caching system, cache node, forecasting parameters
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    • 無線感測網路是在廣泛的區域內散佈大量的無線感測器裝置以達到監控環境的效果，而這些無線感測器通常是由電池供應有限的電量，並配置受侷限的儲存記憶體空間與運算處理器。在這樣的情形下，要如何有效地利用無線感測器的有限的記憶體空間來儲存感測器所收集到的歷史資料，並快速地提供使用者提出查詢的統計量值，逐漸地成為以儲存資料為導向的無線感測網路範疇內一個相當重要的議題。
    在這篇論文裡，我們提出一種可以應用在資訊儲存系統上的資料快取技術，利用感測器取得資料的線性趨勢來對使用者查詢做快速的近似統計量值回報。我們利用階層式儲存架構的特質，試圖在資料傳輸與查詢封包的處理成本間取得平衡，並在各個階層提供使用者不同精確度的資料。在我們所實作的資料快取系統裡，我們將整個無線感測網路的儲存系統劃分為三個階層，各自由許多的無線感測器和快取節點所組成。透過線性資料預測的技術，我們可以讓快取節點藉由目前資料的趨勢來預測下一個時間點可能的資料數值，並且同時減少無線感測器傳送資料給快取節點所造成的龐大傳輸代價，另外，快取節點在記憶體空間裡所存放的各時間區段的線性趨勢與線性模型參數也可以在使用者提出查詢時產生立即、近似的統計結果，以減少使用者查詢所需要處理的時間以及在無線感測網路內傳送所需要耗費的能量。
    在實驗中，我們驗證了利用資料的線性趨勢可以成為在無線感測網路的儲存系統上被使用的一種快取技術，我們也可以發現在此儲存系統內運用不同的線性資料預測技術時，都會對此儲存系統架構內的資料準確度造成影響，並且改變傳輸成本的節省比例，另外，我們也比較了採用不同的線段合併方式對此儲存系統架構內的資料精確度會造成什麼樣的影響與結果。

    中文摘要 I Abstract II 致謝辭 IV Table of Contents V List of Figures VII List of Tables VIII Chapter 1. Introduction 1 Chapter 2. Related Work 8 2.1 Distributed Data Storage 8 2.2 Sensor Network as a Database 11 2.2.1 The Cougar Approach 12 2.2.2 The TinyDB System 13 2.2.3 The BBQ System 13 2.3 Data Compression and Linear Representation 13 2.3.1 Data Compression Techniques 14 2.3.2 Piece-wise Linear Representation 16 Chapter 3. The Proposed Caching Technique 18 3.1 System Architecture 19 3.1.1 Overview of the Architecture 19 3.1.2 Simultaneous Data Processing 21 3.1.3 The Storage Structures 22 3.2 Time-series Data Forecasting 23 3.2.1 Overview of Linear Regression 24 3.2.2 Least Square Error Linear Fit 24 3.2.3 Non-seasonal Holt-Winters Linear Forecast 25 3.2.4 Double Exponential Smoothing Linear Forecast 26 3.2.5 Directly Smoothed Slope based Linear Forecast 27 3.2.6 The Quality Metric 28 3.3 Cache Management Policy 29 3.3.1 Importance of Data Summary 31 3.3.2 Merge Distortion 32 3.3.3 The Replacement Strategy 33 3.4 Local Buffer Management Policy 39 3.4.1 Importance of Data 40 3.4.2 The Replacement Strategy 41 Chapter 4. The Framework of Query Processing 42 4.1 Drill-down Querying 42 4.1.1 Querying the Cache Level 44 4.1.2 Querying the Sensor Level 46 4.2 Query Workload Model 47 4.2.1 Periodic Arrival Process 48 4.2.2 Random Arrival Process 48 4.2.3 Historical Query Arrival Process 49 Chapter 5. Experimental Results 51 5.1 Testing Data Sets and Experimental Setup 51 5.2 Evaluation Metrics 53 5.2.1 Metric for Transmission Overhead 53 5.2.2 Metric for Query Performance 53 5.3 Performance Evaluation 55 5.3.1 Reduced Transmission Overhead 55 5.3.2 Query Performance 57 5.3.3 Hierarchical Data Quality 65 5.3.4 Computational Complexity 73 5.3.5 Program Execution Time 75 Chapter 6. Conclusion 78 References 80

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