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姓名	蔡宗益(Tsung-Yi Tsai)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	大範圍無線感測網路下分散式資料壓縮收集演算法 (Distributed Compressive Data Aggregation in Large-Scale Wireless Sensor Networks)
相關論文	★  Dynamic Overlay Construction for Mobile Target Detection in Wireless Sensor Networks ★ 車輛導航的簡易繞路策略 ★ 使用傳送端電壓改善定位 ★ 利用車輛分類建構車載網路上的虛擬骨幹 ★ Why Topology-based Broadcast Algorithms Do Not Work Well in Heterogeneous Wireless Networks? ★ 針對移動性目標物的有效率無線感測網路 ★ 適用於無線隨意網路中以關節點為基礎的分散式拓樸控制方法 ★ A Review of Existing Web Frameworks ★ 將感測網路切割成貪婪區塊的分散式演算法 ★ 無線網路上Range-free的距離測量 ★ Inferring Floor Plan from Trajectories ★ An Indoor Collaborative Pedestrian Dead Reckoning System ★ Dynamic Content Adjustment In Mobile Ad Hoc Networks ★ 以影像為基礎的定位系統 ★ 車用WiFi網路中的碰撞分析 ★ Exploring Spatial-Temporal Cloaking Against Correlation Attacks for Location-based Services
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摘要(中)	最近幾年，壓縮式取樣技術(compressive sampling theory)正被廣泛地使用在 無線感測網路的資料收集應用上。藉由結合壓縮式取樣技術和路由路徑(routing path)，有些研究提供了集中式的演算法來最小化整體網路的資料傳輸。然而這些 演算法通常需要完整的網路拓樸資訊和複雜的運算來取得最佳解。因此，當網路 的拓樸變動時，這些集中式演算法往往需要耗費許多的傳輸來重建整個路由路 徑。在這篇論文，我們提出了第一個分散式演算法來解決這個問題。我們首先介 紹兩個分散式路由路徑建構演算法來算建立路由路徑。接著，我們發表一個最小 化區域資料傳輸演算法來減少整體網路的資料傳輸量。模擬結果顯示出我們的演 算法所耗費的建置成本遠低於集中式演算法。
摘要(英)	As compressive sampling theory has been extensively used for data aggregation in wireless sensor network, some researches provide a centralize protocol that can minimize the data traffic in the network through the combination of routing and compressive sampling. However, these protocols require the entire network topology information to compute the optimal solution. As a result, when the network environment is not stable, these protocols incur too much overhead. In this thesis, we investigate the decentralized scheme that can efficiently construct the routing path for compressive data aggregation. We first propose two distributed algorithms, namely MRT and MAT, to construct the routing path for compressive data aggregation. After works, an adjustment algorithm is proposed to locally redirect the data flow and further minimize the data traffic. The simulation results indicated that the construction overhead of our approaches is much lower than the centralize protocol.
關鍵字(中)	★ 資料收集 ★ 無線感測網路	關鍵字(英)	★ Compressive Data Aggregation ★ Wireless Sensor Networks
論文目次	1 Introduction 1 2 Literature Review 3 2.1 CS Technique Improvement: . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 CS-based Aggregation Path Optimization: . . . . . . . . . . . . . . . 4 2.3 CS Application in WSNs: . . . . . . . . . . . . . . . . . . . . . . . . 4 3 Preliminary 5 3.1 Compressive Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Hybrid Compressive Data Gathering . . . . . . . . . . . . . . . . . . 6 3.3 Medial Axis of WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4 Main Idea 10 4.1 Minimum Relay Tree . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Medial Axis based Compressive Data Aggregation Tree . . . . . . . 13 4.3 Local Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5 Performance Analysis 16 5.1 Global Data Traffic Comparison . . . . . . . . . . . . . . . . . . . . 18 5.2 Overhead Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.3 Efficiency of Different Network Density . . . . . . . . . . . . . . . . 20 5.4 Efficiency of Different Compression Factor M . . . . . . . . . . . . . 23 6 Conclusion and Future Work 26 7 Reference 27
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指導教授	孫敏德(Ming-Te Sun)	審核日期	2012-7-17
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