無線感測網路具有低頻寬、有限電量、大量分散佈點等特性。在無線感測網路中系統的生命週期受限於有線的能量，因此有效率的傳遞資料並減少能量的消耗在無線感測網路的研究上是一個重要的議題。
本篇論文中，我們提出一個引導式資料聚合機制來解決有效率傳遞資料的問題。我們利用適當的安排網路節點的加權值,使無線感測網路能自動建立有效率的資料傳輸路徑。在無線感測網路中，同一事件經常被數個感測節點探知，為了減少重複性資料的傳輸，我們提出虛擬網格的技術來聚合重複性的資料以減少冗餘資料的傳輸。實驗數據顯示我們的引導式資料聚合機制能有效率地傳遞資料並減少能量的消耗。

Wireless Sensor Networks (WSNs) are characterized by low bandwidth, limited energy, and largely distributed deployment. The lifetime of system depend on sensor nodes'' limited energy in the WSNs. Thus, efficiently routing data to reduce energy consumption is a critical issue in the WSNs.
In this thesis, we proposed a guided data aggregation protocol to resolve the problem of efficiently routing data. We use the method of giving each sensor node a appropriate value of node weight, WSNs could build a efficiently routing path automatically. In the WSNs, an event was detected by several sensor nodes. For reducing the transmission of duplicate data, we propose the virtual grid for aggregate duplicate data to reducing the transmission of redundant data. The simulation results show that our proposed guided data aggregation protocol could routing data efficiently and reducing the energy consumption.

摘 要 i
Guided Data Aggregation in Wireless Sensor Networks ii
ABSTRACT ii
List of Tables vii
Chapter 1 Introduction 1
1.1 Wireless sensor networks 1
1.2 Sparse Data Collection 2
1.3 Motivation 3
Chapter 2 Related Work 5
2.1 Existing Data Aggregation Techniques 5
2.2 Sparse Data Aggregation 6
Chapter 3 Preliminary and Overview 9
3.1 Forming Aggregation Tree 9
3.2 Aggregation 11
3.3 Flow Chart 12
Chapter 4 Guided Data Aggregation Protocol 13
4.1 Forming the Virtual Grid 13
4.2 Guidelines and Distance-Sensitive Weight System 14
4.3 Double Rulings 19
4.4 Explorer Protocol and Terminal Protocol 20
4.5 Forming an Aggregation Tree 25
4.6 Aggregation Data 25
Chapter 5 Algorithm Analysis 27
5.1 Algorithm analysis 27
Chapter 6 Experimental Results 29
6.1 The Simulation Model 29
6.1.1 The Impact of Positions of the Multi-sinks 29
6.1.2 The Impact of Number of Sinks 33
6.1.3 The Impact of the Rate of Event Occurrence 34
6.2 Performance Analysis 37
Chapter 7 Conclusions and Future Work 44
References 45

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