科技日新月異，近年來無線網路的蓬勃發展，延伸出許多不同型態的無線網路類型，其中無線感測網路(Wireless Sensor Networks, WSNs)更是一個熱門的研究議題。無線感測網路是由一群分散式的感測節點所組成，每一個感測器皆具有運算、感測之功能，可以針對不同的應用去感測不同的環境，並將這些感測到的資料回傳到資料收集中心。然而在傳輸過程中如何有效的將大量的資料作聚集的處理，降低感測節點電力上的消耗，是很重要的研究之一。
    本論文利用的是取重心的方式，先建出一條樹狀的路徑，再做資料聚集，此機制還可以解決熱點（Hot spot）問題，達到整體網路電量負載平衡，改善整體網路時間，延長網路壽命。

The advancement of technology changes with each day. In recent years, it extends many different types of wireless network. Wireless sensor networks become a hot issue in research. Wireless sensor networks are composed of multiple sensor nodes that are randomly deployed. Each sensor node has sensing and computing capability to sense different environments according to different applications and transmit these data to sink. However, how to reduce energy consumption in the transmission process is important research.
    In this thesis, we propose a Tree-based Data Aggregation Mechanism using Center of Gravity (TDCoG). Initially, it builds transmission paths of tree structure, and then does data aggregation. This mechanism may solve the hot spot problem and achieve the effect that loading balancing of energy and extending the lifetime of the network.

第一章  緒論	1
    1-1 研究背景	3
    1-2 研究動機	6
    1-3 研究目的	7
    1-4 論文架構	8
第二章  相關研究與背景介紹	 9
    2-1 電量損耗因素介紹	10
    2-2 資料聚集方法	12
      2-2-1 機率式資料聚集方法	13
      2-2-2 群集式資料聚集方法	15
      2-2-3 樹狀式資料聚集方法	19
第三章  運用重心進行樹狀資料聚集演算法	26
    3-1 網路環境設定與假設	29
    3-2 資料聚集階段（Data Aggregation Phase）	31
    3-3 網路維護階段（Network Maintenance Phase）	43
      3-3-1 電量維護（Energy Maintenance Scheme）	45
      3-3-2 產生新來源節點（New Source Node Generation Scheme）	49
第四章  模擬比較與分析	53
    4-1 模擬環境	54
    4-2 模擬結果比較與分析	56
第五章  結論與未來研究方向	61
    5-1 結論	61
    5-2 未來研究方向	62
參考文獻	63
附錄-中文論文 68
附錄-英文論文 76
 
圖目錄
圖 1.1  無線感測網路運作示意圖	 2
圖 2.1  Directed Diffusion運作示意圖	13
圖 2.2  LEACH網路環境架構圖	16
圖 2.3  LEACH-C建構階段流程圖	18
圖 2.4  E-Span樹狀架構圖	19
圖 2.5  LPT樹狀架構圖	21
圖 2.6  EE-Span樹狀架構圖	23
圖 3.1  樹狀資料聚集演算法流程圖	27
圖 3.2  感測器節點佈置環境示意圖	29
圖 3.3  運用重心進行樹狀資料聚集機制流程圖	33
圖 3.4  設定距離Sink最遠的Source node為X	34
圖 3.5  設定距離X最近的Source node為Y	35
圖 3.6  參考點演算法流程圖	36
圖 3.7  利用X,Y,Sink建構出的虛擬三角形取重心	37
圖 3.8  距離G最近的感測器設定為節點I	38
圖 3.9  判斷DSI < DSY	39
圖 3.10  判斷HI < HXY	40
圖 3.11  尋找出I和它鄰近的感測器節點並儲存	41
圖 3.12  電量維護機制（聚集節點）流程圖	45
圖 3.13  挑選備用節點範例圖I	46
圖 3.14  挑選備用節點範例圖II	47
圖 3.15  電量維護機制（資料收集中心）流程圖	48
圖 3.16  設定距離最靠近新來源節點的節點為P	49
圖 3.17  移除P的聚集節點和它相關的聚集節點	50
圖 3.18  聚集節點為空的節點	51
圖 3.19  產生新來源節點機制流程圖	52
圖 4.1  計算優先權公式之各項參數值比較圖	56
圖 4.2  平均代傳跳躍次數比較圖	57
圖 4.3  能量損耗比較圖	58
圖 4.4  節點存活數量比較圖	59

表目錄
表 3.1　Primary Table	31
表 3.2　Temporal Table	32
表 3.3　Candidate Table	32
表 4.1　環境參數設定	54

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