無線感測網路是利用體積小及成本低廉的感測節點所建立的網路，可應用於各種領域的環境中。一般來說，因需要大量撒佈感測節點於監測的環境中，相對的平均生產成本不能太高。又因感測節點的體積小，其擁有的資源與能量是有限的，且感測節點經常被撒佈在一些人類不容易到達的環境中，因此不容易進行能量補充的動作。所以在無線感測網路中，要如何有效的節省能量消耗，以延長整體網路存活時間(Network lifetime)，是一個相當重要的研究議題。
資料聚集(Data aggregation)在無線感測網路中是很重要的研究議題，網路中的感測節點會以多點跳躍的方式，週期性將所感測到的資料回傳給資料收集端(Sink node)。而目前大部份的論文，都只探討如何建立一棵負載平衡的擴張樹(Load Balance Spanning Tree, LBT)，於同質性(Homogeneous)感測網路中，其為撒佈功能性相同節點的網路環境，但其實撒佈功能性不盡相同節點的異質性(Heterogeneous)感測網路，較符合真實世界的應用，因為在較大型的感測任務中，需要同時監控較多的感測變數。
本論文主要是針對在異質性感測網路環境，且在各感測節點(Sensor nodes)電量不盡相同的情形下，建構一棵能量平衡的擴張樹(Energy Balance Spanning Tree, EBT)，以能應用於資料聚集為主要的研究議題。其方法主要是根據各感測節點之剩餘能量不同，而分配不同的負載量(Load)，避免將負載量加諸於同一節點上，讓較沒電的節點負擔較小的負載量，使得每條分支(Branch)的存活時間相等，藉此來延長整體網路的存活時間。

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第一章 導論
1.1 前言...............................................................................1
1.2 研究動機.......................................................................4
1.3 論文架構.......................................................................6
第二章 相關研究
2.1 在同質性感測網路中，資料聚集樹的環境假設.......8
2.2 廣度優先搜尋樹演算法...............................................9
2.3 負載平衡樹演算法.......................................................12
第三章 能量平衡樹演算法
3.1 異質性感測網路中，資料聚集樹的環境假設...........22
3.2 EBT演算法...................................................................23
3.2.1 EBT建構演算法........................................................23
3.2.2 EBT調整演算法........................................................34
第四章 模擬結果與討論
4.1 模擬環境與參數...........................................................38
4.2 於同質性感測網路中之模擬結果...............................40
4.3 於異質性感測網路中之模擬結果...............................44
第五章 結論.........................................................................49

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