邊界覆蓋(Barrier Coverage)在無線感測網路是一個重要且受到廣泛討論的議題，其目的是利用感測器的感測範圍來監控國邊界或是使用者設定的闖越線(如動物園或危險區的闖越線)。在以往的研究中，大多假設佈撒在監控區域中之感測器，其感測範圍為二元感測模型(Binary Sensing Model)，然而，在實際環境中，仍以機率式感測模型較接近真實的監控能力。本論文擬在行動式感測網路中，考慮感測器之感測模型為一機率式感測模型(Probability Sensing Model)，以行動感測器之移動性，提出一行動感測器位置調整技術，在滿足使用者預先設定的網路生命期需求之下，利用有限的電量於行動感測器移動消耗上，進而最大化場景之監控品質。實驗顯示，本論文所提出之行動感測器位置調整技術，在兼顧網路生命期之需求下，能有效的提升監控場景之監控品質。

The barrier coverage problem has been wildly discussed in mobile wireless sensor networks. Most of previous studies assume that the sensing range of deployed sensors is based on Binary Sensing Model. However, in reality, the sensing range of sensor is based on Probability Sensing Model. By considering that the sensor is movable and its sensing range is probability sensing model, this paper proposed a relocation mechanism which aims to improve detection probability of barrier coverage in Mobile WSNs. Besides, the given monitored region also satisfies the predefined requirement of network lifetime. Compared with related works, experimental studies reveal that the proposed relocation mechanism can effectively improve the detection probability of barrier coverage while satisfies the predefined requirement of network lifetime.

目錄
目錄	III
圖目錄	V
表目錄	VII
第一章、簡介	1
第二章、相關研究	5
第三章、網路環境與問題描述	9
3.1 感測模型	9
3.2 環境假設	10
3.3 問題描述	14
第四章、行動感測器位置調整技術	17
4.1 網路初始化	17
4.2 評估監控品質	20
4.3 移動策略	25
第五章、模擬實驗	31
5.1 模擬環境	31
5.2 模擬結果	32
第六章、結論	47
參考文獻	48
附錄 英文論文	51

圖目錄
圖(1)：Elfes感測模型	10
圖(2)：入侵路線	12
圖(3)：入侵路線被感測機率	13
圖(4)：最低共同感測機率	19
圖(5)：為一網路模型轉換工作之例子	20
圖(6)：Ford–Fulkerson演算法	22
圖(7)：Ford–Fulkerson-Algorithm之例子	24
圖(8)：最佳位置計算工作之例子	29
圖(9)：最佳節點挑選例子	30
圖(10)：佈建場景示意圖	33
圖(11)：監控場景之感測機率	34
圖(12)：執行ARMIDP後之場景示意圖	36
圖(13)：執行ARMIDP後監控場景之感測機率	36
圖(14)：行動感測器剩餘電量不均示意圖	37
圖(15)：監控品質比較圖	39
圖(16)：行動感測器剩餘電量比較圖	40
圖(17)：監控品質與耗費時間比較圖	41
圖(18)：行動感測器佈建狀態	42
圖(19)：行動感測器剩餘電量差異比較圖	44
圖(20)：行動感測器平均移動距離比較圖	45
圖(21)：監控場景之監控品質比較圖	46

表目錄
表格(一)：相關論文比較	8
表格(二)：符號表	11
表格(三)：相關模擬參數	31

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