無線視覺感測網路(Wireless Visual Sensor Networks，WVSNs)主要由視覺感測節點(Camera Node)所構成，如何以最少的視覺感測節點來共同分擔視覺感測的工作，在近年來已漸漸被研究學者所討論，其共同的目標均在於選擇較少的Camera Node達到目標監控的目的。然而，由於大部份的研究均將研究重心放在Node的選擇，並無考慮鏡頭轉動，這使得選出的Camera Node可能數量過多。本論文針對無線視覺感測網路，將鏡頭轉動納入考量，在給定的轉動延遲時限內，衡量鏡頭轉動對覆蓋所產生的效益，並在轉動效益最大的考量下，嘗試以分散式的方法找出較少的Camera Node來擔負監控工作，並可完整地監控目標區域。實驗結果顯示，本論文所提出之演算法能夠開發鏡頭轉動的效益，減少參與工作的視覺感測節點數量，並有效地監控目標區域。

A Wireless Visual Sensor Networks (WVSNs) is consists of wireless visual sensor nodes called Camera Nodes. In WVSNs, all Camera Nodes are equipped with cameras and responsible for capturing the images of the target. Therefore, coverage problem is one of the most critical issues in WVSNs and has drawn much attention in recent years. The main purpose of coverage issue is to select minimal number of Camera Nodes for full cover the target. The key difference between the WVSNs and traditional WSN is that each Camera Node can rotate its camera and adjust its focus to change the monitoring area. However, most of the previous research doesn’t consider the camera rotation, and thus, always selects too much Camera Node for full cover the object. In this paper, we take the effect of camera rotation and rotation time into consideration. In addition, aims to develop a distributed camera rotation and Camera Nodes selecting mechanism called Decentralized Visual Coverage Algorithm (DVCA) to pick smaller number of Camera Nodes for monitoring the target cooperatively by appropriate camera rotation. The experimental simulation results show that our proposed algorithm can effectively reduce competition and packet collisions, and decrease the packet delay time from camera nodes to sink node.

List of figures	V
List of tables	VI

壹、	簡介	1
貳、	相關研究	4
參、	網路環境及問題描述	6
3.1	網路場景及假設	6
3.2	問題描述	7
肆、	Decentralized Visual Coverage Algorithm(DVCA)	13
4.1	Network Initialization	13
4.2	建立group	19
4.2.1	Minimum Rotation Angle Method	21
4.2.2	Greedy Bisection Method	22
4.3	挑選負責監控的Camera node	26
伍、	Performance Evaluation	31
陸、	結論	37
Reference	         38
附錄—英文論文	39

List of figures
圖(1)網路場景	7
圖(2)符號定義示意圖	8
圖(3)目標物T的邊界B為多個Si所組成	10
圖(4)Si係由多個子片段 所組成，其中N值為5	11
圖(5)Camera node鏡頭照射方位不同	14
圖(6)Camera node的位置不同	14
圖(7)計算 之長度	15
圖(8-a)Camera node mi鏡頭未轉動的貢獻範圍	17
圖(8-b)Camera node mi鏡頭轉動後的貢獻範圍	17
圖(9)header與member	20
圖(10)m5及m2的貢獻範圍必須透過轉動才能連接	20
圖(11)m5及m2的較小轉動組合	23
圖(12)m2與m3的監控範圍完全重疊	28
圖(13)FOV及佈建數量不同之情況下，比較Camera node選取數量	32
圖(14)Camera node數量為50個時，最大轉動角度的比較	33
圖(15)Camera node數量為70個時，最大轉動角度的比較	33
圖(16)FoV=20度及佈建數量不同之情況下，比轉動角度總合	34
圖(17)FoV=30度及佈建數量不同之情況下，比轉動角度總合	35
圖(18)Camera node數量為50個及FoV不同之情況下，比轉動數量	35
圖(19)Camera node數量為70個及FoV不同之情況下，比轉動數量	36

List of tables
表(一)符號定義	8
表(二)資料結構	18
表(三)一步鄰居的最大貢獻範圍資訊	19
表(四)環境參數	31

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