近幾年的網路通訊相關研究中，無線感測網路(Wireless Sensor Networks)已經成為熱門的研究領域。透過佈建大量的具有低成本、低耗電、小體積且具有通訊能力之感測節點，將感測環境中的各項物理量並轉化為數位資料，達到多方面的應用，無線感測器目前已經被應用在智慧家庭、環境偵測、戰場監控上。近年來由於微機電技術的進度，感測器種類越來越多樣化，從傳統只能感測溫度的感測器進化到感測數位影像的攝影感測器。在無線感測網路中，網路覆蓋的問題一直是被廣泛討論的問題，在過去的研究中，網路覆蓋問題被分為數種不同的類型進行討論，包涵規劃感測器覆蓋感興趣之區域，以及規劃感測器覆蓋某些特定感興趣之目標。不同於上述兩種類型，本論文討論之無線感測網路覆蓋為邊界覆蓋，邊界覆蓋問題主要在於在偵測的區域中以感測器的感測範圍建立一條虛擬之防衛線，當欲偵測之物體橫越感測區域時，會被至少一個感測器所偵測到。本論文主要在解決如何在視覺感測器組成之視覺感測網路中討論邊界覆蓋之問題。
   由於在視覺感測器的偵測範圍是有角度限制，不同於傳統傳統之感測器感測範圍是全向性，此一特性造成過去邊界覆蓋相關研究都不適合運用在視覺網路中。雖然視覺感測器的偵測範圍是有限制的，但其可以360度自由旋轉，與鄰居協同合作形成邊界防衛線。在本論文中提出兩個方法在視覺感測網路建立邊界覆蓋之演算法分別為Cone-based Barrier Coverage Algorithm以及Cellular-based Barrier Coverage Algorithm。在Cone-based Barrier Coverage Algorithm中分析了各種與鄰居間形成邊界覆蓋的情形，並將其歸納整理為三種類型。透過位於感測區域週邊的Sink發起找尋找封包，每個視覺感測器將尋找與之能互相合作之視覺感測器，最後由另一端的Sink集中運算找出最符合效益的視覺感測器組合形成邊界覆蓋。在Cone-based Barrier Coverage Algorithm中需要集中式的運算以及大量的封包傳遞，對於電量有限制的感測網路是不合乎成本的。因此Cellular-based Barrier Coverage Algorithm目的在改善此一缺點，提出一個全分散式的方法，在Cellular-based Barrier Coverage Algorithm中將感測區域切分成許多虛擬格，每一個視覺感測器尋找周遭的視覺感測器，與之形成在虛擬格中之邊界覆蓋。最後透過所提出之演算法，將虛擬格連結成完整之邊界覆蓋。
    針對所提出的邊界覆蓋演算法，本論文中亦進行了一系列的實驗，證明所提出之演算法效能，也將兩個演算法彼此比較，發現Cone-based Barrier Coverage Algorithm由於採用集中式方式建立邊界覆蓋，其成本較少，但其大量的計算與封包傳遞造成其能源效能不佳，進而使得網路的壽命降低。而Cellular-based Barrier Coverage Algorithm雖然能源使用效率較佳，但其建立成本遠較於Cone-based Barrier Coverage Algorithm高。在未來，本論文繼續延伸討論在網路中每一個視覺感測器皆有不同的屬性，像是每一個視覺感測器的感測範圍不同的狀況下如何建立邊界覆蓋，以及引入機率感測模型進入視覺網路中，使得所進行之研究更為貼近現實之生活。

A wireless sensor network (WSN) consists of numbers of sensors deployed in sensing field in an ad hoc or prearranged fashion for the purposes of sensing, monitoring, or tracking environmental events. Barrier coverage is one of the most important issues for various sensor network applications, e.g., national border control, critical resource protection, security surveillance and intruder detection, etc. The WSN which is composed camera sensor is named wireless camera sensor networks (WCSNs). The ordinary barrier coverage construction algorithm cannot apply to WCSNs.  Therefore, in this dissertation, we propose two distributed algorithms which are named cone-based barrier coverage algorithm and cellular-based barrier coverage algorithm.
CoBRA (Cone-based Barrier coveRage Algorithm) achieves barrier coverage in WCSNs. To the best understanding, CoBRA is the first algorithm which tries to deal with the barrier coverage issue in WCSNs. Based on some observations, the basic concept of CoBRA is that each camera sensor can determine the local possible barrier lines according to the geographical relations with their neighbors. A sink in a WCSN initiates Barrier Request (BREQ) messages to form the possible barrier lines. Afterward, a barrier line is constructed by the Barrier Reply (BREQ) message which is initiated by another sink. The barrier coverage is achieved by finding the barrier line in the monitoring area. CoBRA constructs the barrier coverage with minimum number of camera sensors. The rotation angle of a camera sensor is determined. Most important of all, it is a full distributed barrier coverage construction algorithm with only one hop information.
CoBRA uses flooding technique to construct barrier coverage information and border node makes the decision to form the barrier coverage. With the number of control packets increasing, the energy consumption is increasing. It leads network lifetime reducing. Another distributed algorithm, named Cellular-Based Barrier Coverage Algorithm, is proposed to construct the barrier coverage in the wireless camera sensor networks in order to reduce the control message overhead. The divide-and-conquer approach is adapted to deal the barrier construction problem in wireless camera sensor networks. Similar with the CoBRA, cellular-based barrier construction algorithm is full distributed ith only one hop information. 
Overall, the issue involved in the dissertation is really essential and important in wireless camera sensor networks. Experiment results show that CoBRA can efficiently achieve barrier coverage in WCSNs. Comparing to the ideal results, CoBRA can use fewer nodes to accomplish barrier coverage in random deployment scenarios. The simulation result of the cellular-based algorithm indicates the control message overhead is much less than CoBRA. The proposed cellular-based algorithm also can prolong network lifetime.

Content
Chapter 1 Introduction 1
1.1 Research Overview and Contributions 2
1.2 Introduction to WSNs 3
1.3 Introduction to Wireless Camera Sensor Networks 4
1.4 Barrier Coverage in WCSNs 5
1.5 Organization of the Dissertation 7
Chapter 2 Background 8
2.1 Coverage Problem in WSNs 8
2.2 Barrier Coverage Problem 10
Chapter 3 Cone-based Barrier Coverage Algorithm 14
3.1 Introduction 15
3.2 Preliminary 17
3.2.1 Notations and Definitions 17
3.2.2 Assumption and Network Model 21
3.3 Cone-base Barrier coveRage Algorithm (CoBRA) 21
3.3.1 Observations of WCSNs 21
3.3.2 The proposed algorithm  24
3.3.2.1 Initial Phase 25
3.3.2.2 Candidate Selection Phase 25
3.3.2.3 Decision Step 28
3.3.3 Divide-and-conquer 29
3.4 Performance Evaluation 29
3.5 Summary 32
Chapter 4 Cellular-based Barrier Coverage Algorithm 34
4.1 Introduction 34
4.2 Preliminary 38
4.3 Cellular-based Barrier Coverage Algorithm 40
4.3.1 Virtual Cell Construction 41
4.3.2 Cellular-based Barrier Coverage 45
4.3.2.1 Cooperative Camera Selection in Large Virtual Cell 47
4.3.2.2 Cooperative Camera Selection in Small Virtual Cell 50
4.3.3 Cellular-based Barrier Coverage Construction Algorithm 51
4.4 Performance Evaluation 53
4.5 Summary 57
Chapter 5 Conclusions and Future Works 68
5.1 Contributions 68
5.2 Future Work 70
Bibliography 72
Publication List 84

List of Figures
Figure 1.1 Traditional barrier coverage problem in WSNs is not suitable in WCNS.  6
Figure 1.2 Barrier coverage construction in WCSN. 7
Figure 2.1 The concept of CoBRA[73]. 11
Figure 3.1 Traditional barrier coverage problem in WSNs is not suitable in WCNS. 16
Figure 3.2 The sensing model of a camera sensor. 18
Figure 3.3 (a) Sensor i covers sensor j with its sensing area and forms a sensing connect between sensor i and j. (b) Sensor i and j cannot cover each other. But the sensing area of sensor i and j are interconnect. Sensor i and j are sensing connected. 19
Figure 3.4 The network model of CoBRA. 20
Figure 3.5 The definition of SR zone and LR zone. 22
Figure 3.6 Three different types of barrier lines. (a)Type SS. (b)Type SL. (c)Type LL. 23
Figure 3.7 Barrier line of type SS can be formed by using different camera sensors. (a)Barrier line formed by a and i. (b)Barrier line formed by i and b. 24
Figure 3.8 An example of camera sensor and its neighbors.(a) Camera sensor i with 4 neighbors. (b) The stored information of camera sensor i. 26
Figure 3.9 Boundary sensors forms a barrier line with virtual sensors (a)Type SS. (b)Type SL. 28
Figure 3.10 Successful probability of finding a barrier line under different number of camera sensors. 30
Figure 3.11 Successful probability of finding a barrier line under different width of monitoring area. 31
Figure 3.12 Successful probability of finding a barrier line under different field of view. 32
Figure 3.13 Number of barrier lines can be found under different approaches. 33
Figure 4.1 Traditional barrier construction algorithms cannot apply to wireless camera sensor networks. 36
Figure 4.2 Barrier coverage construction in WCSN. 37
Figure 4.3 The attribute of a camera sensor. 38
Figure 4.4 The examples of the sensing connected. 39
Figure 4.5 The attribute of a camera sensor. 40
Figure 4.6 Triangular virtual cell construction. 42
Figure 4.7 Quadrilateral virtual cell construction. 42
Figure 4.8 Quadrilateral virtual cell construction. 43
Figure 4.9 The monitor area is divided into several Hexagonal-based virtual cells. 44
Figure 4.10 The monitor area is divided into several small virtual cells. 45
Figure 4.11 The concept of proposed barrier coverage construction algorithm. 46
Figure 4.12 A virtual cell is barrier covered. 47
Figure 4.13 (a)SIL-S type cooperative candidate camera sensor. (b)SOL-S type cooperative candidate camera sensor. 48
Figure 4.14 (a)SIL-D type cooperative candidate camera sensor. (b)SOL-D type cooperative candidate camera sensor. 58
Figure 4.15 (a)ISS type cooperative candidate camera sensor. (b)OLS type cooperative candidate camera sensor. (c)OBS type cooperative candidate camera sensor. (d)ORS type cooperative candidate camera sensor (E)OFS type cooperative candidate camera sensor. 59
Figure 4.16 The successful ratio of the barrier construction on different sensing ranges. 60
Figure 4.17 The successful ratio of the barrier construction on different scenario widths. 61
Figure 4.18 Average number of sensors to construct barrier coverage when number of sensors is different. 62
Figure 4.19 Average number of the sensors to construct the barrier coverage when width of scene is different. 63
Figure 4.20 The number of control message overhead to construct the barrier coverage when the number of the camera sensors is different. 64
Figure 4.21 The number of control message overhead to construct the barrier coverage when the width is different. 65
Figure 4.22 The number of transmissions on a camera sensor. 66
Figure 4.23 The network life time when number of sensor in the scene is different. 67

List of Tables
Table 2.1 Summary of related work on barrier coverage. 12
Table 3.1 The format of BREQ. 27
Table 3.2 The format of BREP. 29
Table 3.3 Simulation parameters. 30
Table 4.1 Simulation Parameters. 54
Table 4.2 The ratio between large virtual cells and small virtual cells on a barrier construction when the number of sensors is different. 56
Table 4.3 The ratio between large virtual cells and small virtual cells on a barrier construction when the width of the scene is different. 56

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