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系統識別號 U0002-1807200616104800
中文論文名稱 無線感測網路之定位與追蹤
英文論文名稱 Localization and Tracking in Wireless Sensor Networks
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 94
學期 2
出版年 95
研究生中文姓名 王勝石
研究生英文姓名 Sheng-Shih Wang
學號 690190052
學位類別 博士
語文別 英文
口試日期 2006-06-17
論文頁數 90頁
口試委員 指導教授-石貴平
委員-陳宗禧
委員-陳裕賢
委員-張志勇
委員-謝孫源
委員-王三元
委員-石貴平
中文關鍵字 無線感測網路  定位  異質型感測網路  偵測  追蹤 
英文關鍵字 Wireless Sensor Network (WSN)  Localization  Wireless Heterogeneous Sensor Network (WHSN)  Detection  Tracking 
學科別分類 學科別應用科學資訊工程
中文摘要 無線感測網路(Wireless Sensor Network)是由數量眾多且微小的無線感測器(Sensor)所構成,這些感測器均符合體積小、低成本、低耗電及傳輸距離短的特性。近幾年來,無線感測網路已經廣泛應用在許多領域,許多研究也已針對該網路的相關議題進行探討,其中感測器位置是一項重要的因子,也就是說,必須得知感測器的位置才能設計符合實際需求的方法。此外,無線感測網路的應用中,偵測和追蹤是兩項重要的應用,特別是使用者在意的緊急事件。在本論文中,我們將先說明上述研究議題的重要性與面臨的挑戰,並提出有效率的解決方案完成感測器的定址,及偵測並追蹤事件。
無線感測網路中的許多應用必須靠感測器的精確位置標示事件發生的區域。然而,一些實際上的應用並不需要感測器的精確位置,因此本論文將提出一個以方向為主的定址方法(稱為DLS),感測器可以DLS方法得到相對於匯集點(Sink)的相對方向。基本上,DLS是以空間區域性(Spatial Locality Property)基礎,並透過我們設計的錨點佈設策略提高錨點附近感測器的定址正確性,藉此感測器可以根據接收到的封包以決定自己的方向。再者,我們還設計虛擬雙方向座標系統(Virtual Dual Direction Coordinate System, VDDC system)以改善靠近兩相鄰方向邊界附近的感測器之定址正確率。
以無線網路進行偵測與追蹤的方法中,所有的感測器均配備相同的感測元件,但迄今尚未有研究針對利用具備不同感測元件的感測器進行事件追蹤與偵測之可行性進行探討。此外,在一些日常的應用中,事件的偵測與追蹤可能必須透過不同種類的感測器方能完成,因此,本論文將提出一個分散式的事件追蹤與偵測方法(CollECT),該方法主要是應用在無線異質型感測網路(Wireless Heterogeneous Sensor Network, WHSN),即網路中所有的感測器並不相同。CollECT的核心概念是相同種類的感測器建立三角形結構(Triangulation),此外,CollECT亦考慮不同種類感測器的協同合作概念以決定事件是否發生。再者,CollECT亦選擇一些感測器代表事件發生區域的邊界。
整體而言,本論文中不僅提出了一個方向為主的定位演算法,還設計了一個應用於無線異質型感測網路之事件偵測與追蹤機制。我們的實驗結果顯示DLS的平均定位正確率可達94%,而模擬結果不但驗證了DLS的可行性與實用性。此外,在事件的偵測與追蹤方面,透過CollECT,平均約有92%位於事件發生區域內的感測器可被正確識別。
英文摘要 The wireless sensor network (WSN) is a network comprising a huge number of tiny wireless devices called sensors, which promise communications in short distances. A sensor is characterized by its small size, low cost, low power, and short radio range. Typically, sensors are randomly deployed in an area of interest in either an ad hoc or manual pre-scheduled manner depending on the requirements of applications. Unlike other wireless networks, the WSN is application-specific and energy-constrained in essence.
Currently, the WSN is widely used for a variety of applications such as environmental monitoring, battlefield rescue, home automation, etc. Much research has paid attention to numerous attractive topics, among which sensor localization is essential but extremely crucial for many applications in WSNs. Additionally, in WSNs, detection and tracking are important operations especially for the urgent target of interest. In the dissertation, we primarily address these critical and attractive issues and aim at the development of mechanisms for such themes.
Numerous applications for WSNs require physical location to recognize more precise positions at which designated targets or events occur. Majority of the existing approaches assume that each sensor is aware of its location information (e.g., 2D coordinate) via either an installed GPS receiver or other GPS-less localization schemes. However, the precise location information may be unavailable due to the constraints in terrain. Additionally, several applications can tolerate the diverse level of accuracy in such geographic information, and in terms of efficiency, acquiring the direction of a sensor takes less effort than obtaining its physical location. As a result, in this dissertation, we propose a direction-based localization scheme, called DLS for a sensor to determine its direction related to the sink. Basically, DLS is motivated by the spatial locality property based on our comprehensive observations of received packets at a sensor. In addition, DLS adopts the anchor deployment strategy to improve the estimated accuracy of the sensor close to the sink. Furthermore, with the aid of a novel virtual dual direction coordinate (VDDC) system, DLS is able to efficiently and precisely position sensors around the boundary of two adjacent directional regions.
Recent research on detection and tracking has paid much attention to the WSN in which all sensors are identical in sensing units, but the potentiality of the utilization of different types of sensors has not been explored. In addition, the existing approaches largely focus on the event, which can be identified by the homogeneous sensors. However, the constraints in sensing and communication capabilities make these solutions difficult to apply to the network in which the event has to be detected and tracked via various types of sensors. In the dissertation, we propose a fully distributed protocol, CollECT, to event detection and tracking in Wireless Heterogeneous Sensor Networks (WHSNs), regarded as a network comprising various types of sensors. Basically, CollECT focuses on triangulation construction, in which the sensors with the same type construct the triangulation composed of multiple logical triangles to represent the attribute region. Specially, sensor collaboration of the different types of sensors is taken into account to determine the existence of the event. Additionally, CollECT also aims to select the sensor, called border sensor to represent the event boundary. In principle, CollECT includes the vicinity triangulation, event determination, and border sensor selection procedures to construct the attribute region, to determine the existence of the event, and to identify the event boundary, respectively.
Overall, the issues involved in the dissertation are really essential and important in wireless sensor networks. We not only propose a direction-based localization scheme for a sensor to obtain its geographic information, but also devise an efficient detection and tracking protocol for WHSNs to identify the event of interest. Experimental results demonstrate that the average estimated correct rates in DLS approximately reach 94%, 86%, and 81% for the networks with 4, 8, and 16 directions, respectively. The results also validate the practicality of DLS, and show that DLS effectively achieves direction estimation with regardless of sink placement or network density. In terms of performance of CollECT, approximately 94% sensors within the event region can be correctly identified on average. Moreover, the border sensors selected by CollECT also reasonably stand for the event boundary.
論文目次 Contents
1 Introduction .............................................1
1.1 Research Overview and Contributions ....................2
1.2 Introduction to WSNs ...................................3
1.3 Localization in WSNs ...................................3
1.4 Tracking in WSNs .......................................5
1.5 Organization of the Dissertation .......................5
2 Background ...............................................7
2.1 Localization Approaches for WSNs .......................7
2.1.1 Range-based Localization Schemes .....................7
2.1.2 Range-free Localization Schemes ......................9
2.1.3 Summary .............................................10
2.2 Tracking Approaches for WSNs ..........................10
2.2.1 Target Tracking Schemes .............................10
2.2.2 Boundary Detection Schemes ..........................12
2.2.3 Summary .............................................14
3 Direction-based Localization in WSNs ....................15
3.1 Introduction ..........................................16
3.2 Preliminaries .........................................18
3.2.1 Network Model .......................................18
3.2.2 Basic Concepts ......................................21
3.3 Spatial Locality Property .............................23
3.4 Direction-based Localization Scheme (DLS) .............25
3.4.1 Anchor Deployment Strategy ..........................26
3.4.2 Multi-message Decision Scheme .......................27
3.4.3 Virtual Dual Direction Coordinate (VDDC) System .....28
3.5 Discussions ...........................................32
3.6 Performance Evaluations ...............................33
3.6.1 Simulation Environment ..............................34
3.6.2 Simulation Results ..................................34
3.6.2.1 E®ects of Anchor Deployment Strategy ..............35
3.6.2.2 Comparison of Di®erent Localization Schemes .......37
3.6.2.3 E®ects of the VADC system .........................42
3.6.2.4 Edge Sink Performance .............................44
3.6.2.5 Summary ...........................................46
3.7 Summary ...............................................47
4 Collaborative Tracking in WHSNs .........................49
4.1 Introduction ..........................................50
4.2 Preliminaries .........................................53
4.2.1 Network Model .......................................53
4.2.2 Basic Concepts ......................................56
4.3 Collaborative Event deteCtion and Tracking Protocol (CollECT) .................................................59
4.3.1 Vicinity Triangulation ..............................59
4.3.2 Event Determination .................................64
4.3.3 Border Sensor Selection .............................67
4.3.4 Event Tracking ......................................68
4.4 Discussion about Triangulation Consistency ............69
4.5 Performance Evaluations ...............................71
4.5.1 Simulation Environment ..............................72
4.5.2 Simulation Results ..................................72
4.5.2.1 Event Accuracy ....................................72
4.5.2.2 Border Sensor Fitness .............................75
4.5.2.3 Summary ...........................................76
4.6 Conclusions ...........................................78
5 Conclusions and Future Works ............................79
5.1 Conclusions ...........................................79
5.2 Future Works ..........................................81

List of Figures
3.1 Example of the network model with Ndir = 4. Without loss of generality, the sink is placed at the center. Four anchors (i.e., a1, a2, a3, and a4) are deployed at the range r to the sink on each axis. The network is virtually partitioned into 8 regions: 4 directional regions (i.e., R1, R2, R3, and R4) and 4 axis regions (i.e., A1, A2, A3, and A4). Each axis region is divided into two sub-regions. Namely, axis region Ai includes sub-regions A(1) i and A(2) i. ................19
3.2 Overview of DLS. The sink originates an LREQ packet outward, and the packet then floods throughput the network by means of packet dissemination. All unknown sensors will determine their directions in accordance with the LREQ packets received. .........................................22
3.3 Spatial locality property. The network is a 500m£ 500m square region with Ndir = 4. The sink is at the center of the network. (a) average percentage of packets received at a sensor vs. number of sensors. (b) average percentage of packets received at a sensor vs. communication range ......24
3.4 Anchor deployment strategy. The shaded region in the network is the critical region. ...........................27
3.5 State transition diagram of DLS at each sensor. .......28
3.6 Coding system with Ndir = 4 in DLS, where r is the communication range of a sensor. Each direction Ri comprises many regions, individually represented by R(1) i , R(2) i , A(1) i , A(2) i , and the critical region in Ri. All unknown sensors are identified as two direction codes, represented in the form (primary direction code, auxiliary direction code). ...........................................................30
3.7 Example of the virtual dual direction coordinate (VDDC) system. (a) VDDC system for Ndir = 4. (b) VDDC system for Ndir = 8. .................................................31
3.8 Different sink placement approaches. (a) the sink is at the corner. (b) the sink is at the edge. (c) the sink is within the network. Ndir is assumed to be 8. ..............33
3.9 Estimated accuracy for di®erent anchor deployment strategies and the number of directions. ..................36
3.10 Example of spatial sensor distribution for 500 sensors scattered in the network with Ndir = 4. The blue and red sensors respectively represent the sensors with accurate and erroneous estimates. ......................................38
3.11 Estimated accuracy vs. number of sensors for different localization schemes. The number of directions in the network is 2n. ....................................................39
3.12 Estimated accuracy vs. communication range for di®erent localization schemes. .....................................39
3.13 Improvements of DLS in correct rates compared to RLS and GLS. ......................................................40
3.14 Performance of scalability of DLS. ...................42
3.15 Improvements of DLS in correct rates compared to DLS-VADC for different numbers of sensors. DLS-VADC means the VADC system is not involved in DLS. .......................43
3.16 Improvements of DLS in correct rates compared to DLS-VADC for various communication ranges. DLS-VADC means the VADC system is not involved in DLS. .......................44
3.17 Estimated accuracy vs. number of sensors in DLS. The sink is placed at the edge. ...............................45
3.18 Estimated accuracy vs. range in DLS. The sink is placed at the edge. ..............................................46
4.1 Example of a WHSN, in which two attribute regions, Ra1 and Ra2 , exist, and form event region, Re1 . The circle and the square sensors are able to respectively perceive attributes a1 and a2. The dark, gray, and white sensors are urgent, alert, and ordinary sensors, respectively. ........51
4.2 Triangulation vs. non-triangulation. (a) Sensor s is in attribute region 4.3 Overview of CollECT, where T = 2. (a) The result of sensor deployment. All sensors are ordinary ones. (b) The result of vicinity triangulation. (c) The result of event determination. (d) The result of border sensor selection. .58
4.4 Logical triangle reorganization. (a) Sensors u, v, and w form a logical triangle LTuvw. (b) Sensor x is inside the logical triangle LTuvw. (c) Sensor w is inside the triangle with vertices u, v, and x. (d) Sensors u, v, w, and x form
a quadrilateral in which uw < vx. (e) Sensors u, v, w, and x form a quadrilateral in which vw < ux. (f) Sensors u, v, w, and x form a quadrilateral in which wx < uv. ..............61
4.5 Alert-In-Triangulation Test. Suppose event spreads out from a small region (i.e., event source) at the upper left of the ‾gure. The circle, the and square sensors are able to detect attributes a1 and a2, respectively. Event e1 is composed of attributes a1 and a2. Gray sensors are alert sensors. Sensors c (blue border) and y (red border) are respectively the leading sensors of LTcde and LTxyz, and are responsible for the AIT test when receiving the VTREQ packets. ..................................................66
4.6 Example results of event determination and border sensor selection. (a) Sensors a, c, e, x, y, and z become urgent sensors. (b) Sensors b and d become border sensors according to Rules 4.1 and 4.2. .....................................67
4.7 Triangulation consistency. Suppose attribute region Ra1 spreads out from a small region at the above left of the figure. ...................................................69
4.8 Three cases of triangulation consistency. (a) Sensor x receives VTREQ packets from v and w prior to u. (b) Sensor x receives VTREQ packets from u and v prior to w. (c) Sensor x receives VTREQ packets from u and w prior to v. ...........70
4.9 Example of roles of all sensors in the network, in which rs = 30m. The circle and square sensors are able to detect attributes a1 and a2, respectively. The black, gray, and white sensors respectively indicate the urgent, border, and ordinary sensors. The red and blue sensors indicate the alert sensors corresponding to a1 and a2, respectively. .........74
4.10 Event accuracy vs. sensing range, where Ns = 400. ....74
4.11 Event accuracy vs. number of sensors, where rs = 30m. ...........................................................75
4.12 Border sensor fitness vs. communication range, where Ns = 400. ....................................................77
4.13 Border sensor fitness vs. number of sensors, where rc = 40m. ......................................................77

List of Tables
4.1 Notations and terminologies. ..........................54
4.2 Information in a VTREQ packet. ........................59
4.3 Information in an EVT packet. .........................64
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