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系統識別號 U0026-3105201910431700
論文名稱(中文) 感測網路之資料定位校正暨障礙物容忍路徑規劃演算法
論文名稱(英文) Data Localization Correction and Obstacle Tolerant Path Planning Algorithms for Sensor Networks
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所
系所名稱(英) Institute of Manufacturing Information and Systems
學年度 107
學期 2
出版年 108
研究生(中文) 蔡榮貴
研究生(英文) Rong-Guei Tsai
學號 P98031056
學位類別 博士
語文別 英文
論文頁數 106頁
口試委員 指導教授-蔡佩璇
口試委員-蔡孟勳
口試委員-謝孫源
口試委員-陳盈如
口試委員-陳建志
口試委員-李佳衛
中文關鍵字 海底無線感測網路  移動式錨節點輔助定位  定位  路徑規劃 
英文關鍵字 underwater wireless sensor networks  mobile-anchor-node-assisted localization  localization  path planning 
學科別分類
中文摘要 在感測網路的許多應用中,定位是一個重要的技術之一,許多應用都必須仰賴定位技術的支持,像是資料匯集,目標追蹤,路由協定等。本論文的研究可分為兩個部分:(1)資料定位校正以及(2)路徑規劃。相比於陸地上的感測網路,海底無線感測網路(UWSNs)存在更多的限制與挑戰,水下的感測器是動態的,感測器位置不斷的變化,為靜態感測網路設計的定位方法不能應用於UWSNs。本文提出了一種採用資料位置校正的定位方法,稱為Data Localization Correction Approach(DLCA),可不需要額外耗費通訊成本和感測器電力的情況下定位。在不失一般性的情況下,我們基於kinematic model和meandering current mobility model來模擬海洋的環境,實驗結果表明,DLCA可以顯著地降低通訊成本,同時保持較高的定位精準度。然而,為了降低佈署成本以及環境上的限制,Mobile-Anchor-Node-Assisted Localization(MANAL)是一種可行的網路架構。移動錨節點(mobile-anchor-node, MAN)提供其自己的位置資訊以幫助感測器定位。然而,在現實的環境中,因為障礙物阻擋了MAN所經過的路徑,使得感測器不能從MAN接收足夠的三個位置資訊。我們提出了obstacle tolerant path planning (OTPP)方法來解決由於障礙物導致的感測器無法定位的問題。OTPP近似最佳化信標點的數量和路徑規劃,確保所有感測器可以從MAN接收三個位置資訊並減少MAN廣播的次數。實驗結果顯示,OTPP比Z-curve [14]表現更好,因為它使用較少的信標點總數,因此更適合於存在障礙物的環境。與Z-curve相比,OTPP可以減少定位誤差並提高定位覆蓋率。
英文摘要 In many applications of sensor networks, localization is an important technology. Many applications rely on localization technologies, such as data aggregation, target tracking, and routing protocols. This thesis can be divided into two parts: (1) data localization correction and (2) path planning. Underwater wireless sensor networks (UWSNs) demonstrate more limitations and challenges than terrestrial sensing networks. Underwater sensors are dynamic, and sensor positions are changing constantly. Localization schemes designed for static sensor networks cannot be applied to UWSNs. This thesis presents a hybrid localization approach with data-location correction, called Data Localization Correction Approach (DLCA), which positions data without additional communication overhead and power consumption on sensors. Without loss of generality, we simulate the ocean environment based on a kinematic model and meandering current mobility model. Our results show that DLCA can significantly reduce communication costs, while maintaining relatively high localization accuracy. However, to reduce deployment costs and environmental constraints, Mobile-Anchor-Node-Assisted Localization is a viable network architecture. A mobile anchor node (MAN) provides its own location information to assist the localization of sensor nodes. However, in a realistic environment, sensor nodes generally cannot be located because the obstacles block the path traversed by the MAN, thereby rendering the sensor incapable of receiving sufficient three location information from the MAN. We proposed the obstacle tolerant path planning (OTPP) approach to solve the problem of sensor not being located owing to obstacle blocking. OTPP can approximate the optimum beacon point number and path planning, thereby ensuring that all the unknown nodes can receive the three location information from the MAN and reduce the number of MAN broadcast packet times. Based on the experiment results, OTPP performs better than Z-curve because it uses less total number of beacon points and is thus more suitable in an obstacle-present environment. Compared with the Z-curve, OTPP can reduce localization error and improve localization coverage.
論文目次 摘要 I
ABSTRACT II
誌謝 III
TABLE OF CONTENTS IV
FIGURES VII
TABLES X
CHAPTER 1 INTRODUCTION 1
1.1 Localization and Challenges in Underwater Sensor Networks 1
1.2 Path Planning and Challenges for Mobile Anchor Node Assisted Localization 4
1.3 Localization Schemes 7
1.3.1 Centralized Localization and Distributed Localization 7
1.3.2 Anchor Based versus Anchorless 7
1.3.3 Range Based versus Range Free 8
1.4 Distance Estimation Technologies 12
CHAPTER 2 RELATED WORKS 16
2.1 Localization Algorithms 17
2.1.1 Stationary Localization Algorithms 17
2.1.2 Mobile Localization Algorithms 22
2.1.3 Mixed Localization Algorithms 28
2.2 Path Planning Algorithm 35
2.2.1 Offline Path Planning 36
2.2.2 Online Path Planning 44
CHAPTER 3 DESIGN OF DLCA 49
3.1 Network Topology 49
3.2 Overview of DLCA 50
3.2.1 Node Localization 51
3.2.2 Data Packet Format for Data-Location Correction 51
3.2.3 Data Structure of DLCA 53
3.2.4 DLCA Table Initialization 54
3.3 Recursive Correcting Data Packet Localization 56
3.3.1 Find Victim Node 56
3.3.2 Compute Location of Victim Node by Shift Vectors 58
3.3.3 Recursively Correct Data Locations 60
3.4 Summary 60
CHAPTER 4 DESIGN OF OTPP 62
4.1 Preliminary 63
4.2 Problem Description 64
4.3 Obstacle-Tolerant Path Planning Algorithm 67
4.3.1 Deployment of Beacon Points in OTPP 67
4.3.2 Path Planning in OTPP 73
4.4 Summary 75
CHAPTER 5 SIMULATION RESULTS 76
5.1 DLCA Performance Evaluation 76
5.1.1 Simulation Settings 76
5.2 Results and Analysis 80
5.2.1. Performance with Varying Average Moving Speed 80
5.2.2. Performance with Varying Times of Pd 81
5.2.3. Performance with Varying Anchor Percentage 83
5.3 OTPP Performance Evaluation 85
5.3.1 Simulation Setup 87
5.4 Results and Analysis 88
5.4.1 Impact of Blocking Rate 88
5.4.2 Impact of Resolution 92
5.4.3 OTPP versus Online Path Planning Algorithms 94
CHAPTER 6 CONCLUSION AND FUTURE WORKS 96
REFERENCES 97
APPENDIX 104
PUBLICATIONS 105
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