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系統識別號 U0026-2011201616382200
論文名稱(中文) 基於移動信標之無線感測網路定位路徑規劃機制
論文名稱(英文) Σ-Scan : A Mobile Beacon-Assisted Localization Path Planning Algorithm for Wireless 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) Institue of Manufacturing Information and Systems
學年度 105
學期 1
出版年 105
研究生(中文) 鄭文達
研究生(英文) Wen-Dar Cheng
學號 P96031123
學位類別 碩士
語文別 英文
論文頁數 35頁
口試委員 指導教授-蔡佩璇
口試委員-謝孫源
口試委員-鄧德雋
口試委員-蘇淑茵
口試委員-蔡孟勳
中文關鍵字 移動信標  無線感測網路  路徑規劃  網路定位  三邊定位 
英文關鍵字 mobile beacon  WSN  path planning  network localization  trilateration 
學科別分類
中文摘要 無線感測網路(WSN) 已經被廣泛應用在各個專業領域, 例如自然環境監測、工業控制、健康管理、交通控管、軍事用途及海底感測網路等。準確的位置資訊是無線感測器中最重要的資訊之一, 全球定位系統(GPS) 是目前最為廣泛使用的定位技術, 然而因為環境上的限制,GPS 無法適用於所有無線感測網路之情境, 例如室內或者有遮蔽物之環境, 再加上GPS 成本不菲, 若將動則上百、上千之感測器上皆安裝GPS 晶片,將使此一WSN 架構的經濟門檻難以被普遍實踐, 在諸多限制之下, 移動信標(mobile beacon) 架構被提出用來協助定位, 移動信標主要是一個主動移動並裝載GPS 的節點,透過特定路徑及特定廣播點來協助區域內所有感測器以進行定位計算,移動信標架構可以有效地減少硬體成本, 然而利用移動信標定位有以下幾項困難點, 主要圍繞在信標該如何去移動及廣播, 其中包括路徑的長短、廣播點的相對位置是否具有共線性、廣播點的位置及數量、通訊半徑如何去與定位精準度和覆蓋率達成一個良好的平衡等。早期移動信標的路徑規劃大多注重於如何達到最短路徑以達到省電的效果,然而達到最短路徑同時伴隨的弊病就是精準度不夠、無法全域定位及發生廣播點共線而產生的定位不確定性問題, 因此隨後的論文開始修改或是提出創新的路徑規劃以解決上述的缺點, 但是迄今移動信標之路徑在追求精準與追求省電之間尚未找到一個完善的平衡點, 因此本篇論文預期能提出一個路徑規劃演算法, 藉此希望能夠解決目前現有方法的諸多問題, 其中包括共線所導致之定位不確定性、多餘路徑、無法將區域內感測器完全定位、精準度不夠等問題, 本篇論￿文將在移動信標及定點感測器之架構下, 提出一個創新的路徑規劃機制Σ-Scan, 基於成本、精確度、陸上環境及WSN 感測器同步的困難性之考量,Σ-Scan 選擇range-based 中的Received Signal Strength Indication(RSSI) 做為我們的測距工具, 並採用以節省時間為優先之三邊定位法(Time Priority Trilateration, TPT) 以達到快速定位及延長感測器壽命之目的, 經由實驗結果顯示,Σ-Scan 能同時保留高精準度並且在路徑長度方面能比近幾年之以精準為目標之移動信標路徑還要短、在共線性上本篇論文提出之解決方法也能改善其他論文之共線問題, 總結來說Σ-Scan 能同時達到高精準度、無共線性、全域定位、短定位時間、最短路徑及延長感測器壽命, 並在與近期提出之路徑相比之後得到較高性能。Σ-Scan 目前為止能夠運用在任何無障礙物之矩形區域,Σ-Scan 已經解決或優化大多數現有路徑規劃之問題, 未來我們希望Σ-Scan 能夠解決路徑途中有障礙物的情境以及讓Σ-Scan 能夠適用於更多的規則與不規則圖形之區域。
英文摘要 There are already several applications of Wireless Sensor Network (WSN) used in different professional fields, such as, environmental surveillance, industrial control, health management, traffic control, military application, and Underwater Wireless Sensor Network (UWSN). Having accurate localization information is one of the most essential condition in WSN. Although Global Positioning System (GPS) has been the most common way to localize any equipment, however, there are lots of scenarios being unable to work properly with GPS, such as, indoor areas and sheltered environments; moreover, due to the cost of GPS, it is not realistic to use GPS under the architecture of WSN which usually has hundreds of thousands of sensors waiting to be localized. As a result, the architecture of mobile beacon was proposed to solve these problem. A mobile beacon is a beacon having the ability to travel around, and with specific path and broadcast positions also known as keys, a mobile beacon can assist sensors receiving its packages to localize themselves. Nevertheless, there are problems to be solved; one is where a mobile beacon should broadcast localization packages, another is how to travel through these keys, and the other is how to achieve higher localization accuracy, higher coverage, and less collinearity problems. The path planning for mobile beacon, in early time, focuses on the shortest path length and the lowest number of keys to reach power efficiency. As time goes by, more and more path planning chasing for high accuracy are proposed. However, there is still not a good balance point for a trade-off between power efficiency and high localization accuracy. Hence, the method proposed in this paper: Σ-Scan aims to reach as high accuracy as other recent methods and as short as possible to reach shortest path length. Σ-Scan will use Received Signal Strength Indication (RSSI) as our ranging technique and trilateration as our localization algorithm. As the simulation results showed in section 4, Σ-Scan can reach high accuracy, high coverage, low collinearity problems, shorter path length, and less number of keys at the same time. Σ-Scan can be applied in any obstacle-free 2D rectangular area so far. In the future, we aim to reduce the limitation of Σ-Scan so that Σ-Scan can fit in more irregular areas.
論文目次 摘要 i
Abstract ii
Table of Contents iii
List of Tables v
List of Figures vi
Chapter 1. Introduction 1
Chapter 2. Related Work 4
Chapter 3. Σ-Scan 8
3.1. Units of Σ-Scan 8
3.1.1. Unit Rectangle 8
3.1.2. Unit Square 9
3.1.3. Special Unit 10
3.2. Σ-Scan Area 10
3.2.1. Definitions of a Σ-Scan Area 10
3.2.2. Cases of Σ-Scan Area 11
3.3. Number of Keys 12
3.4. Path of Σ-Scan 13
3.4.1. Paths of Different Units 13
3.4.2. Paths of Different Cases 13
3.5. Path Length 16
3.6. Collinearity problem 16
Chapter 4. Simulation Result 20
4.1. Localization Algorithm 20
4.1.1. Trilateration 20
4.1.2. Time-Priority Trilateration 21
4.1.3. Accuracy-Priority Trilateration 22
4.2. Simulation 22
4.2.1. Accuracy, Coverage and Collinearity Problem 22
4.2.2. Path Length and Key Number 23
4.2.3. Time-out System 25
Chapter 5. Conclusion 30
Bibliography 31
Appendix A. Comparisons to Other Path Planning 33
A.1. Hilbert and Z-curve 33
A.1.1. Unit Square 33
A.1.2. Time-Out 34
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