臺灣博碩士論文加值系統

在無線感測器網路中，位置(即座標)是一項非常重要的資訊，這是因為有需多的感測器網路應用都會需要使用到此資訊，如安全監視系統，燈光控制系統，火警系統等等,而室內環境中複雜的電磁波傳輸特性是室內定位演算法遭遇的一個大問題。我們經觀察後發現目前對於室內定位演算法的研究大部分是先將大量的事前知識先輸入給網路中的幾個特定感測器在進行定位計算，透過這種方式獲得的定位準確度雖然相當精確，但是同時也需要在定位之前執行需多的準備工作。因此在本篇論文當中，我們提出一個只需要非常少量事前知識的無線感測器網路室內定位演算法，其利用室內障礙物的特性將感測器分組，讓感測器在各自群組中作初步的定位，並在最後將各群組有效的重新組合在一起，得到相當不錯的定位準確度。
在這個研究中，我們使用C語言撰寫整個模擬的環境與設定，觀察演算法在不同程度的雜訊干擾之下的表現，並與其他演算法比較，嘗試說明本演算法的一些特性與限制，以及在某些特殊環境之下的表現。

Localization is an important topic in wireless sensor networks because lots of the applications need this information to perform normally, such as a surveillance system, a light control system, or a fire alarm system. One of the biggest problems in indoor localization is the complex propagation characteristics of the indoor environment. We find that most of the current works solve this by giving lots of prior-knowledge to some specific nodes in the network. Although in this way we can get a very accurate localization result, this kind of schemes needs lots of efforts to make the algorithm perform well. In this work, we propose an indoor localization algorithm for wireless sensor networks which needs few prior-knowledge. It uses the characteristics of the obstructions in the environment to group nodes in the network. Sensor nodes first locally calculate their position, and then we combine each group together and get good localization accuracy.
In this work, we use C language to write the whole simulation. We observe the performance of the algorithm in different noise power and compare to other indoor localization algorithms. We also try to explain the limitation of the algorithm and the performance of it in some special cases.

誌謝 2
摘要 3
Abstract 4
Contents 5
List of figures 6
List of tables 7
Chapter 1 Introduction 8
1.1 Motivation 8
1.2 Related Work 11
1.3 Idea 16
Chapter 2 Assumptions & Requirements 20
Chapter 3 Algorithm 23
Chapter 4 Simulation 35
4.1 Simulation Setup 35
4.2 Simulation Result 39
4.3 Characteristics for the Algorithm 45
4.4 Observations for the Algorithm 49
Chapter 5 Conclusions 56
Chapter 6 Future Work 58
Reference 60

1.Paramvir Bahl and Venkata N. Padmanabhan, “RADAR: An In-Building RF-based User Location and Tracking System”, INFOCOM, 2000
2.D. Moore, J. Leonard, D. Rus, and S. Teller, “Robust Distribution Network Localization with Noisy Range Measurements”, ACM SenSys, 2004
3.Xiaoli Li, Hongchi Shi, and Yi Shang, “A Sorted RSSI Quantization Based Algorithm for Sensor Network Localization”, ICPADS, 2005
4.Haowen Chan, Mark Luk, and Adrian Perrig, “Using Clustering Information for Sensor Network Localization”
5.Kiran Yedavalli, Bhaskar Krishnamachan, Sharmila Ravula, and Bhaskar Srinivasan, “Ecolocation: A Sequence Based Technique for RF localization in Wireless Sensor Networks”, IPSN, 2005
6.Cesare Alippi, Alan Mottarrella, Giovanni Vanini, “A RF-Map Based Localization Algorithm for Indoor Environments”, IEEE, 2005
7.Yiming Ji, Saad Biaz, Santosh Pandey, Prathima Agrawal, “ARIADNE: A Dynamic Indoor Signal Map Construction and Localization System”, MobiSys, 2006
8.Hyuk Lim, Lu-Chuan Kung, Jennifer C. Hou, and Haiyun Luo, “Zero-Configuration, Rubust Indoor Localization: Theory and Experimentation”, INFOCOM, 2006
9.Y.E Mohammed, A.S. Abdallah, Y.A. LIU, “Characterization of indoor Penetration Loss at ISM Band”, CEEM, 2003
10.Joseph Polastre, Robert Szewczyk, and David Culler, “Telos: Enabling Ultra-Low Power Wireless Research”
11.[NS] Chapter 17, Radio Propagation Models