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系統識別號 U0002-2208201103121000
中文論文名稱 感測網路中運用人工智慧演算法定位之成本與效能評估
英文論文名稱 Cost and Performance Evaluation on AI-based Localization in Wireless Sensor Networks
校院名稱 淡江大學
系所名稱(中) 電機工程學系碩士班
系所名稱(英) Department of Electrical Engineering
學年度 99
學期 2
出版年 100
研究生中文姓名 江奕均
研究生英文姓名 Yi-Jun Jiang
學號 697470549
學位類別 碩士
語文別 中文
口試日期 2011-06-16
論文頁數 90頁
口試委員 指導教授-莊博任
委員-陳省隆
委員-吳庭育
中文關鍵字 類神經網路  定位  無線感測網路  效能評估 
英文關鍵字 Neural Network  Localization  Wireless Sensor Networks  performance evaluation 
學科別分類 學科別應用科學電機及電子
中文摘要 無線感測網路(Wireless Sensor Network, WSN)主要被運用於區域資料的蒐集,透過資料的蒐集可以使得操作者對於其區域有更深的了解並根據資料做出更好的決策。WSN雖然可以針對一整片區域進行資料的蒐集,但是對於某些任務來說,必須要更準確的了解每個感測器所處於的位置,因此必須要對感測器做定位的動作以增加資料的可用性,而定位的準確性與所需花費的成本也為其最關鍵的問題。
就目前定位計算的分類大致可以分為兩類: Statistics-based與AI-based。Statistics- based的方法計算較為簡單,但限制較多且定位的結果較不準確,如Kalman filter。而AI-based則反之,雖然計算較為複雜,但卻有限制少與高準確率的優點,如PSO、Neural Network。在AI的演算法中,我們主要評估不同Neural Network定位方法間彼此的差異性,根據定位方法的不同,NN的輸入也將有所差異,這樣的差異也將使得效能有所分別。文中除了比較NN定位方法間的差異外,我們還納入了PSO-based定位方法以評估不同AI間的區別。
對於WSN而言,除了要有好的效能外,成本也是非常重要的部分,若是沒有將成本與效能做一個平衡,將使得WSN壽命減少亦或參考資料價值不足。
為此,我們以不增加額外成本的前提下提出了一個運用NN-based的定位方法,新方法運用Online的訓練方式使得訓練出來的網路模型與拓墣呈正相關,在訓練時我們所納入的訓練組為拓墣全部可能出現的情形,亦即全拓墣式的訓練組,Online的訓練方式與全拓墣式的訓練組將使得我們所訓練出來的網路模型與拓墣是完全相關。為了使效能更加增進,我們提出了結合RSSI與hop count兩種測距使得測距可以更加精準,而在網路模型輸入的部分,我們也將其做倒數的處理使其具有權重,結合兩種測距以及權重的概念將使得我們可以在不增加成本的前提下,擁有更佳的效能。
英文摘要 Wireless Sensor Network is used mainly in regional data collection, through data collection can make the operator to understand the area and make the better decisions based on the information. Although it can collect the data of the area, but for the certain tasks, it must understands the location of each sensor, therefore, the sensors must be located to increase the availability of information, the accuracy of location and the cost are the most critical issues.
The classification of the current location calculation can be divided into two categories: Statistics-based and AI-based. The calculation of statistics-based method is simple, but it is more restrictive and inaccurate location, such as Kalman filter. The AI-based is contrary, although the calculation is more complex, but the limits are few and the accuracy is high, such as PSO and Neural Network. In the AI algorithms, we evaluate mainly the different location methods of Neural Network, according to the different location methods, input of Neural Network will be different, there will cause the performance difference. The paper in addition to compare the different between Neural Network location methods, we also included a PSO-based location method to evaluate the different between different AI.
For WSN, in addition to have a good performance, cost is also a very important part, if there is no balance of cost and performance, it will cause the reference value of location is not enough and life reduced of WSN.
We premise a location method of NN-based without additional cost, new method uses online training method to cause the trained network model correlated with the topology, we included all possible scenarios in topology to our training data, this mean the topology will be training data, online training method and topology training data will make our trained network model is completely relevant with topology. In order to improve the performance, we propose the estimate distance that combine two kinds estimate distance of RSSI and hop count to make more accurate, in the input part of network model, we also do the inverse cause the inputs have weight, combining two estimate distance and the concept of weight will cause the method have the better performance without addition cost.
論文目次 第一章、緒論 1
1.1、前言 1
1.2、章節架構 4
第二章、相關研究背景 5
2.1、無線感測網路簡介 5
2.2、無線定位系統 9
2.3、無線測距技術 12
2.4、運用Statistics-based定位 17
2.4.1、Kalman filter定位 17
2.5、運用AI-based定位 22
2.5.1、PSO-based定位 22
2.5.2、NN-based定位 28
第三章、現有AI-based定位之評估 43
3.1、AI-based定位之研究動機 43
3.2、現有AI-based定位之評估 45
第四章、我們提出的新方法 49
4.1、新方法的研究動機 49
4.2、新方法的運作過程 53
4.3、新方法的成本與效能評估 60
4.3.1、模擬參數 60
4.3.2、總節點數變化與定位誤差之評估 64
4.3.3、錨節點百分比與定位誤差之評估 66
4.3.4、RSSI取樣次數與定位誤差之評估 67
4.3.5、RSSI標準差與定位誤差之評估 69
4.3.6、稀疏節點的定位成功率 70
4.3.7、RSSI樣本為10之評估 72
4.3.8、成本評估 75
4.3.9、綜合評比 79
第五章、結論 81
第六章、參考文獻 85


圖目錄
圖1. 感測器基本元件 6
圖2. 三點定位機制 10
圖3. 多點定位機制 10
圖4. 三角定位機制 11
圖5. 卡爾曼濾波器流程 18
圖6. 卡爾曼濾波器模型 19
圖7. 均數0、變異數5的高斯隨機變數分布圖 20
圖8. PSO定位方法MODE2 28
圖9. NEURAL NETWORK網路模型 30
圖10. SUBBEACON搜尋方式 40
圖11. VN與SUBBEACON關係圖 47
圖12. 新方法測距修正 56
圖13. 新方法之訓練組產生演算法 59
圖14. 總節點數變化與定位誤差之評估 64
圖15. 錨節點百分比與定位誤差之評估 66
圖16. RSSI取樣次數與定位誤差之評估 67
圖17. RSSI標準差與定位誤差之評估 69
圖18. 稀疏節點的定位成功率 70
圖19. 總節點數變化與定位誤差之評估(RSSI樣本為10) 74
圖20. 錨節點百分比與定位誤差之評估(RSSI樣本為10) 74
圖21. RSSI標準差與定位誤差之評估(RSSI樣本為10) 74
圖22. 稀疏節點的定位成功率(RSSI樣本為10) 74
圖23. 節點至SINK所需經過的平均HOP COUNT 75


表目錄
表1. COMPARISON OF LOCALIZATION ERRORS (CM) 44
表2. 拓樸模擬參數 61
表3. PSO參數設定 61
表4. DANA ET AL.參數設定 62
表5. BP、VNBP 參數設定 62
表6. 新方法的參數設定 63
表7. 成本評估 76
表8. 定位方法綜合評比 80
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