# 臺灣博碩士論文加值系統

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 在無線感測網路(wireless sensor networks)中，對於每個使用者而言，存取無線感測網路資料傳輸的最小成本路徑是主要探討的議題之一，主要目的在於利用最短的時間將訊息從起始地傳送到目的地，除了時間的效益外，亦可有效控制傳輸的成本。著名的貪婪演算法(greedy algorithm)Dijkstra algorithm可以說是最小成本路徑的基礎，我們以Fast Marching Methods來求非線性方程式Eikonal Equations的近似解；並根據每個節點的距離值以Back-tracking來求其路徑座標得到路線，最後以這兩個演算法來做比較。使用幾何光學(Geometrical Optics)的方法在宏觀的概念下，考慮在不同密度下，欲最佳化的網路模組，使用Fast Marching Method計算其成本函數，再針對不同的網路模組根據梯度向量找出最佳的繞送路徑。並期望能將此方法套用到更多不同模組上。
 For each user in wireless sensor networks, the minimum cost routing path of wireless sensor networks is one of the main issues to explore. The main purpose is to use the shortest time to transfer messages from the beginning to the destination, in addition to the benefits of time, can effectively control the cost of transmission. A well-known greedy algorithm-Dijkstra algorithm can be said that the basis of the minimum cost path. We use Fast Marching Method to seek approximate solution of the non-linear equation-Eikonal Equation. According to the distance value of each node using the Back-tracking to find the coordinates to get the route, and finally to compare these two algorithms.In the concept of macroscopic use the method of geometrical optics. When using the Fast Marching Method to calculate the cost function, the network module which you want to optimize must consider in different density. For different network modules based on the gradient vector to find the optimal routing path. Focusing on finding the best routing path on the basis of the gradient vector. It is expected to apply this method to a wide range of modules.
 第一章 緒論第二章 相關研究第三章 計算距離值 3.1 Dijkstra演算法 3.2 Fast Marching Method 3.2.1 Fast Marching Method原理 3.2.2逆向有限差分法 3.2.3 Fast Marching Method架構 3.3 回溯法第四章 成本函數 4.1 Optics Analogy 4.2套用網路模組第五章 實驗模擬 5.1 最小成本路徑 5.2 成本函數網路模組第六章 結論與未來展望參考文獻
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