用於無線感測網路中達到較高覆蓋率的新適應定位演算法

Abstract

許多無線感測網路的應用(例如:森林火災的偵測、環境監控、軍事應用、野生動物的追蹤)都需要位置資訊來偵測並且記錄事件發生的地點。如何獲取感測器的位置資訊變成一個重要的問題，並且通常被稱為定位問題。利用全球定位系統(GPS)是一個可能的解決方案，但是並不實際。其原因在於一個無線感測網路經常包含數千個感測器，以致於為每個感測器裝上GPS的價格太過昂貴而難以實現這個解決方案。在文獻[3]中，黃等人研究了一個新的最佳化問題，最小花費定位問題(minimum cost localization problem)，這個問題透過利用最小數量的錨點來定位出所有感測器的位置。本篇論文的目的是對於無線感測網路提出可調式演算法的概念。我們的演算法比文獻[3]的演算法簡易，並且考慮了所有文獻[3]的演算法所考慮的情形；實驗數據也顯示我們的演算法有比較好的覆蓋率。

Many applications in wireless sensor networks (such as forest fires detection, environment monitoring, military, and wildlife tracking) require position information to detect and record events. How to obtain the position information of the sensors become an important problem and is usually called the localization problem. Using Global Positioning System (GPS) is a possible solution for localization but is impractical. The reason is that a wireless sensor network usually has thousands of sensors and it is too expensive to equip every sensor a GPS. In [3], Huang et al. studied a new optimization problem, minimum cost localization problem, which aims to localize all sensors by using the minimum number of anchors. The purpose of this thesis is to propose adaptive algorithms for the localization problem. Our algorithms are simpler than the algorithms in [3] and cover all the cases in the algorithms in [3]; simulation results also show that our algorithms have better coverage.