近年來，智慧型生活的議題漸漸的受到關注，因應而生的相關情境感知系統(Context-aware. Systems)也持續受到研究與開發，在這樣的系統中，使用者或目標物的位置資訊通常是提供相關應用服務的重要依據之一，所以設計一個高準確的定位系統來提供所需的位置資訊是相當具有價值的。而一直來全球衛星定位系統(GPS)就是廣泛被使用在取得使用者位置資訊（如行車導航，戰地）的定位技術，但是由於此技術需滿足視距(Line-of-Sight)要求，因此，無法用於室內的情境感知系統。
本篇論文將建置一個以無線感測器為基礎的室內定位系統，利用無線感測器所發出的訊號，擷取定位目標所發送的訊號強度值來估算目標所在的位置。我們的系統主要有三個貢獻，首先，為了處理訊號強度在不同時間的變異性，我們提出索引位置的概念，以此位置收集的訊號強度來選擇合適的Radio Map。其次，我們紀錄訊號強度傳播的最小最大半徑，用來限縮定位目標所在範圍，藉此減少搜索時間以及增加位置估計的準確度。最後，我們提出一個訊號強度特徵的概念來估計目標位置。實驗顯示，我們的定位系統在同樣環境下，相對於其他同樣以訊號強度為基礎的定位方法，定位的準確度有著顯著的增進。

Recently, the smart environment with context-aware capability has becoming a popular research area. As the location information is an important context for a smart environment, a high accurate positioning system which provides the position information of a user in a smart environment is needed. While the Global Positioning System (GPS) is the most commonly used positioning system, it cannot be used in an indoor environment due to its Line-of-Sight (LOS) requirement.
In this paper, we developed an indoor localization system using wireless sensor networks. The proposed localization system uses the Received Signal Strength Indicator (RSSI) of the wireless sensor network for locating the position of an indoor user. Our system makes three contributions. First, to counter the temporal variations of the received signal strength, we propose to use an index location which possesses the largest variations in received signal strength to select a proper radio map in online location prediction phase. Second, we propose to record the minimal and maximal propagation ranges of received signal strength of a sensor node and use them to limit the search space in online prediction so as to reduce the searching time and increase the prediction accuracy. Lastly, we propose to the concept of signal strength fingerprint of a location and use this concept for location prediction. The experiments show that our localization system provides more than 100 percent improvement on prediction accuracy over the existing RSSI-based localization methods.

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