智慧型手機已經成為我們生活中的一部分，並且為使用者們提供了各式各樣的服務功能。智慧型手機一般都具備有多樣的感測器，使之非常適合情境感知計算。現今，大部分在智慧型手機上的情境感知應用程式以各自的方式實作他們的情境感知功能並藉由offline learning去建立靜態不變的情境模型。然而，隨著情境感知應用程式的數量增長，將往往會導致低效率的系統資源使用和情境資訊的處理。事實上，基於線上機器學習系統的情境感知應用程式有許多相同的部分。在這篇論文中，我們發現可以將這些相同的部分給擷取出來成為一個獨立且統一的服務框架去協助各種不同的情境感知應用程式。我們將會討論如何在Android系統上去實作這樣一個廣泛應用的服務框架。我們也探討了我們所提出的這個服務框架與沒有使用這個服務框架的效能情況。

Smartphones are becoming an integral part of our life that provide various services to their users. Smartphones are typically equipped with a variety of sensors, making them ideal for context-aware computing. Most context-aware applications running on smartphones today implement their own logic for context-awareness and rely on offline learning to build static context models. However, with an increase in context-aware applications on smartphones, such a practice often leads to inefficient uses of system resources and ineffective handling of dynamic contexts. In fact, context-aware applications based on online machine learning have many common components. In this thesis, we show that it is possible to identify these common components to develop a unified service framework that supports various context-aware applications in a smartphone system. We will discuss how to implement such a general-purpose service framework in an Android system. Performance of the proposed framework is compared with that without using the framework.

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