近年來，智慧型手機上的個人行為感測應用程式相當流行。這些應用程式讓跑步訓練者或是旅遊愛好者等。在手機上使用GPS感測器去紀錄使用者行走的GPS路徑點(GTPs-GPS track points)，並且上傳這些GPS路徑點到雲端服務器讓使用者能分享路徑資料給其他使用者。
然而上傳GTPs時不僅消耗網路傳輸量及手機耗電量，同時上傳的GTPs資料點通常夾帶了多餘或不精確的資料點，例如使用者在路上逗留徘徊的GTPs。為了解決這個問題，我們提出了此論文，稱之為節省上傳能源的GPS路徑轉角偵測過濾演算法(CEGF- Corner Extraction by GPS Filtering for Power-Efficient Location Uploading)，目的是從GTPs中過濾出轉角特徵點(CFGPs-corner feature GPS points)，而這些CFGPs能代表至相對應的道路。手機上的應用程式只需上傳CFGPs就能達到省電的目的。
這篇論文最具有挑戰性的問題是如何過濾掉不具特徵性的GTPs以及如何決定道路的轉角處。最後我們的實驗結果顯示，利用CEGF能有效過濾掉大量的GTPs並且CEGPs能精確地代表道路路段。更進一步，在手機上利用CEGF最多可省下87.7%的上傳電量。

Over the past few years, personal sensing applications, such as travel path sharing and location recording,
have been more and more popular. These applications use GPS sensors to record GPS track points
(GTPs) on smartphones and upload the GTPs to the cloud for information sharing. However, uploading
GTPs consumes network bandwidth and battery energy, and the uploaded GTPs often contain
redundant or inaccurate information, due to factors such as blocking of GPS signals and stalling in
user movements. To address the problem, we present in this thesis the corner extraction by GPS
filtering (CEGF) technique that extracts corner feature GPS points (CFGPs) from GTPs. CFGPs
are characteristic corners representing the corresponding roads. Applications only need to upload
the CFGPs to save the uploading energy on smartphones. The challenging problem is how to filter
out non-representative GTPs and determine when the roads turn. Our experimental results show
that CEGF can efficiently filter a large number of GPS track data into CFGPs and accurately represent
the road sections. Furthermore, CEGF can save up to 87.7% of battery life time on a Samsung
smartphone.

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