近年來，以病人為中心的無線醫療環境日漸重要。它不只提供了病人的生理資訊，也為病患節省了到醫院做健康檢查所花費的時間，進而延續病患的生命及節省醫院的醫療成本，透過病患發生緊急狀況時，即時知道病患的位置，我們將可以避免不必要的意外發生。

在這篇論文中，我們提出室內定位N-Cluster-k-NN演算法，透過隨處可見無線網路接入點來做為定位的訊號來源以節省硬體設備上面的成本。本篇論文第三章中，透我們撰寫的安卓應用程式，可以讓使用者一鍵快速安裝並迅速的設定演算法的參數，同時也可以記錄前三點的歷史位置，並且結合了雲端伺服器的功能，可以即時在網頁得知使用者的位置。

本篇論文第四章我們實際在實做N-Cluster k-NN及N-k-NN 透過模擬得到的最佳參數進行了手機上面的實測，測試環境是在清華大學台積館四樓的LaRC實驗室。根據模擬的結果，我們的演算法N-Cluster k-NN比N-k-NN的複雜度較低，而精確度可達98.67％。不僅保有了低的複雜性同時不影響計算的準確性。在大小為227.59平方公尺的地方下，整個空間被分成12個位置。並用最佳參數去實際測試，我們的算法平均錯誤率是4.75%，平均錯誤距離為3.728公尺。與其它設計相比，我們的演算法不僅可以有低的複雜度，精確度在實測結果上也達到95.25%，並且可以實際即時的定位。

本篇論文在第五章的部分我們實際把系統運行在醫院裡面進行模擬與實作。在大小為134.04平方公尺，我們的演算法平均錯誤率實測結果為5.75％，平均錯誤距離3.328公尺。即使在不同的環境中，一樣可以有不錯的精確度。此外，複雜度跟其他系統相比也相對減少了三分之一。有了這樣的精確度和便攜性，每一位病人的透過安卓手機即時發送到雲端伺服器，讓醫生及病患家屬可以透過網頁充分了解他們的位置。

Recently, the patient-centric wireless medical environment has become more and more important. It not only provides patients’ physiological information, but also helps to save time on taking physical examinations in hospitals, which would further extend patients’ life span without increasing the medical cost. Therefore, we took advantage of Android smart phones and developed a ”Portable Healthcare System”. When emergencies occur, the patients can be traced by the devices and be rescued as soon as possible.

In this thesis, we propose a novel indoor positioning method deploying WiFi access points (APs) called N-Cluster k-NN algorithm, which does not cost extra money for infrastructures and still offers decent accuracy comparing to other indoor positioning techniques. According to the offline simulation, the complexity of N-Cluster k-NN is low, while the accuracy can be up to 98.67%. In brief, the complexity and the calculation are greatly decreased, while the accuracy is still maintained.

In chapter 3 and 4, we’ll talk about the Android-based platform and the Android application with intuitive user interface and quick access to changing parameters, in order to briefly demonstrate our result to determine the location of the users in real time. By this Android application, we’ll introduce the implementation of the online positioning. Online positioning was tested under N-Cluster k-NN with the optimal parameters obtained from offline simulations. The experimental environment was the Laboratory of Reliable Computing (LaRC) on the fourth floor in the TSMC building in National Tsing Hua University (NTHU). The whole environment was equally divided into twelve grids, which is about the size of a normal ward. With the background dimension in 227.59 m2, the average error rate of our algorithm is 4.75%, and the average error distance is 3.728 m. Compared to other designs, the accuracy of our algorithm does not differ much.

In chapter 5, we further applied the algorithm above in National Taiwan University Hospital (NTUH). With the background dimension in 134.04 m2, the average error rate of our algorithm is 5.75%, and the average error distance is 3.328 m. Even in a different environment, the system is able to achieve a comparable result. Moreover, the complexity in NTUH is about a third less than it was in the LaRC. With such accuracy and portability, precise positions of every patient are sent to the cloud server and computed in real time, which enables doctors to be fully informed by their mobile handsets, with the minimum energy consumption and the longer duration the device can stand by.

Abstract i
1 Introduction 1
1.1 Backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Introduction to Positioning . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Review of Healthcare Systems . . . . . . . . . . . . . . . . . . . . . 4
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Recent Developments about Positioning 7
2.1 Global Positioning System (GPS) . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Overview of WiFi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 IEEE 802.11 Standard [1] . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Algorithms for Location Determination via WiFi . . . . . . . . . . . . . . . 11
2.3.1 Angle of Arrival (AOA) [2] . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Time of Arrival (TOA) [3] . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.3 Time Difference of Arrival (TDOA) [4] . . . . . . . . . . . . . . . . 13
2.3.4 Received Signal Strength Indicator (RSSI) [5] . . . . . . . . . . . . . 15
2.4 Selection of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.1 Clustering and Probability Distribution Algorithm . . . . . . . . . . 15
2.4.2 Nearest Neighbor Mean Algorithm (NNM) [6] . . . . . . . . . . . . 17
2.4.3 k Nearest Neighbor Algorithm (k-NN) [6] . . . . . . . . . . . . . . . 17
2.5 Radio Map Construction [7] . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5.1 Propagation Path Loss Model . . . . . . . . . . . . . . . . . . . . . 18
2.5.2 Large Scale Propagation Model . . . . . . . . . . . . . . . . . . . . 21
2.5.3 Small Scale Propagation Model . . . . . . . . . . . . . . . . . . . . 22
2.5.4 Selection of Path Loss Model [8] . . . . . . . . . . . . . . . . . . . 22
2.6 RSSI-based Positioning Algorithms . . . . . . . . . . . . . . . . . . . . . . 23
2.6.1 Sorting RSSI Data by the Most Robust Access Point . . . . . . . . . 23
2.6.2 The Concept of NNM Algorithm . . . . . . . . . . . . . . . . . . . . 24
2.6.3 Optimized k Value and Training Data Number . . . . . . . . . . . . 24
3 Experimental Platform and Related Techniques 25
3.1 Android Application Developing . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.1 Software Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.2 Development Environment [9] . . . . . . . . . . . . . . . . . . . . . 28
3.1.3 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Synchronization with Patient-Centric Medical Cloud . . . . . . . . . . . . . 30
3.2.1 Real-time Location Information Recording on PC . . . . . . . . . . . 30
3.2.2 Applications on Android Smartphones . . . . . . . . . . . . . . . . . 30
3.2.3 Applications on PC . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.4 Xoops System [10] . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.5 Real-time Positioning Website . . . . . . . . . . . . . . . . . . . . . 35
4 Positioning in the General Experimental Environment 41
4.1 Block Diagram of the Proposed Algorithm . . . . . . . . . . . . . . . . . . . 41
4.2 Positioning in the Environment in NTHU . . . . . . . . . . . . . . . . . . . 42
4.2.1 Offline Training Phase . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.2 Discussion about the Number of Training Data . . . . . . . . . . . . 44
4.3 Discussion about N-Cluster k-NN and N-k-NN . . . . . . . . . . . . . . . . 46
4.3.1 Discussion about the Parameter Sets of N-Cluster k-NN and N-k-NN 46
4.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4.1 Simulation Results of N-Cluster k-NN and N-k-NN . . . . . . . . . . 47
4.5 Implementation on an Android Smartphone . . . . . . . . . . . . . . . . . . 48
5 Positioning in NTUH 53
5.1 Block Diagram of the Proposed Algorithm . . . . . . . . . . . . . . . . . . . 53
5.2 Positioning in the Environment in NTUH . . . . . . . . . . . . . . . . . . . 54
5.2.1 Offline Training Phase . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.2.2 Discussion about the Number of Training Data . . . . . . . . . . . . 55
5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.3.1 Implementation on an Android Smartphone . . . . . . . . . . . . . . 57
5.4 Comparison with Other Based Algorithms . . . . . . . . . . . . . . . . . . . 59
6 Future Works and Conclusion 61
6.1 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

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