臺灣博碩士論文加值系統

本論文將探討如何運用Raspberry Pi 樹莓派組合成 Pi beacon,取得訊號強 度(rssi),實驗的系統包括利用四個Raspberry Pi組成的Pi beacon及一顆estimote iBeacon低功耗藍牙裝置,並且將實驗室座標化.在實驗室沒有障礙物的地方 規劃出84個座標點,在每一個座標點對四個Raspberry Pi 組成的Pi beacon及一 顆estimote iBeacon逐一收集資料,並且使用高斯混合模型(GMM)和多層感知機(MLP)進行預測,比較哪種方法錯誤率較低.代表哪種方法辨識度較高,比較 適合室內定位.
本論文首先介紹室內定位和低功耗藍牙以及ibeacon,然後說明用Raspberry Pi來做藍芽的發送和接收器的動機,並介紹高斯混合模型(GMM)和多層感知機(MLP),以及實驗流程和最後的結果.


關鍵詞 : 樹莓派，低功耗藍牙裝置，高斯混合模型，多層感知機

This paper will discuss how to use the Raspberry Pi to create a low-power Bluetooth device (iBeacon) to obtain signal strength (RSSI). The system under consideration includes four low-power Bluetooth devices built by the Raspberry Pi and an esti- mote iBeacon and in the lab Eighty-four coordinate points were generated without obstacles. Data were collected one by one at each coordinate point for the low-power Bluetooth devices built by four Raspberry Pi’s and one estimote iBeacon. However, machine learning method-based neural network and the famous Gaussian Mixture model(GMM) were used to perform prediction on the data. Comparing the models in terms of error rate, MLP produced the lowest with 3%.
This paper first introduces the Raspberry Pi and iBeacon and explains the motiva- tion of using the Raspberry Pi as a Bluetooth sender and receiver and introduces the machine learning methods and neural networks used, as well as the experimental flow and results.





Keywords: Raspberry Pi, iBeacon, GMM, MLP

書名頁 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
論文口試委員審定書........................................................... ii
授權書.............................................................................. iii
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..... . . . . . vii
英文摘要 .............................................................................viii
誌謝...................................................................................... ix
目錄....................................................................................... x
表目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..... . . . . . xii
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..... . . . xiii

一、序論 ................................................................................ 1

1.1 研究動機與背景............................................................. 1
1.2 研究概述 ...................................................................... 1
1.3 論文大綱 ...................................................................... 1

二、文獻探討........................................................................... 2

2.1 iBeacon介紹................................................................. 2
2.2 室內定位 ...................................................................... 4
2.3 低功耗藍牙技術............................................................. 4
2.4 RSSI............................................................................ 7
2.5 三角定位法(Triangulation)............................................. 8

三、機器學習方法 .................................................................. 9

3.1 神經網路 ..................................................................... 10
3.2 GMM .......................................................................... 11
3.2.1 建立高斯混合模型 ............................................... 11
3.3 MLP............................................................................. 12

四、系統設計與實作............................................................... 16

4.1 RaspberryPi ............................................................... 16
4.1.1 硬體介紹 ............................................................ 17
4.1.2 軟體介紹 ............................................................ 18
4.2 PiBeacon.................................................................... 19
4.3 實驗設計 ..................................................................... 22
4.4 PiBeacon V.S. iBeacon ............................................... 23

五、實驗分析.......................................................................... 26

5.1 實驗設備 ..................................................................... 26
5.2 實驗流程 ..................................................................... 27
5.3 模擬環境 ..................................................................... 27
5.4 研究成果 ..................................................................... 29
5.4.1 高斯混合模型....................................................... 31
5.4.2 多層感知器 ......................................................... 37
5.5 機器學習比較方法 ....................................................... 48

六、結論 .............................................................................. 50

參考文獻 ............................................................................... 51

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