臺灣博碩士論文加值系統

近年來， 隨著物聯網的發展備受關注，各項建築物已漸漸走向“智慧化”，室內定位系統(IPS)的需求增長迅速，以增強居住者的便利性和安全性，因此運用藍牙低功耗(BLE)無線通訊技術建構一移動定位服務(LBS)，將變得真實可靠。然而，目前該技術仍存在著許多挑戰，尤其是廣播頻道的行為與多徑效應導致的接收信號強度指示器(RSSI)波動，進而導致定位精度差。為了減輕這些影響，本論文提出一種基於BLE藍牙無線技術之室內定位系統，將可提高精度並同時降低功耗和成本，主要技術是透過程式規劃與設計，將物與物之間有效地連結來構成一位基網型網路(LBMN)並實現於人與物之間以及事物(設備)之間的交互式通信，同時，進一步結合穿戴式手環、演算法和系統設計等方法，通過結果的分析證明，以上技術提高了系統的精度，對於在北科大的環境(45m×31m)中部屬了至少間隔2.5 m的網格點，定位精準度可以達到1.5 m。此外，本文已經證明該系統在效率與功能方面具有可發展性和高效性並且精度優於現有解決方案。

Internet of Things(IoT) has grasped great attention in recent years. Each constructed buildings tend to be “intelligent” to enhance the convenience and safety of occupants. Therefore, Indoor Positioning System (IPS) using Bluetooth Low Energy (BLE) technology to construct Location Based Service(LBS) is currently becoming real and available. However, this technology exists various challenges related to the behavior of the advertising channel 37、38、39 and the multipath effect. Because of these factors, they will lead to the Received Signal Strength Indicator (RSSI) fluctuations and precision.
In order to mitigate these effects, this paper presents an IPS based on BLE, that improves accuracy while reducing power consumption and costs. The main point of this technique is that using PeTacom Protocol to establish Location Based Mesh Network (LBMN) and realize interactive communication between people to objects. Simultaneously, the system integrates various sensors, algorithms, and system design to establish an IPS. The analysis of the results proves that all the proposals improve the precision of the system, which precise goes up to 1.5 m for moving in area 45m×31m for devices.

摘　要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究方法與步驟 3
1.4 文獻回顧 4
1.4.1 無線技術優劣 4
1.4.2 基於低功耗藍牙室內定位 6
1.4.3 BLE Mesh Network 8
1.5 論文架構 10
第二章 藍牙技術簡介 11
2.1 藍牙技術 11
2.2 BLE協議棧 12
2.3 GAP 13
2.4 GATT 14
第三章 系統開發環境介紹 16
3.1 CC2640 / CC2640R2F晶片介紹 16
3.2 軟硬體開發環境 17
3.2.1 藍牙晶片 17
3.2.2 Node模組 21
3.2.3 Router模組 22
3.2.4 移動式Tag 23
3.2.5 固定式Tag 23
第四章 系統設計 25
4.1 前言 25
4.2 系統架構 25
4.2.1 藍芽端 26
4.2.2 設定端： 28
4.2.3 雲端資料庫與網頁： 28
4.3 廣播資料格式 29
4.4 信號傳播模型 34
4.5 室內定位方法 36
4.5.1 歐幾里得距離 36
4.5.2 三點定位方法 37
4.5.2 兩點定位方法 41
4.5.3 單點定位方法 42
4.6 Mesh Network System 43
4.6.1 初始設定 43
4.6.2 燈源裝置Node上傳 44
4.6.3 燈源裝置Router上傳與下載 47
4.6.4 燈源裝置Node下載 48
4.6.5 BLE裝置報平安 50
4.7 穿戴式手環 51
4.7.1 系統架構 51
4.7.2 手環演算法 52
4.7.4 穿戴式手環(回傳) 54
4.8 固定式感測器 55
4.8.1 智慧坐墊 55
4.8.2 磁簧開關 56
4.8.3 煙霧感測器 58
第五章 系統功能與結果驗證 59
5.1 RSSI分析 59
5.1.1 RSSI穩定度實驗結果 59
5.1.2 人體遮蔽對於RSSI影響 61
5.1.3 廣播通道的多樣性行為對於RSSI造成之影響 63
5.2 估算路徑損耗指數 65
5.3 物聯網系統實驗分析 67
5.3.1 BLE設備初始設定 67
5.3.2 物聯網系統上傳與報平安 69
5.3.3 物聯網系統下載與控制 70
5.3.4 物聯網系統傳輸效率 73
5.4 穿戴式手環實驗分析 75
5.4.1 單點定位 75
5.4.2 兩點定位 77
5.4.3 三點定位 78
5.5 固定式感測器實驗 80
5.5.1 坐墊行為判斷 80
5.5.2 磁簧開關觸發判斷 82
5.5.3 煙霧感測器 83
第六章 結論與未來展望 85
6.1 結論 85
6.2 未來展望 86
參考文獻 87

1.Gupta, A., Tsai, T., Rueb, D., Yamaji, M. and Middleton, P., Forecast: Internet of Things–Endpoints and Associated Services, Worldwide, Dec. 2017.
2.J. A. Stankovic, "Research Directions for the Internet of Things,", IEEE Internet of Things Journal, vol. 1, no. 1, 2014, pp. 3-9.
3.張志勇、陳正昌，「物物相聯的龐大網路-物聯網」，科學月刊，第四十五卷，第六期，2014，第419-427頁。
4.Pu, Q., Gupta, S., Gollakota, S. and Patel, S., "Whole-Home Gesture Recognition Using Wireless Signals," Proceedings of the 19th annual international conference on Mobile computing & networking, Hong Kong, 2013, pp. 27-38.
5.Wang, Y., Liu, J., Chen, Y., Gruteser, M., Yang, J. and Liu, H., "E-eyes: Device-free Location-oriented Activity Identification Using Fine-grained WiFi Signatures," Proceedings of the 20th annual international conference on Mobile computing and networking, Maui Hawaii, 2014, pp. 617-628.
6.Lin, X.Y., Ho, T.W., Fang, C.C., Yen, Z.S., Yang, B.J. and Lai, F. "A mobile indoor positioning system based on iBeacon technology," Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan Italy, 2015, pp. 4970-4973.
7.Yang, Z., Zhou, Z. and Liu, Y., "From RSSI to CSI: Indoor Localization via Channel Response," ACM Computing Surveys (CSUR), vol. 46, no. 2, 2013, pp. 25.
8.Capriglione, D., Casinelli, D. and Ferrigno, L., "Use of frequency diversity to improve the performance of RSSI-based distance measurements," Proceedings of the 2015 IEEE International Workshop on Measurements & Networking (M&N), Coimbra Portugal, 2015, pp. 1-6.
9.Lin, D.，「【物聯網時代】無線通訊，一百公尺的連線戰爭」，科技新報，臺北，2014。
10.Yamaguchi, S., Arai, D., Ogishi, T. and Ano, S., "Short paper: Experimental study of long-term operation of BLE tags for realizing indoor location based service," In Proceedings of the 2015 18th International Conference on Intelligence in Next Generation Networks, Paris France, 2015, pp. 136-138.
11.蕭佑和，「群雄並起-室內定位技術大比拚」，網頁，臺北，2018。
12.Choi, M., Park, W.K. and Lee, I., "Smart office energy management system using Bluetooth low energy based beacons and a mobile app," In Proceedings of the 2015 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas the USA, 2015, pp. 501-502.
13.A. Ozer, E. John, "Improving the Accuracy of Bluetooth Low Energy Indoor Positioning System Using Kalman Filtering, " In Proceedings of the 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 15-17 December 2016.
14.J.W. Qiu, C.P. Lin, Y.C. Tseng, "BLE-based collaborative indoor localization with adaptive multi-lateration and mobile encountering, " In Proceedings of the 2016 IEEE Wireless Communications and Networking conference, " Doha, Qatar, 3-6 April 2016.
15.A. Filippoupolitis, W. Oliff, B. Takand, G. Loukas, "Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons, " Sensors, vol. 17, no. 1230, 2017.
16.G.D. Blasio, A. Quesada-Arencibia, C.R. García, J.M. Molina-Gil, C. Caballero-Gil, " Study on an Indoor Positioning System for Harsh Environments Based on Wi-Fi and Bluetooth Low Energy, " Sensors, vil. 17, no.299, 2017.
17.V. C. Paterna, A. C. Augé, J. P. Aspas and M. A. P. Bullones, "A Bluetooth low energy indoor positioning system with channel diversity weighted trilateration and Kalman filtering," Sensors, vol. 17, no. 12, 2017.
18.J. Röbesaat, P. Zhang and M. Abdelaal, O. Theel, "An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study," Sensors, vol. 17, no. 5, 2017, pp. 951.
19.Bluetooth SIG, " Mesh Profile Specification: 1.0," Bluetooth Special Interest Group: Kirkland, USA, 2017.
20.J. Rossey, A. Shahid and J. Hoebeke, "The Bluetooth Mesh Standard: An Overview and Experimental Evaluation," Sensors, vol. 18, no. 8, 2018, pp. 2409.
21.Lin, Y.W. and Lin, C.Y., " An Interactive Real-Time Locating System Based on Bluetooth Low Energy Beacon Network," Sensors, vol.18, no.5, 2018, pp. 1637.
22.Microchip, Bluetooth Low Energy Link Layer Overview.
http://microchipdeveloper.com/wireless:ble-link-layer-overview
23.K. Townsend, "Introduction to Bluetooth Low Energy," 2014.
24.Bluetooth Core Specification Version 4.2.
https://www.bluetooth.com/specifications/bluetooth-core-specification/
25.K. Mariusz, R. Jacek and B. Adam, " Accuracy analysis of the RSSI BLE Sensor Tag signal for indoor localization purposes," In Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk Poland, 2016.
26.廖紘毅，物聯網應用於室內照護與監控，碩士論文，國立臺北科技大學機電整合研究所，臺北，2015。