臺灣博碩士論文加值系統

近年物聯網(IoT)的技術蓬勃發展，隨著生活中已有許多普及化的智慧應用，人們對於更大範圍與更低功耗之網路技術需求也不斷增長。在低功耗廣域網路(LPWAN)無線通訊技術中最常使用LoRa網路系統作為輔助物聯網來實現不同應用中裝置間的傳輸。在物聯網的智慧應用中，為有效利用收到的訊息作更深入運用，常需要結合定位技術獲取其裝置的位置資訊。過去多以高定位精確度的全球定位系統(GPS)作為市場上定位的主要技術，但由於GPS使用時功耗大，加上裝置中安裝GPS成本高。過去GPS常作為導航之應用，對於有些不需要即時追蹤及高精準度的定位應用，如工作人員、學生或訪客於不同廠區(校園)間活動與停留時間或車輛停放不同停車場等之安全資訊記錄，使用GPS的成本相對太高。為了降低應用中需要定位的使用成本，本文選擇使用屬於LPWAN中的LoRa系統在既有設備下不需要額外增加硬體成本，利用LoRa設備間傳遞資料時的RSSI值估算其對應之距離進行定位。但由於LoRa低功率的特性，量測到的RSSI值對於距離變化之靈敏度不高。本文參考過去本實驗室提出ZigBee之RSSI循環收集法，建立屬於LoRa系統之環境通道模型曲線。並提出環境通道模型曲線修正法來解決量測到的RSSI值與對應距離的誤差變動，建立有效環境通道模型曲線。接著利用此曲線使用三邊定位法進行定位距離估算，並針對實際實驗中估算待測節點與參考節點間距離產生之誤差，使用過去本實驗室提出的自我調整誤差法進行修正得出待測節點位置。最後針對不同誤差將定位區域分為不同誤差容忍度，提供未來不同定位誤差需求之有效定位面積估算。

Due to the technology of the Internet of Things (IoT) has flourished in recent years, there are many popular applications in different fields such as smart life. The development of network technology is growing for the trend toward wider coverage range and lower power consumption. LoRa system is one of the most considered use in Low Power Wide Area Network (LPWAN) as an auxiliary role of IoT to assist the transmission between devices in various applications. In order to make more received information for the smart applications of IoT, it often needs to increase the positioning technology to obtain the location information of the devices. The Global Positioning System (GPS) with the high positioning accuracy is the most popular choice for positioning on the markets. Meanwhile, the expensive hardware cost and the high power consumption are existed. Therefore, GPS are often used for navigation applications. In contrast to the cost, GPS is not valuable for applications that do not require real-time tracking and high accuracy of positioning, such as to record the stay duration and place of the staff, students or visitors to be present at their factories (campus) or to record the vehicle information of parking duration and its parking lot. In such cases, we choose LoRa system which belongs to the LPWAN to estimate the end-device location by RSSI value when it transmits data between LoRa gateway without extra equipment. Indeed, due to the low-power feature of LoRa, the RSSI is not sensitively varied to its distance change. Refer to the ZigBee RSSI cycle collection method proposed in our previous study, we first create a related curve between RSSI and distance for the environment where exists LoRa system, named channel model. To observe the curve of the channel model, it is not a monotone decreasing function but a vibration decreasing one. The curve is hard to estimate a reasonable distance. We then propose a channel model curve correction method to solve the variation error of the original measured curve, and name it as an effective environmental channel model curve. Furthermore, the modified curve is adopted to estimate the location of the end-device by using the trilateral positioning method with the self-adjustment error method proposed in our previous study to improve the error between the estimated and actual position of the node (end-device). Finally, the total coverage of positioning is classified into different areas of error levels by using the statistical results. In the future, we can decide the suitable positioning size to perform positioning applications according to the provided different error levels.

摘要……………i
Abstract……………ii
誌謝……………iv
目錄……………v
表目錄……………vii
圖目錄……………viii
第一章 緒論……………1
第二章 相關研究……………4
2.1 LoRaWAN簡介……………4
2.2 LoRa硬體設備……………8
2.3 自我調整誤差法……………10
第三章 環境通道模型之建立……………12
3.1 系統架構……………12
3.2 環境通道模型……………16
3.3 環境通道模型曲線修正法……………22
第四章 實驗結果及討論……………25
4.1 參考節點及待測節點擺放位置與定位估算結果……………25
4.2 定位估算誤差討論……………31
第五章 結論……………37
參考文獻……………38
Extended Abstract

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