物聯網(Internet of Things, IoT)是近期非常重要也受到高度關注的議題，主要訴求是「萬物聯網」，就是將全球物件以及各式各樣的感應器也連上網路，除了數據收集與資料分析外，也可以回饋進行各種控制。低功耗廣域網路(LPWAN)技術為了滿足物聯網需求，應運而生，專門針對低功耗、遠距離、大量連接的物聯網路應用。這些技術典型應用場景是在城市高密度的進行數據收集。
LPWAN技術基本上是讓End-Device能透過1 hop，就將數據傳送到Gateway的星狀拓撲，這種架構能夠讓我們更有效地建立物聯網。LPWAN可分為兩類，一種為工作於未授權頻譜(ISM)，如LoRa、SigFox等技術；另一種為工作於授權頻譜下，如LTE-M 、NB-IoT等。其中，我們選擇目前最受到注目的LoRa技術進行探討。LoRa目前有一個稱為LoRaWAN的官方建議網路協定，但LoRaWAN是個不完整的LoRa協定，它只定義了MAC層(OSI模型第二層)和一些網路層(OSI模型第三層)的部分。由於LoRa使用未授權頻譜(ISM)進行傳輸，因此傳輸受到Duty Cycle 1%、傳輸功率的限制，傳輸的效率類似於pure ALOHA。目前的LoRaWAN Specification尚為1.0.2版 ，MAC層的設計並未周全，應對其擴充性方法多加描述。
於是，我依照LoRaWAN Specification 1.0.2 和官方Regional Parameters文件，推導LoRaWAN中一個Gateway的網路容量，再使用若干方法，測試其擴充性。我的目標為提供一個可以增加網路容量的機制，透過讓End-Device使用Adaptive Data Rate的方式，可讓封包傳輸時間縮短，使原本接近滿載的LoRaWAN封包碰撞率降低，因而提高網路容量。
本論文以Python建立了模擬器以模擬LoRa網路，評估和分析LoRaWAN網路的性能。然後，引入「快速調整SF方法」，來評估性能。

In recent years, Internet of Things (IoT) has attracted great attention, therefore leading to the imperative need for citywide, high-density meter reading. In order to improve the connection of a wide variety of sensors to the network, Low-Power Wide-Area Network (LPWAN) has been developed rapidly.
LPWAN is a star-like topology that allows effective data transmission from End-Device to Gateway through single-hop. LPWAN can be divided into two categories: one is for the unauthorized spectrum (ISM), such as LoRa and SigFox; the other is for the authorized spectrum, such as LTE-M and NB-IoT. In this study, LoRa is selected for research. LoRaWAN is currently the official protocol for LoRa. Howerver, LoRaWAN is an incomplete LoRa protocol, which defines only the MAC layer and some of the network layer. Since LoRa uses ISM for transmission, transmission is limited by 1% of duty-cycle and transmission power. In addition, the transmission efficiency of LoRaWAN is similar to pure ALOHA.
In accordance with LoRaWAN Specification 1.0.2 and the official Regional Parameters file, we estimated the network capacity of a Gateway in LoRaWAN, and then used several methods to test its scalability. Our goal is to provide a mechanism that increases network capacity by applying the Adaptive Data Rate (ADR) to End-Device, which effectively decreases packet transmission time and collision rate.
This paper first simulates LoRa networks based on the simulator developed in Python, and then analyzes the performance of LoRaWAN networks, and finally evaluates the performance of "The Method of Quick SF Adjustment".

Abstract III
Figure List VII
Table List IX
Chapter 1 Introduction 1
Chapter 2 Related Works 4
2.1 LoRa 4
2.1.1 LoRa Packet Structure 5
2.1.2 Airtime 7
2.1.3 Communication Range 8
2.1.4 Collision Behaviour 9
2.2 Main LoRa Chips 10
2.3 LoRaWAN 12
2.3.1 Class A 15
2.3.2 Class B 16
2.3.3 Class C 16
2.4 Regional Parameters 17
Chapter 3 System Design and Implementation 19
3.1 LoRa Network Design 20
3.1.1 Basic LoRaWAN 20
3.1.2 LoRaWAN with Optimized SF 21
3.1.3 LoRaWAN with Downlink 22
3.1.4 LoRaWAN with Adaptive Data Rate 22
3.1.5 LoRaWAN with "Quick SF Adjustment" 25
3.2 LoRa Network Implementation 28
3.2.1 Input and Output 29
3.2.2 Airtime Calculation 33
3.2.3 Calculate Strength of the Signal 33
3.2.4 Collision Detection 34
3.2.5 Channel Access 35
3.2.6 Quick SF Adjustment 36
Chapter 4 Experimental Results 38
4.1 The Effect of Downlink on LoRaWAN 38
4.2 The Effect of the ADR on LoRaWAN 40
4.3 The ADR Under High-Loading Conditions 43
4.4 The Performance of "Quick SF Adjustment" 46
Chapter 5 Conclusion and Future Works 54
Reference i

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