臺灣博碩士論文加值系統

隨著物聯網技術高度發展，相關應用也愈來愈廣泛及多元，Gartner 預測到 2020 年，透過物聯網(IoT)相連的裝置將高達 260 億台，其中又以汽車物聯 網的成長速率最快。物聯網的概念是將每個可以聯網的裝置進行連接，彼此之 間共享資訊，以達到資訊共享的目的，並為資料分析與機器學習應用開創新的 應用領域。本篇論文以現行建置在各大路口的紅綠燈為基礎，在紅綠燈上安裝 必要的物聯網裝置，針對上下班時段或因交通事故所造成的交通壅塞狀況，以 及假日節慶可能湧現的大量車潮，提出一個「以軟體定義網路為基礎的智慧交 通控制平台」。在網路管理部份，我們擬藉助軟體定義網路(Software Defined Networking，SDN)監控、設定與管理的優勢，應用於紅綠燈號誌管理，以期 達到動態調節紅燈與綠燈時脈，紓解車流量，降低平均行車時間的目的。系統 整體架構分為交通資訊的中控端，即 SDN 的控制平面(control plane)，與靠 近駕駛人的紅綠燈端，亦即SDN的資料平面(data plane)。控制平面直接和雲 端模組連接，負責計算及監測大範圍區域中紅綠燈與車流量的狀態，當車流量 大時啟動協同管理模式，將許多小區域的紅綠燈群組起來並做協調。資料平面 和霧計算模組連接，主要針對小區域中的紅綠燈做時脈調控。紅綠燈上的物聯 網裝置負責蒐集道路上車輛的行車資訊，透過軟體定義網路為基礎的智慧交通 控制平台，結合無線通訊、軟體定義網路及雲與霧計算等技術，並透過提出的 演算法達到紅綠燈自適應調配的目標，進而紓解壅塞的車流，減少平均行車時間。
在智慧交通控制平台中，實驗結果顯示所提出的紅綠燈號誌演算法，可有 效降低行車時間最高達79.7%，且在100公尺x 100公尺的模擬地圖中，隨著車 輛數由 100 輛車增加到 600 輛車，能有效減少等待紅燈的時間，相對縮短了整 段路程所需要的行車時間。當單位面積車輛數增加時，愈能顯示該演算法所發 揮的效益。

With the trend of the rapid development of the Internet of Things (IoT), the
related applications are becoming more and more diverse. Gartner predicted that by 2020, there are up to 26 billion devices connected via the IoT devices. Among these, one major part is the automotive IoT devices. The concept of the IoT is to connect every device that can be connected, share information, to achieve the purpose of information sharing, and open up new application areas for data analysis and machine learning. This research is based on the assumption that there are IoT devices embedded in the traffic lights at road intersections. An "SDN Based Smart Traffic Control Platform" is proposed for the traffic congestion which is caused by traffic accidents or a large number of traffic flows that may arise during holiday festivals. In the part of network management, we apply Software Defined Networking (SDN) to traffic light management based on its underlying advantages like monitoring, setting, and management, to dynamically adjust the timing of stop light and thus reduce the average travel time. The system structure consists of the central control of the traffic, which is the control plane of the SDN, and the traffic lights close to the vehicles, which is the data plane of the SDN. The IoT devices on the traffic lights are responsible for collecting information from the vehicles. This kind of information is exchanged through IoT devices and SDN.
In summary, this research proposed an "SDN Based Smart Traffic Control Platform" that combines wireless communication, software-defined networking, and other relevant technologies together with the proposed algorithm to effectively relieve traffic congestion and reduce average travel time. Experiment results show that the proposed algorithm is able to reduce the moving time up to 79.7%. With the number of vehicles increased from 100 to 600 on an 100m x 100m simulating environment, the waiting time for red lights can be largely reduced.

第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目標 2
1.4 論文架構 4
第二章 相關研究 5
2.1 物聯網(Internet of Things，IoT) 5
2.2 軟體定義網路(Software Defined Networking，SDN) 5
2.2.1 OpenFlow 7
2.2.2 Ryu Controller 8
2.3 雲計算(Cloud Computing) 9
2.4 霧計算(Fog Computing) 9
2.5 車載網路(Vehicular Ad-hoc NETwork , VANET) 12
2.6 交通控制理論 13
第三章 研究方法 14
3.1 問題分析 14
3.1.1 大數據與系統負載 14
3.1.2 系統即時性服務 14
3.1.3 多元化車載網路 15
3.1.4 網路頻寬分配 15
3.2 方法論 16
3.2.1 系統架構 16
3.2.1.1 智慧路由 17
3.2.1.2 智慧運算 19
3.2.2 系統功能模組 20
3.2.2.1 號誌變換模組 20
3.2.2.2 運算相關參數 21
3.2.3 交通號誌變換演算法 23
第四章 模擬實驗與結果分析 33
4.1 實驗環境 33
4.1.1 SDN 環境架設與設定 34
4.1.2 伺服器群架設與設定 36
4.1.3 VanetSimulator 車載模擬器相關設定 36
4.1.4 實驗環境介接 39
4.2 實驗結果與分析 43
4.2.1 實驗參數 43
4.2.2 實驗一 :比較不同密度下，旅行時間與等待時間之關係 43
4.2.3 實驗二 :比較有/無演算法，旅行時間長短之關係 48
4.2.4 實驗三 :比較不同網路架構下，傳送封包所需的流量 49
第五章 結論與未來研究 53
5.1 結論 53
5.2 未來研究 53
參考文獻 55

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