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博碩士論文 etd-0712112-185739 詳細資訊
Title page for etd-0712112-185739
論文名稱
Title
運用機器學習於車載網路內之路徑選擇
Using Machine Learning for Routing Path Selection in VANET
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
65
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2012-07-06
繳交日期
Date of Submission
2012-07-12
關鍵字
Keywords
車載網路、機器學習、路側單元、路由協定、傳輸延遲
Routing Protocol, Transmission Delay, VANET, RSU, Machine Learning
統計
Statistics
本論文已被瀏覽 5695 次,被下載 909
The thesis/dissertation has been browsed 5695 times, has been downloaded 909 times.
中文摘要
在車載網路(VANET)的高度變異性環境中,由於車輛的高移動性與周遭建築物的阻礙,使得車輛之間的傳輸穩定性受到相當的挑戰。在車載網路中進行路由計算時,須盡可能尋找車輛密度較高的路段進行傳輸,以避免carry-and-forward之情形發生。目前已有許多路由機制是透過偵測各路段的車輛密度,以作為其選擇傳輸路徑的考量。然而,在車輛密度變化快的情形下,單純週期性偵測各路段的車輛密度無法獲得即時的密度資訊。

本論文提出一套新的路由機制,透過路側單元(Road-side Unit, RSU)的參與,以及機器學習(Machine Learning)系統來維護道路資訊。透過Machine Learning來對車輛密度的變化進行預測,並延伸至各路段車流移動的預測,藉此建構完整且即時的道路交通系統,提供車載網路完善的路由參考資訊。經由模擬結果顯示,相較於其他比較的方法,我們所提出的方法在各項評測項目上皆有較佳的效能表現。
Abstract
none
目次 Table of Contents
第一章 導論 1
1.1 簡介 1
1.2 研究動機 5
1.3 論文架構 7
第二章 相關背景與研究 8
2.1 車載網路 8
2.2 專用短距通訊 12
2.3 路由協定介紹 16
2.4 機器學習於無線網路之應用 21
第三章 協定設計 23
3.1 情境假設 24
3.2 路由流程 26
3.3 道路交通流量預測系統 31
3.3.1 車輛移動預估 33
3.3.2 傳輸能力判斷 35
3.4 傳輸延遲時間估測機制 37
3.5 路由協定流程圖 40
第四章 模擬與效能分析 41
4.1 模擬環境與參數設定 41
4.2 評測項目 44
4.3模擬結果與分析 45
第五章 結論 55
參考文獻 56
參考文獻 References
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