研究生: |
魯家宏 Lu, Jia-Hong |
---|---|
論文名稱: |
稀疏的快速道路上訊息傳遞之路由選擇 The Routing of Messages in a Sparse Highway VANET |
指導教授: |
黃政吉
Huang, Jeng-Ji |
口試委員: |
熊大為
Shiung, David 梁耀仁 Liang, Yao-Jen 黃政吉 Huang, Jeng-Ji |
口試日期: | 2022/06/30 |
學位類別: |
碩士 Master |
系所名稱: |
電機工程學系 Department of Electrical Engineering |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 中文 |
論文頁數: | 53 |
中文關鍵詞: | 車載網路 、修復延遲 、網路斷開 、嵌入式馬可夫鏈 |
英文關鍵詞: | VANET, repair delay, network disconnection, embedded Markov chain |
DOI URL: | http://doi.org/10.6345/NTNU202201343 |
論文種類: | 學術論文 |
相關次數: | 點閱:71 下載:3 |
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車輛即連網絡(vehicle ad hoc network, VANET) 可以幫助實現多樣的智慧運輸系統(Intelligent Transportation Systems, ITS)的相關應用。例如,可及時散播安全信息來提高行車安全性;收集道路交通信息以緩解城市中交通擁堵,網絡連接在車輛即連網絡中應用程序的服務品質(Quality of servers, QoS)。
本文探討在稀疏快道上的一個車輛即連網絡(vehicle ad hoc network, VANET)中,一部車輛傳遞訊息到它的後方車輛端到端延遲(end-to-end delay)的分析與討論,並討論雙向車流密度不同的情況,之前已經有很多討論修復延遲的論文。然而,大多數其中忽略了網絡斷開恢復對前一次斷開的依賴性。為了解決這個問題,我們討論恢復網絡斷開可能會受到前一個斷開的影響,從而開發出嵌入式馬可夫鏈EMC (Embedded Markov Chain)得到恢復延遲的穩態概率分佈。模擬的結果顯示,我們的分析是高度準確的。
Vehicle ad hoc network (VANET) can help realize various applications related to Intelligent Transportation Systems (ITS). For example, safety information can be disseminated in a timely manner to improve driving safety; road traffic information can be collected to alleviate traffic congestion in cities, and the Quality of Servers (QoS) of network connection applications in vehicles is connected to the network.
This paper discusses the analysis and discussion of the end-to-end delay of a vehicle transmitting a message to its rear vehicle in a vehicle ad hoc network (VANET) on a sparse highway, and discussing the case where the two-way traffic density is different, there have been many papers discussing repair delays before. However, most of them ignore the dependence of network disconnection recovery on the previous disconnection. In order to solve this problem, we discuss that the recovery network disconnection may be affected by the previous disconnection, and thus develop the EMC (Embedded Markov Chain) to obtain the steady-state probability distribution of the recovery delay. The simulation results show that our analysis is highly accurate.
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