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研究生:董其豐
研究生(外文):Chi-Feng Tung
論文名稱:車載網路中以移動性預測為基礎支援穩定連線之轉傳選擇機制
論文名稱(外文):Mobility Prediction-Based Relay Selection Scheme for Stable Connections in VANETs
指導教授:周立德周立德引用關係
指導教授(外文):Li-Der Chou
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:94
中文關鍵詞:轉傳選擇車載網路移動性預測
外文關鍵詞:VANETMobility PredictionRelay Selection
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車載網路(Vehicular Ad hoc networks)為近年來行動隨意網路(Mobile Ad Hoc Networks)的型態改良,其主要通訊分別利用車輛對車輛通訊與車輛對路邊設施 (Road Side Unit)通訊,建立起動態的網路拓撲。由於在車輛對車輛通訊的架構當中,車輛高速移動以及容易改變的網路拓撲環境,造成此架構的無線網路通訊產生不穩定的現象。為了解決此問題,許多研究分別提出不同的車載網路封包路由機制,想藉由挑選車輛節點密度較高的道路進行傳送,以提高封包傳送成功的機率。然而在決定路徑之後,其轉傳節點選擇通常沿襲MANETs中路由協定的做法,選擇距離目的地端最近的節點作為轉傳對象,亦或是考慮節點的電量與運算能力作為篩選的條件。然而,VANETs中,車輛節點移動範圍受限於道路的拓撲形狀或是各種不同的道路規範以及駕駛者行為,與傳統MANETs中節點隨意移動的型態大不相同。且由於車輛得以裝載能力較高的運算設施與供電來源,因此以供電量與運算能力做為考量並不適當。更重要的是,由於車輛移動速度過快,使得轉傳節點更容易離開發送端的傳輸範圍,也造成重複的進行路段建置行為浪費大量的網路頻寬。本論文提出以移動性預測為基礎支援穩定連線之轉傳選擇機制,結合道路拓撲形狀與車輛的移動性預測連線時間,使得封包路由能夠盡量保持和車輛移動方向相同,減少連線中斷次數。另外本論文考慮現實狀況全球定位系統存在誤差,進行線道正確位置修正,以提供更精確的車輛移動預測機制。本論文在最後呈現實驗結果,本機制相較於其他轉傳節點選擇演算法,能夠平均提升25.6%的連線時間,以及降低20.2%的斷線次數,並且將連線時間之預測準確度控制於5.09%的絕對百分比誤差中。證明在現實環境多線道的情況下,本節點選擇機制能夠提供更穩定的網路連線時間。
Vehicular ad hoc network (VANET) is a novel class of wireless network. Vehicles implemented with on board unit (OBU), which can communicate with each other by vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2R) network architecture. In the V2V network environment, vehicular network topology is changing dynamically all the time with high mobility, the communications occurred in this architecture is intermittent pattern. To solve this problem, numerous researchers have proposed different kinds of VANET routing protocol to enhance the success of packets delivery ratio through choosing the path with high density. After determining the routing path, the choosing of relay nodes usually follow the mobile ad hoc networks (MANETs) routing protocol, that is, it selects the closest node with the destination or consider the power supply and computation ability of node. However, In MANET, mobile nodes are moving in a random and irregular pattern hence, it is very hard to predict their movements. Unlike unpredictable mobility in MANET, vehicles are limited by predefined road segments, traffic rules and driver behaviors. Furthermore, nowadays Vehicular on board units are able to equip a much more powerful vehicle rechargeable battery and a larger volume of communication device than before; therefore, it becomes inappropriate to take power supply volume and computing into account. Therefore, it is unsuitable to determine the relay node by these terms. What’s more important is that the vehicle moves too fast to make the relay node easily to leave the transmission range of sender, as a result, it wastes a lot of network bandwidth for route maintenance and re-discovery. In order to solve the above mentioned problems, this thesis proposed a mobility prediction-based relay selection scheme for stable connections in VANETs which takes both direction of vehicles and routing path into account. It makes packet routing and vehicles keep moving in the same direction, for reducing the link disconnection. This thesis takes GPS errors into consideration when determining the lane position to predict the vehicles mobility, for a even more accurate mobility prediction. Finally, the proposal is examined by the conducted simulation— the simulation results show that the proposed mechanism, on average, can increase connecting time by 25.6% but decrease number of link disconnection by 20.2%; furthermore, the prediction accuracy of connecting time is improved within the absolute error of 5.09%. This proves that in the multi-lanes scenarios, the proposed mechanism can provide even more stable connecting time than others.
摘要 i
Abstract ii
目錄 iv
圖目錄 v
表目錄 vii
第1章 緒論 1
1.1 概要 1
1.2 研究動機 2
1.3 研究目的 5
1.4 章節架構 6
第2章 背景知識與相關研究 7
2.1 車載網路 7
2.2 車載網路之路由機制 9
2.3 轉傳選擇之相關研究 11
2.4 結論與比較 16
2.5 相關技術 17
第3章 以移動型預測為基礎支援穩定連線之轉傳選擇機制 22
3.2 前提假設 23
3.3 以移動性預測為基礎支援穩定連線之轉傳選擇機制 24
3.4 車載網路之路由路徑選擇 32
3.5 多線道的都市環境轉傳節點選擇 35
3.6 全球定位系統之線道誤判修正 45
第4章 模擬實驗與結果 47
4.1 車輛移動模型 47
4.2 實驗環境設定 49
4.3 實驗結果 51
第5章 結論與未來展望 77
5.1 結論 77
5.2 未來展望 78
參考文獻 81
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