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研究生:黃文
研究生(外文):HUANG, WEN
論文名稱:模糊推論機應用於軟體定義車載網路之繞送機制效能研究
論文名稱(外文):A Study on Performance of Routing Mechanisms of Fuzzy Inference Machine Applied to Software-Defined Vehicle Networks
指導教授:黃永發黃永發引用關係
指導教授(外文):HUANG, YUNG-FA
口試委員:譚旦旭簡福榮徐演政鄭佳炘
口試委員(外文):TAN, TAN-HSUJEAN, FU-RONGHSU, YEN-TSENGCHENG, CHIA-HSIN
口試日期:2019-07-31
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊與通訊系
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:2019
畢業學年度:107
語文別:中文
論文頁數:51
中文關鍵詞:第五代行動通訊技術車輛對車輛無線通訊模糊推論系統毫米波繞送延遲模糊推論繞送-初步模糊推論繞送-優化
外文關鍵詞:5th generation mobile networksVehicle-to-VehicleFuzzy Inference SystemMimi-meter WaveRouting LatencyFuzzy Inference Routing-preliminaryFuzzy Inference Routing-Refine
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第五代 (5th generation, 5G) 行動通訊技術是一個高傳輸量、低延遲和高可靠性的重要技術,其中車載隨意行動網路(Vehicular ad-hoc networks, VANET)透過車輛對車輛(Vehicle-to-Vehicle, V2V)無線通訊技術提供訊息的交換,其中低延遲與高傳輸容量的效能需求最具挑戰性。本研究中,我們應用模糊推論系統(Fuzzy inference system)提出FIR-P(Fuzzy inference routing-preliminary)繞送機制之模擬推論,在不同密度(density)情況與繞送距離(L_a)模擬環境下,模擬結果可以符合高考靠度90%以上滿足軟體定義車載網路(Software defined network vehicular network, SDVN)之低於1毫秒繞送延遲(Transmission latency)要求。 另外,將FIR-P優化加入不同V2V繞送範圍,提出FIR-R(Fuzzy inference routing-refine) 繞送機制,模擬結果在有效的繞送距離下,能符合超高可靠度99%以上滿足SDVN之低於1毫秒繞送延遲要求。
The 5th Generation (5G) mobile communication technology is an important technology with high transmission capacity, low latency and high reliability. Among them, Vehicular Ad-hoc Networks (VANET) pass Vehicle-to-Vehicle (V2V) Wireless communication technology provides the exchange of information, and the performance requirements of low latency and high transmission capacity are the most challenging. In this study, we use the fuzzy inference system to propose a simulation inference of the Fuzzy inference system-preliminary (FIR-P) routing mechanism. Under different density and routing distance (L_a) simulation environments. The simulation results can meet the requirements of the high-reliable by more than 90% to meet the transmission latency requirements of the Software Defined network Vehicular Network (SDVN) to be less than 1 ms. In addition, the FIR-P optimization is added to different V2V routing ranges, and the Fuzzy Inference System-Refine (FIR-R) routing mechanism is proposed. The simulation results can meet the ultra-high reliability of more than 99% and meet the SDVN the delay required to be less than 1 ms under the effective winding distance.
目錄
摘要........I
Abstract...II
致謝........III
目錄........IV
圖目錄......VI
表目錄......IX
第一章 緒論.............1
1.1. 研究背景與動機....1
1.2. 文獻探討.........4
1.3. 研究方法.........5
1.4. 論文架構.........7
第二章 車載隨意行動網路模型.....8
2.1. 介紹............8
2.1.1. 毫米波雷達......8
2.1.2. V2V、V2I鏈結...8
2.1.3. 軟體定義網路....9
2.2. 模擬環境........10
2.2.1. 通道模型.......12
2.2.2. 繞送延遲時間...12
2.2.3. 傳輸量.........14
第三章 模糊推論機之繞送機制應用...15
3.1. 模糊推論架構............15
3.2. 繞送機制之模糊推論.......15
3.3. 模擬結果................27
第四章 具能源效率之模糊推論機......33
4.1. 功率消耗之模糊推論........33
4.2. 模擬結果..................39
第五章 結論........................46
參考文獻............................47
圖目錄
圖1.1 IMT對2020年後的應用情境圖[9]....3
圖1.2 軟體定義車載網路架構圖[11].......6
圖2.1 SDN架構圖[29]..................10
圖2.2 V2V三種繞送方式示意圖............11
圖2.3 5G軟體定義網路multi-hop車載中繼架構圖[5]....11
圖2.4 multi-hop車載繞送架構圖[5]......13
圖3.1 模糊推論系統.....................15
圖3.2 三種繞送方式的延遲時間之累積分布函數(La=300m,ρ=0.29)...16
圖3.3 三種繞送方式的延遲時間之累積分布函數(La=300m,ρ=0.3)....17
圖3.4 三種繞送方式的延遲時間之累積分布函數(La=400m,ρ=0.22)...18
圖3.5 三種繞送方式的延遲時間之累積分布函數(La=400m,ρ=0.23)...18
圖3.6 三種繞送方式的延遲時間之累積分布函數(La=400m,ρ=0.45)...19
圖3.7 三種繞送方式的延遲時間之累積分布函數(La=400m,ρ=0.46)...19
圖3.8 三種繞送方式的延遲時間之累積分布函數(La=500m,ρ=0.17)...20
圖3.9 三種繞送方式的延遲時間之累積分布函數(La=500m,ρ=0.18)...21
圖3.10 三種繞送方式的延遲時間之累積分布函數(La=500m,ρ=0.36)..21
圖3.11 三種繞送方式的延遲時間之累積分布函數(La=500m,ρ=0.37)..22
圖3.12密度之歸屬函數圖....23
圖3.13 La之歸屬函數圖.....23
圖3.14 輸出P之歸屬函數圖...24
圖3.15 FIR-P繞送機制之模糊推論系統架構圖...25
圖3.16 模糊系統之輸出結果圖..27
圖3.17 平均繞送延遲時間......28
圖3.18 不同繞送的延遲時間之累積分布函數(La=300m)....29
圖3.19 不同繞送的延遲時間之累積分布函數(La=400m)....29
圖3.20 不同繞送的延遲時間之累積分布函數(La=500m)....30
圖3.21 90%繞送延遲時間........31
圖3.22 平均傳輸容量...........32
圖4.1密度之歸屬函數圖..........34
圖4.2 繞送距離之歸屬函數圖.....34
圖4.3 繞送方式之歸屬函數圖.....35
圖4.4 TR之歸屬函數圖..........36
圖4.5 FIR-R繞送機制之模糊推論系統架構圖...37
圖4.6 模糊系統之輸出結果圖....39
圖4.7 平均繞送延遲時間........40
圖4.8 不同繞送的延遲時間之累積分布函數(La=300m)....41
圖4.9 不同繞送的延遲時間之累積分布函數(La=400m,)...42
圖4.10 不同繞送的延遲時間之累積分布函數(La=500m)...42
圖4.11 99%繞送延遲時間....43
圖4.12 平均傳輸容量.......44
圖4.13 平均功率...........45
表目錄
表3.1 FIR-P繞送方式之模糊規則.....26
表4.1 FIR-R繞送方式之模糊規則.....37
表4.2 TR之模糊規則...............38

參考文獻
[1]陳梅鈴,「全球5G市場發展趨勢」,電腦與通訊,取自:https://ictjournal.itri.org.tw/Content/Messagess/contents.aspx?&MmmID=654304432070702333&msID=654526036164003536.
[2]X. Ge, H. Cheng, G. Mao, Y. Yang, and S. Tu, “Vehicular Communications for 5G Cooperative Small Cell Networks,” IEEE Transactions on Vehicular Technology, 2016, Vol. 65, No. 10, pp. 7882-7894.
[3]C. Harsch, A. Festag, and P. Papadimitratos, “Secure Position-Based Routing for VANETs,” in Proceedings. of IEEE 66th vehicular technology conference, 2007, pp. 26-30.
[4]I. Ku, Y. Lu, and M. Gerla, “Towards Software-Defined VANET: Architecture and Services,” 2014 13th Annual Mediterranean Ad Hoc Networking Workshop, Piran, Slovenia, 2014, pp. 103-110.
[5]X. Ge, Z. Li, and S. Li, “5G Software Defined Vehicular Networks,” IEEE Communications Magazine, 2017, Vol. 55, Iss. 7, pp. 87-93.
[6]C. Harsch, A. Festag, and P. Papadimitratos, “Secure Position-Based Routing for VANETs,” 2007 IEEE 66th Vehicular Technology Conference, Baltimore, MD, USA, 2007, pp. 26-30.
[7]S. Chen, F. Qin, B. Hu, X. Li, and Z. Chen, “User-Centric Ultra-Dense Networks (UUDN) for 5G: Challenges, Methodologies, and Directions,” IEEE Wireless Communications, 2016, Vol. 23, No. 2, pp. 78-85.
[8]M. X. Gong, R. Stacey, D. Akhmetov, and S. Mao, “A Directional CSMA/CA Protocol for mmWave Wireless PANs,” 2010 IEEE Wireless Communication and Networking Conference, Sydney, Australia, 2010, pp. 1-6.
[9] 簡均哲,汪海瀚,eMBB/URLLC/mMTC鼎立5G標準制定全面啟動,新通訊元件雜誌,取自:https://www.2cm.com.tw/2cm/zh-tw/tech/F20D9109E8FC4D34B9CC25B24A786283.
[10]J. Wan, S. Tang, Z. Shu, D. Li, S. Wang, M. Imran, and A. V. Vasilakos, “Software-Defined Industrial Internet of Things in the Context of Industry 4.0,” IEEE Sensors Journal, 2016, Vol. 16, No. 20, pp. 7373-80,.
[11]L. Li, Z. Mao, and J. Rexford, “Toward Software-Defined Cellular Networks,” 2012 European Workshop on Software Defined Networking, Darmstadt, Germany, 2012, pp. 7-12.
[12]L. A. Zadeh, “Fuzzy sets,” Information and Control, 1965, Vol. 8, pp. 338-353.
[13]G. Karagiannis, O. Altintas, E. Ekici, G. Heijenk, B. Jarupan, K. Lin, and T. Weil, “Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions,” IEEE Communications Surveys & Tutorials, 2011, Vol. 13, No. 4, pp. 584-616.
[14]M. Jutila, “An Adaptive Edge Router Enabling Internet of Things,” IEEE Internet of Things Journal, 2016, Vol. 3, No. 6, pp. 1061-1069.
[15]C. Huang, R. Lu, and K. R. Choo, "Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges," IEEE Communications Magazine, 2017, Vol. 55, No. 11, pp. 105-111.
[16]C. Huang, M. Chiang, D. Dao, W. Su, S. Xu, and H. Zhou, "V2V Data Offloading for Cellular Network Based on the Software Defined Network (SDN) Inside Mobile Edge Computing Architecture," IEEE Access, 2018, Vol. 6, pp. 17741-17755.
[17]H. Wu, R. Fujimoto, G. Riley, and M. Hunter, "Spatial propagation of information in vehicular networks," IEEE Transactions on Vehicular Technology, 2009, Vol. 58, No. 1.
[18] 黃紹傑,軟體定義網路之V2V通訊效能最佳化之研究,碩士論文,國立台北科技大學電機工程研究所,台北,2018。
[19] H. Seliem, R. Shahidi, M. H. Ahmed and M. S. Shehata, "Drone-Based Highway-VANET and DAS Service," IEEE Access, 2018, Vol. 6, pp. 20125-20137.
[20] N. B. Truong, G. M. Lee, and Y. Ghamri-Doudane, “Software Defined Networking-Based Vehicular Adhoc Network with Fog Computing,” 2015 IFIP/IEEE International Symposium on Integrated Network Management, Ottawa, Canada, 2015, pp. 1202-1207.
[21] M. Hadded, R. Zagrouba, A. Laouiti, P. Muhlethaler, and L. A. Saidane, "A multi-objective genetic algorithm-based adaptive weighted clustering protocol in VANET", 2015 IEEE Congress on Evolutionary Computation, 2015, pp. 994-1002.
[22] I. Tal, and G. M. Muntean, “User-oriented cluster-based solution for multimedia content delivery over VANETs”, IEEE international Symposium on Broadband Multimedia Systems and Broadcasting, 2012, pp. 1-5.
[23] Y. Shi, L. H. Zou, and S. Z. Chen, “A mobility pattern aware clustering mechanism for mobile vehicular networks,” Applied Mechanics and Materials, 2012, Vol. 130, pp. 317-320.
[24] Y. F. Huang and J. H. Wen, “Adaptive fuzzy interference cancellation for CDMA communication system, ” 2000 IEEE 51st Vehicular Technology Conference, 2000, pp. 1120-1124.
[25] J. H. Wen and Y. F. Huang, “Fuzzy-based adaptive partial parallel interference canceller for CDMA communication systems over fading channels,” IEE Proceedings-Communications, 2002, Vol. 149, No. 2, pp. 111-116.
[26] L. Altoaimy and I. Mahgoub, “Fuzzy logic based localization for vehicular ad hoc networks, ” 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, Orlando, USA, 2014, pp. 121-128.
[27] J. E. Naranjo, C. Gonzlez, R. Garca, T. de Pedro, “Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver”, IEEE Transactions on Intelligent Transportation Systems, 2008, Vol. 9, No. 3, pp. 438-450.
[28] M. X. Gong, R. Stacey, D. Akhmetov, and S. Mao, “A Directional CSMA/CA Protocol for mmWave Wireless PANs,” 2010 IEEE Wireless Communication and Networking Conference, Sydney, Australia, 2010, pp. 1-6.
[29] 潘叡,「軟體定義網路架構簡介」,國立台灣大學計算機及資訊網路中心電子報,第29期,2014。
[30] 賴佑賓,裝置對裝置通訊資源分組分享方法於上行鏈路毫米波蜂巢式網路,碩士論文,國立中正大學通訊工程研究所,嘉義,2018。
[31] H. Jung, and I. H. Lee, “Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics,” Sensors, 2018.
[32] S. Li, Z. Li, X. Ge, J. Zhang, and M. Jo, “Multi-Hop Links Quality Analysis of 5G Enabled Vehicular Networks,” 2017 9th International Conference on Wireless Communications and Signal Processing, Nanjing, China, 2017.
[33] G. Yu, L. Xu, D. Feng, R. Yin, G. Y. Li, and Y. Jiang, “Joint Mode Selection and Resource Allocation for Device-to-Device Communications,” IEEE Transactions on Communications, 2014, Vol. 62, No. 11, pp. 3814-3824.


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