本篇論文提出了一種基於行動邊緣計算（MEC）架構的車載網路資料分流方法，透過路基台（RSU）將4G / 5G蜂巢網路的數據流量分流到IEEE 802.11p網路。在本文中，想要進行資料分流的車輛Vs可以使用（i）當Vs處於RSU的訊號範圍內時，通過車輛到基礎設施（V2I）的連接進行資料分流，以及（ii）使用一車對車對基礎設施（V2V2I）的分流路徑，在Vs接近前方的RSU或離開後方的RSU且不在RSU訊號範圍內時，仍能透過此分流路徑進行資料分流。上述情況在本文稱為V2V2I資料分流。由於某些RSU的訊號範圍可能重疊，例如在地理位置上相鄰的停車場，超市，速食店等所部署的RSU，多個RSU的部署和信號重疊的RSU之間的切換處理需要納入考量，以盡可能地進行V2V2I資料分流。本文採用了MEC架構，具有集中式的計算機制來檢查是否存在可以執行VANET資料分流的V2V2I分流路徑。使用MEC架構，道路上的每輛車都會定期向相應的MEC伺服器回報自身的相關資訊，包括位置，速度，車輛ID，相鄰車輛的ID，所能偵測到之RSU的ID等。基於接收到的回報資訊，MEC伺服器可以在其接收回報資訊的時間點tr來查找是否存在一個或多個可利用的V2V2I分流路徑。由於MEC伺服器可能無法在其收到車輛Vs的回報資訊的當下找出良好的V2V2I分流路徑，即可能會有更好的V2V2I分流路徑在tr + T的時間區間出現，因此本篇論文採用了時間延遲的預測機制，MEC伺服器可以預測在一個回報資訊的週期T內可能存在的潛在V2V2I分流路徑，並透過本文提出的路徑品質函數來評估V2V2I分流路徑之優劣，以便MEC伺服器可以找到更好的V2V2I分流路徑。此路徑品質函數在多個RSU的環境中同時考慮了車輛和RSU的網路狀況。效能評估的結果表明，該方法優於傳統的V2I分流方法，可以提高車載資料分流的效能。

This thesis proposes a Mobile Edge Computing (MEC) -based Vehicular ad hoc Network (VANET) data offloading method, which is called Predicted k-hop-Limited Multi-RSU-Considered (PKMR) Vehicle to Vehicle to Infrastructure (V2V2I) Offloading, to offload the 4G/5G cellular network’s data traffic to IEEE 802.11p network through Road Side Units (RSUs). In this work, the source vehicle Vs that wants to have data offloading can use (i) a Vehicle to Infrastructure (V2I) link to do VANET offloading when Vs is inside the signal coverage of RSU, i.e., self-offloading, and (ii) a V2V2I path, which denotes a n-hop V2V2I path connecting Vs and the ahead/rear RSU, to do VANET offloading when Vs is approaching the ahead RSU or leaving the rear RSU, i.e., when Vs is outside the signal coverage of the RSU. The aforementioned (ii)’s scenario is called V2V2I offloading in this work. Since the signal coverages of some RSUs, e.g., those RSUs deployed by parking lots, supermarkets, fast food stores, etc., that are neighboring geographically, may be overlapped, (i) the configuration of multi-RSU’s deployment and (ii) the RSU handoff processing between the signal-overlapped RSUs need to be considered to utilize the V2V2I data offloading as more as possible. This work adopts the MEC architecture to have the centralized mechanism for checking whether the aforementioned n-hop V2V2I path that can execute VANET data offloading exists or not. Using the MEC architecture, each vehicle on the road reports its context, including location, speed, its ID, IDs of neighboring vehicles, IDs of sensed RSUs, etc., to the corresponding MEC server periodically. Based on the received context reports, the MEC server can use the snapshotted connection topology of its administered vehicles and RSUs that existed on the time point tr, on which the MEC server received the context report, to find whether there is one or more V2V2I offloading path between the source vehicle Vs, which wants to have the V2V2I data offloading, and the ahead/rear RSUs of Vs. Since it may not find a good V2V2I path based on the snapshotted connection topology of the administrated vehicles and RSUs that exist on the time point tr when the MEC server received the context report of the source vehicle Vs, i.e., some much better V2V2I paths may come out on the time point of tr + T, this work adopts the time-postponed prediction mechanism, for which the potential V2V2I paths that may exist from the time point tr to the time point that the MEC server will receive the next context report of Vs are detected and compared, such that the MEC server can find the better V2V2I offloading path. Additionally, a quality function is designed to evaluate the qualities of different offloading paths to select the most suitable offloading path. The quality function takes both vehicles’ and RSUs’ network conditions into consideration in the multi-RSU’s environment. The performance evaluation shown that the proposed method is better than the traditional self-offloading method and can enhance the data offloading performance.

[1] K. Zheng, Q. Zheng, P. Chatzimisios, W. Xiang and Y. Zhou, “Heterogeneous Vehicular Networking: A Survey on Architecture, Challenges, and Solutions,” IEEE Communications Surveys & Tutorials, VOL. 17, NO. 4, pp. 2377–2396, June 2015
[2] A. Singh and B. Singh, "A Study of the IEEE802.11p (WAVE) and LTE-V2V Technologies for Vehicular Communication," Proceedings of the 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, pp. 157-160, 2020.
[3] K. Abboud, H. A. Omar and W. Zhuang, "Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey," IEEE Transactions on Vehicular Technology, VOL. 65, NO. 12, pp. 9457-9470, Dec. 2016.
[4] A. K. Ligo, J. M. Peha, P. Ferreira and J. Barros, "Throughput and Economics of DSRC-Based Internet of Vehicles," IEEE Access, VOL. 6, pp. 7276-7290, 2018.
[5] S. Chen, J. Hu, Y. Shi, Y. Peng, J. Fang, and R. Zhao, L. Zhao, "Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G," IEEE Communications Standards Magazine, VOL. 1, NO. 2, pp. 70-76, 2017.
[6] Y. Wang, J. Wang, Y. Ge, B. Yu, C. Li and L. Li, "MEC Support for C-V2X System Architecture," Proceedings of the 19th IEEE International Conference on Communication Technology (ICCT), Xi'an, China, pp. 1375-1379, 2019.
[7] H. Abou-zeid, F. Pervez, A. Adinoyi, M. Aljlayl and H. Yanikomeroglu, "Cellular V2X Transmission for Connected and Autonomous Vehicles Standardization, Applications, and Enabling Technologies," IEEE Consumer Electronics Magazine, VOL. 8, NO. 6, pp. 91-98, 1 Nov. 2019.
[8] S. Chen, J. Hu, Y. Shi, L. Zhao and W. Li, "A Vision of C-V2X: Technologies, Field Testing, and Challenges With Chinese Development," IEEE Internet of Things Journal, VOL. 7, NO. 5, pp. 3872-3881, May 2020.
[9] F. Rebecchi, M. Dias de Amorim, V. Conan, A. Passarella, R. Bruno and M. Conti, "Data Offloading Techniques in Cellular Networks: A Survey," IEEE Communications Surveys & Tutorials, VOL. 17, NO. 2, pp. 580-603, 2015.
[10] H. Zhou, H. Wang, X. Li and V. C. M. Leung, "A Survey on Mobile Data Offloading Technologies," IEEE Access, VOL. 6, pp. 5101-5111, 2018.
[11] H. Zhou, H. Wang, X. Chen, X. Li and S. Xu, "Data Offloading Techniques Through Vehicular Ad Hoc Networks: A Survey," IEEE Access, VOL. 6, pp. 65250-65259, 2018.
[12] C.-M. Huang, S.-Y. Lin, and Z.-Y. Wu, "The k-hop-limited V2V2I VANET Data Offloading using the Mobile Edge Computing (MEC) Mechanism," Vehicular Communications, VOL. 26, 2020.
[13] C.-F. Lai, "The Mobile Edge Computing (MEC) -based Vehicle to Vehicle to Infrastructure (V2V2I) VANET Data Offloading using the Delay-constrained Computing Method," Master thesis, Dept. of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, 2019/7.
[14] Y. Mao, C. You, J. Zhang, K. Huang and K. B. Letaief, "A Survey on Mobile Edge Computing: The Communication Perspective," IEEE Communications Surveys & Tutorials, VOL. 19, NO. 4, pp. 2322-2358, 2017.
[15] H. Liu, F. Eldarrat, H. Alqahtani, A. Reznik, X. de Foy and Y. Zhang, "Mobile Edge Cloud System: Architectures, Challenges, and Approaches," IEEE Systems Journal, VOL. 12, NO. 3, pp. 2495-2508, Sept. 2018.
[16] X. Huang, R. Yu, J. Kang, Y. He and Y. Zhang, "Exploring Mobile Edge Computing for 5G-Enabled Software Defined Vehicular Networks," IEEE Wireless Communications, VOL. 24, NO. 6, pp. 55-63, Dec. 2017.
[17] L. Li, Y. Li and R. Hou, "A Novel Mobile Edge Computing-Based Architecture for Future Cellular Vehicular Networks," Proceedings of the 15th IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, pp. 1-6, 2017.
[18] P. Salvo, I. Turcanu, F. Cuomo, A. Baiocchi and I. Rubin, "LTE Floating Car Data Application Off-loading via VANET Driven Clustering Formation," Proceedings of the 12th Annual Conference on Wireless On-demand Network Systems and Services (WONS), Cortina d'Ampezzo, Italy, pp. 1-8, 2016.
[19] N. Cheng, N. Lu, N. Zhang, X. Zhang, X. S. Shen and J. W. Mark, "Opportunistic WiFi Offloading in Vehicular Environment: a Game-Theory Approach," IEEE Transactions on Intelligent Transportation Systems, VOL. 17, NO. 7, pp. 1944-1955, July 2016.
[20] N. Wang and J. Wu, "Opportunistic WiFi Offloading in a Vehicular Environment: Waiting or Downloading now?" Proceedings of the 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA, pp. 1-9, 2016.
[21] P. Si, Y. He, H. Yao, R. Yang and Y. Zhang, "DaVe: Offloading Delay-Tolerant Data Traffic to Connected Vehicle Networks," IEEE Transactions on Vehicular Technology, VOL. 65, NO. 6, pp. 3941-3953, June 2016.
[22] T. Yu, X. Zhu, H. Chen and M. Maheswaran, "HetCast: Cooperative Data Delivery on Cellular and Road Side Network," Proceedings of the 28th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, pp. 1-6, 2017.
[23] J. Feng and Z. Feng, "A Vehicle-assisted Offloading Scheme for Hotspot Base Stations on Metropolitan Streets," Proceedings of the 28th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, pp. 1-6, 2017.
[24] G. S. Aujla, R. Chaudhary, N. Kumar, J. J. P. C. Rodrigues and A. Vinel, "Data Offloading in 5G-Enabled Software-Defined Vehicular Networks: a Stackelberg-Game-Based Approach," IEEE Communications Magazine, VOL. 55, NO. 8, pp. 100-108, Aug. 2017.
[25] W. Liu, X. Wang, W. Zhang, L. Yang and C. Peng, "Coordinative Simulation with SUMO and NS3 for Vehicular Ad Hoc Networks," Proceedings of the 22nd Asia-Pacific Conference on Communications (APCC), Yogyakarta, pp. 337-341, 2016.
[26] K. G. Lim, C. H. Lee, R. K. Y. Chin, K. Beng Yeo and K. T. K. Teo, "SUMO Enhancement for Vehicular ad hoc Network (VANET) Simulation," Proceedings of the 2nd IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Kota Kinabalu, Malaysia, pp. 86-91, 2017.