本篇論文提出使用行動邊緣計算的架構計算出在限制的延遲時間內距離 RSU n-hop 的車對車對基礎網路的資料分流機制。在有一輛車 Vx 想透過網路下載資料的情境下，如果 Vx 不在 RSU 的訊號範圍之中，那 Vx 只能透過 4G/5G 的行動網路下載資料，若能透過一輛在 RSU 訊號範圍內的車 Vy 來下載資料，而後使用 Vx 與 Vy 之間的 n-hop 車對車資料分流機制把 Vx 想要的資料從行動網路下載分流至透過一條車對車對基礎網路的分流路徑下載。在本篇論文中利用了行動邊緣計算的架構來達成集中式的運算，每一輛車會將自身的資訊回報給行動邊緣計算的伺服器，包含位置、速度、行駛方向、車輛 ID、鄰近車輛的 ID、能夠連接上的 RSU 的 ID 等等，車輛不管是還沒進入到 RSU 的訊號範圍還是離開了 RSU 的訊號範圍，行動邊緣計算伺服器都會根據車輛的回報資訊去找出一條能進行資料分流的車對車對基礎網路的資料分流路徑。由於行動邊緣計算伺服器是收到車輛的回報訊息才開始資料分流機制的計算，使得就算有資料分流路徑但是要等到車輛的回報訊息才能使用，因此行動邊緣計算伺服器會預測車輛的在回報周期內的行駛狀況，找出這段時間內最好的資料分流路徑，車輛在延遲一段時間之後就能使用這條分流路徑而不是等到車輛的下一個回報的時間點。在本篇論文中，評斷資料分流路徑的方法分為兩種，第一種是以分流時間為基準的評斷方式，第二種是不僅考慮分流的時間並考慮了資料分流路徑的網路競爭情況的評斷方式。在效能評估中，在不同的車輛密度下，所提出的兩種方法均優於傳統的只使用 RSU 來進行資料分流的方式，在所提出的兩種方法之中，第二種方法優於第一種方法。

This thesis proposes a Mobile Edge Computing (MEC)-based Delay-constrained n-hop data offloading method. In the proposed method, when a vehicle X is not in the IEEE 802.11p Road Side Unit (RSU)’s signal coverage, if there is a multiple-hop vehicle-to-vehicle (V2V) path connecting vehicle Y, which is inside the RSU’s signal coverage, then vehicle X can launch the vehicle-to-vehicle-to-infrastructure (V2V2I), in which I denotes RSU, VANET data offload from the 4G/5G cellular network to the RSU through the multiple-hop V2V path. In this work, the MEC is used to have the centralized mechanism for checking whether the aforementioned n-hop V2V2I path for helping vehicles’ data offloading exists or not. Using the proposed method, each vehicle reports its context, including position, speed, driving direction, vehicle ID, and neighboring vehicles’ ID, the ID of the RSU that can be connected, etc., to the MEC server. The MEC server calculates whether the n-hop V2V2I path exists or not for vehicle X based on the reported contexts from all vehicles when the MEC server receives X’s reported context. In addition to derive the V2V2I paths based on the snapshotted VANET topology on the time point of receiving X’s reported context, this work proposed a scheme to have the MEC server to derive the potential V2V2I paths that may exist in the future, i.e., in a constrained time interval. In this way, it can find the better n-hop V2V2I VANET data offloading path. This work proposes two methods for finding the data offloading path. The first method is based on the data offloading path’s lifetime. The second method not only considers the data offloading path’s lifetime but also data offloading path’s quality, which is based on the contention level of the path. The performance analysis shown that the proposed two methods are better than the traditional method, which is enabled when the source vehicle is inside RSU’s signal coverage, and the second method is better than the first method in different vehicle density’s situations.

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