近年來行動邊緣運算已成為物聯網中的一項熱門的應用，邊緣運算可藉由衛星通訊的輔助實踐在偏遠地區，然而與衛星直接通訊所消耗的能量對物聯網設備來說是一項挑戰。本篇論文研究透過無人機來輔助行動邊緣運算系統，其中無人機與衛星皆可提供行動邊緣運算的服務。此論文的目的是在無人機的電量有限之條件下，決定無人機的飛行軌跡與物聯網設備的上傳決策，以最小化所有物聯網設備的能量消耗。為了解決此問題，我們首先利用一個現有的啟發式演算法來解決經典的越野問題，接著我們進一步提出一個動態規劃的演算法，減少無人機在盤旋期間的能量消耗以服務更多的物聯網設備。模擬數據顯示我們提出的演算法相比其他方法有更好的效能表現。

In recent years, mobile edge computing (MEC) has become one of the most popular applications in the Internet of Things (IoT). With the help of satellite communications, MEC can be realized in remote areas. However, when transmitting directly to satellites, the energy consumption of IoT devices remains a challenge. In this thesis, an unmanned aerial vehicle (UAV) assisted MEC system is studied, in which the UAV and satellite are both feasible MEC servers providing computation services. This thesis aims to minimize the total energy consumption among all IoT devices by jointly determining the offloading decision and UAV's trajectory under the constraint of an energy budget. To tackle the problem, we utilize an existing heuristic algorithm for solving the classic Orienteering Problem and propose a dynamic programming (DP) algorithm to reduce the hovering cost of the UAV to serve more IoT devices. Numerical results show that the performance of the proposed algorithm is better than the baselines.

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