Abstract
In this paper, we investigate an unmanned aerial vehicle- (UAV-) enhanced mobile edge computing network (MUEMN), where multiple UAVs are deployed as aerial edge servers to provide computing services for ground moving equipment (GME). Each GME is trained to simulate movement by a Gauss-Markov random model in this MUEMN. Under the condition of limited energy cost, UAV dynamically plans its flight position according to the movement trend of GME. Our objective is to minimize the total energy consumption of GME by jointly optimizing the offloading decisions of GME and the flight positions of UAVs. More explicitly, we model the optimization problem as a Markov decision process and achieve real-time offloading decisions via deep reinforcement learning algorithm according to the dynamic system state, where the asynchronous advantage actor-critic (A3C) framework with asynchronous characteristics is leveraged to accelerate the learning process. Finally, numerical results confirm that our proposed A3C-based offloading strategy can effectively reduce the total of energy consumption of GME and ensure the continuous operation of the GME.
Highlights
Mobile users usually have limited computing capabilities and battery storages; it is challenging to provide a satisfactory computing service and achieve a low service delay when they face with the emerging applications with computationintensive features [1–3]
We compare A3C with the following there commonly used baseline methods: (1) Greedy: when the ground moving equipment (GME) is in the coverage area of the unmanned aerial vehicles (UAVs), the GME selects either local execution or UAV execution for the computation task depending on the magnitude of the local computation delay and transmission delay [27]
(2) Random: the GME within UAV signal coverage can randomly select the object of computational task execution, i.e., local execution or UAV execution
Summary
Mobile users usually have limited computing capabilities and battery storages; it is challenging to provide a satisfactory computing service and achieve a low service delay when they face with the emerging applications with computationintensive features [1–3]. In this context, mobile edge computing (MEC) is considered as a key technology to mitigate these issues [4]. The location of MEC server is usually fixed and cannot be changed flexibly according to the needs of mobile users, which restricts the extension of MEC [7, 8]. Compared with the general communication infrastructure, unmanned aerial vehicles (UAVs) are highly flexibility and inexpensive, enabling reliable communication. UAVs equipped with MEC servers greatly enhance the application scalability of the traditional MEC model [9, 10]
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