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- W4313162413 abstract "Mobile edge computing (MEC) is a promising technology to enhance the computation capability of smart devices (SDs) in the Internet-of-Things (IoT). However, the performance of MEC server is limited due to the fixed location and constrained coverage. In order to address this issue, a multiple unmanned aerial vehicles (UAVs) assisted MEC system is studied in this paper. We consider an energy constrained multi-UAV assisted MEC system where multiple UAVs collaborate with each other to provide computing services, and UAVs can dynamically change the frequency according to the computing task size. We aim to maximize the computation bits, SDs’ fairness and UAVs’ load balancing in multi-UAV MEC system by jointly optimizing the trajectory and frequency. To address this problem, we model it as a Partially Observable Markov Decision Process, and propose a joint optimization strategy based on multi-agent deep reinforcement learning. Finally, we evaluate our strategy against some typical benchmark strategies on the realistic dataset. The experiment results show that our strategy can outperform other strategies." @default.
- W4313162413 created "2023-01-06" @default.
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- W4313162413 date "2022-01-01" @default.
- W4313162413 modified "2023-09-30" @default.
- W4313162413 title "Joint Optimization of Trajectory and Frequency in Energy Constrained Multi-UAV Assisted MEC System" @default.
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- W4313162413 doi "https://doi.org/10.1007/978-3-031-20984-0_30" @default.
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