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Intelligent ubiquitous computing for future UAV-enabled MEC network systems
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DOI：10.1007/s10586-021-03434-w.png)
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In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly.
Keyword:
Edge computing
Unmanned aerial vehicles
Jamming
Reinforcement learning
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