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Intelligent task offloading and collaborative computation in multi-UAV-enabled mobile edge computing
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AI
DOI：10.23919/JCC.2022.04.018.png)
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摘要
En 中文
This article establishes a three-tier mobile edge computing (MEC) network, which takes into account the cooperation between unmanned aerial vehicles (UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions, while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution (DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network (DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.
Keyword:
Task analysis
Servers
Trajectory
Optimization
Computational modeling
Autonomous aerial vehicles
Resource management
mobile edge computing
multi-UAV
collaborative cloud and edge computing
deep neural network
differential evolution
