Fairness-Aware Task Loss Rate Minimization for Multi-UAV Enabled Mobile Edge Computing

Zhu, Chong; Zhang, Guopeng; Yang, Kun

doi:10.1109/lwc.2022.3218035

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…In [ 5 ], Qiu et al studied a joint placement, resource allocation, and user association problem for UAV-aided wireless networks with constrained backhaul links. In [ 6 ], Zhu et al proposed a multi-agent deep deterministic policy-gradient-based solution to optimize the flight trajectory, the association between the UAVs and user devices, and the task association of the user devices. In [ 7 ], Mozaffari et al first introduced optimal transport theory to optimize cell association in UAV-enabled wireless networks, which yields improvements in terms of the average network delay.…”

Section: Introductionmentioning

confidence: 99%

“…Most previous studies [ 5 , 9 , 10 , 11 ] primarily focused on UAV-enabled wireless communication networks, overlooking the computing requirements of users. Furthermore, few previous studies [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ] considered heterogeneous UAV-enabled networks, i.e., deploying UAVs with different communication and computing capabilities. Motivated by these issues, in this paper, we study the UAV deployment and user association problem to save the energy consumption of UAVs in a more extensive heterogeneous multi-UAV-enabled MEC networks.…”

Section: Introductionmentioning

confidence: 99%

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Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks

Han,

Zhou,

et al. 2023

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Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%