Response Delay Optimization in Mobile Edge Computing Enabled UAV Swarm

Zhang, Qixun; Chen, Jingran; Lei, Jiansheng; Feng, Zhiyong; Han, Zhu; Chen, Zhiyong

doi:10.1109/tvt.2020.2964821

Cited by 80 publications

(47 citation statements)

References 35 publications

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“…In [26], Asheralieva et al studied network operation problem in UAVenabled MEC network, and they developed a framework based on hierarchical game-theoretic and reinforcement learning. In [27], Zhang et al established a communication and computation optimization model in an MEC-enabled UAV network, where the successful transmission probability was derived through using stochastic geometry.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-Assisted Mobile Edge Computing

Wang

Pan

et al. 2022

IEEE Trans. on Mobile Comput.

156

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In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all UEs via optimizing user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based Trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the considerable performance and both outperform traditional algorithms.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-Assisted Mobile Edge Computing

Wang

Pan

et al. 2022

IEEE Trans. on Mobile Comput.

156

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“…Zhang Q et al proposed a joint communication and calculation optimization solution for the UAV group scenario supported by mobile edge computing technology (MEC). Under the communication and calculation constraints, the transmission efficiency is improved and the response delay is reduced [114].…”

Section:  Uav-emergency Communicationsmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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“…Most studies assume that the UAV plays the role of an intermediate node that connects the user equipment (UE) to the BS. To ensure that the UAVs perform their required functions smoothly, several studies have attempted to increase the stability of the network in terms of energy consumption, packet delay, and queuing [15][16][17][18][19][20][21].…”

Section: Uav Network Optimizationmentioning

confidence: 99%

“…Zhang and others [15] analyzed the response delay that occurs when a packet is transmitted in a two‐layer UAV network by dividing it into two parts: communication and computation (queuing). The proposed two‐layer UAV network consists of Bottom‐UAVs, which serve as nodes, and Top‐UAVs, which manage the Bottom‐UAVs and communicate with the control center.…”

Section: Background and Related Workmentioning

confidence: 99%

Combined time bound optimization of control, communication, and data processing for FSO‐based 6G UAV aerial networks

Seo

Chung

2020

ETRI Journal

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Because of the rapid increase of mobile traffic, flexible broadband supportive unmanned aerial vehicle (UAV)‐based 6G mobile networks using free space optical (FSO) links have been recently proposed. Considering the advancements made in UAVs, big data processing, and artificial intelligence precision control technologies, the formation of an additional wireless network based on UAV aerial platforms to assist the existing fixed base stations of the mobile radio access network is considered a highly viable option in the near future. In this paper, a combined time bound optimization scheme is proposed that can adaptively satisfy the control and communication time constraints as well as the processing time constraints in FSO‐based 6G UAV aerial networks. The proposed scheme controls the relation between the number of data flows, input data rate, number of worker nodes considering the time bounds, and the errors that occur during communication and data processing. The simulation results show that the proposed scheme is very effective in satisfying the time constraints for UAV control and radio access network services, even when errors in communication and data processing may occur.

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Response Delay Optimization in Mobile Edge Computing Enabled UAV Swarm

Cited by 80 publications

References 35 publications

Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-Assisted Mobile Edge Computing

Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-Assisted Mobile Edge Computing

UAV Swarm Intelligence: Recent Advances and Future Trends

Combined time bound optimization of control, communication, and data processing for FSO‐based 6G UAV aerial networks

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