Multi-UAV-Assisted MEC System: Joint Association and Resource Management Framework

Nway, Nway; Kang, Seok‐Won; Alsenwi, Madyan; Tun, Yan Kyaw; Hong, Choong Seon

doi:10.1109/icoin50884.2021.9333960

Cited by 23 publications

(15 citation statements)

References 12 publications

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“…To validate the performance of our proposed algorithm, we compare it with four other benchmarks in Figure 7 and Figure 8 ; They are, without bandwidth allocation optimization, i.g., the bandwidth of each TD is equally divided; without task split radio optimization, i.g., task split radio of each ITD is fixed as

; without vertical location optimization, i.g., the fly height of each UAV is fixed at 70m; without horizontal location optimization, i.g., the horizontal location of UAVs are obtained by the method in [ 13 ]. It is dividing the clusters of TDs by using K-means method and then setting the centroid of the clusters as the horizontal location of UAVs.…”

Section: Numerical Resultsmentioning

confidence: 99%

“… Related works on UAV-assisted MEC systems. The references are in order from top to bottom: [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. …”

Section: Figurementioning

confidence: 99%

“…In [ 12 ], the authors minimized the total energy consumption of the NOMA-based MEC networks underlaying the UAV with time, computation capacity, and UAV trajectory. The work in [ 13 ] aimed to minimize the energy consumption of terminal devices and UAVs by joint optimizing device association, task assignment and computing resource allocation.…”

Section: Introductionmentioning

confidence: 99%

“…The aforementioned research [ 9 , 10 , 11 , 12 , 13 ] investigating MEC systems only considered UAV servers. It is notable that UAVs have better offloading links but are poor in computing with limited computing resources compared to GAPs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Huang

Zhang

et al. 2022

Sensors

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Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air–ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV–TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.

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Section: Numerical Resultsmentioning

confidence: 99%

“… Related works on UAV-assisted MEC systems. The references are in order from top to bottom: [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. …”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Huang

Zhang

et al. 2022

Sensors

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“…et al [ 18 ] comprehensively considered latency, energy consumption, link quality, and other factors in a multi-UAV-enabled road traffic monitoring scenario, and obtained a computation resource allocation strategy with better overall performance based on the sequential game. EI N.N et al [ 19 ] considered device association, task assignment, and computational resource allocation problems comprehensively, and proposed an iterative algorithm based on block coordinate descent to minimize the energy consumption of mobile devices and UAVs. Yan et al [ 20 ] considered the MEC network of a multi-UAV and multi-ground user scenario and constructed a Stackelberg game model: The UAV, as the vice-leader, maximizes the income by optimizing the location and pricing of computing resources.…”

Section: Introductionmentioning

confidence: 99%

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach

Yuan¹,

Zhidong²,

Tan³

2022

Sensors

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This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm’s feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

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Contract-based Cooperative Computation and Communication Resources Sharing in Mobile Edge Computing

et al. 2023

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Multi-UAV-Assisted MEC System: Joint Association and Resource Management Framework

Cited by 23 publications

References 12 publications

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach

Contract-based Cooperative Computation and Communication Resources Sharing in Mobile Edge Computing

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