Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Xiao, Zhu; Chen, Yanxun; Jiang, Hongbo; Hu, Zhenzhen; Lui, John C. S.; Min, Geyong; Dustdar, Schahram

doi:10.1007/s11276-022-03051-4

Cited by 29 publications

(6 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combined with the actual situation, the possibility and frequency of natural disasters in mountainous areas are relatively high compared with plain areas, and it is more reasonable to set the post-disaster area in mountainous areas [4]. Considering the mountainous area with many trees and undulating mountains, the channel fading between the ground base station and the vehicle mainly considers the shadow fading [5], and the rest of the fading is regarded as an ideal situation and ignored, then the channel gain between the mth ground base station and the kth vehicle user can be written as ℎ 𝑚 𝑘 [6], ℎ 𝑚 𝑘 is given by…”

Section: System Communication Modelmentioning

confidence: 99%

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

Ding¹

2023

Natural Language Processing, Information Retrieval and AI

View full text Add to dashboard Cite

With the development of UAV technology, using UAV as a base station in the air can quickly restore vehicle communications after disasters. In order to reduce the delay and maximize the rational use of bandwidth and power, this paper applies TDMA technology to UAV communication network, and proposes a joint optimization allocation strategy of bandwidth and power. First of all, a deep learning network needs to be trained. The use of deep learning can improve the accuracy of prediction. The reward mechanism is set through the change of delay. The purpose of training is to enable the UAV to choose the optimal bandwidth allocation coefficient under the dynamic change of the environment. Then, a joint optimization strategy is proposed to set the SNR threshold to ensure the communication quality. The user's transmission rate is calculated according to the Shannon formula, Finally, the scheme with minimum delay is selected as the final bandwidth and power allocation value. In the simulation experiment, compared with the previous traditional algorithm, the network performance has been further improved in terms of reducing delay and energy consumption, and what needs to be improved may be the problem of computation.

show abstract

Section: System Communication Modelmentioning

confidence: 99%

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

Ding¹

2023

Natural Language Processing, Information Retrieval and AI

View full text Add to dashboard Cite

show abstract

“…To solve the above challenge, numerous researchers have concentrated their attention to unmanned aerial vehicle (UAV)-enabled MEC networks, where UAV can act as aerial MEC server to provide flexible computing service and extensive coverage for ground users due to its flexible and fast deployment, high channel quality and low cost. [5][6][7] However, because of the limited power of UAVs and devices, energy-efficient resource allocation is still critical for UAV-MEC networks. Ji et al 8 investigated the minimization of weighted energy consumption between devices and UAV within a single UAV-MEC network, where the computation resources and UAV trajectory are optimized simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…To solve the above challenge, numerous researchers have concentrated their attention to unmanned aerial vehicle (UAV)‐enabled MEC networks, where UAV can act as aerial MEC server to provide flexible computing service and extensive coverage for ground users due to its flexible and fast deployment, high channel quality and low cost 5–7 . However, because of the limited power of UAVs and devices, energy‐efficient resource allocation is still critical for UAV‐MEC networks.…”

Section: Introductionmentioning

confidence: 99%

Computation‐efficiency‐oriented resource optimization in NOMA‐assisted multi‐UAV‐enabled MEC networks

Liu,

Tian,

Zhou

et al. 2024

Int J Communication

View full text Add to dashboard Cite

SummaryUnmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy‐efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV‐enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)‐oriented partial offloading framework for UAV‐enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k‐means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non‐orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.

show abstract

“…This innovative approach enables resourceconstrained TDs to efficiently delegate computationally demanding tasks to nearby edge servers for processing. Such a paradigm shift not only enhances the overall efficiency of IoT ecosystems but also relieves the computational strain on TDs [4], [5], [6]. However, deploying mobile edge computing servers in intricate terrains, such as rugged mountainous forests or demanding disaster relief zones, poses formidable cost challenges [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Joint Offloading Decision and Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing System

Qiu,

Li,

2024

Preprint

View full text Add to dashboard Cite

Multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computational services to Internet of Thing (IOT) terminal devices (TDs) in the limited or no available infrastructures. However, irrational trajectory of UAVs and offloading decision by TDs contribute to increased delay and energy consumption in the UAV-MEC system. In this paper, we present a two-stage optimization algorithm that jointly considers computation-offloading decision and trajectory to minimize the weighted sum of delay and energy consumption in the multi-UAV-assisted MEC system. Specifically, a Stackelberg Game-based computation offloading (SG-CO) algorithm is designed to obtain the optimal offloading decision for TDs. Furthermore, under the given TDs offloading decision, we develop the Simulated Annealing-Beetle Antennae Search (SA-BAS) algorithm to achieve the optimal trajectory for UAVs, in which SA-BAS algorithm accelerates convergence and enhances global optimization performance. Extensive simulation analysis demonstrates that the proposed scheme outperforms the benchmark schemes, showcasing its effectiveness in minimizing the weight sum of delay and energy consumption.

show abstract

Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Cited by 29 publications

References 97 publications

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

Computation‐efficiency‐oriented resource optimization in NOMA‐assisted multi‐UAV‐enabled MEC networks

Joint Offloading Decision and Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing System

Contact Info

Product

Resources

About