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Resource Management in UAV Enabled MEC Networks

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Resource Management in UAV Enabled MEC Networks. / Abrar, Muhammad; Almohaimeed, Ziyad M; Ajmal, Ushan et al.
In: Computers, Materials & Continua, Vol. 74, No. 3, 31.03.2023, p. 4847-4860.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Abrar, M, Almohaimeed, ZM, Ajmal, U, Akram, R, Masroor, R & Hussain, M 2023, 'Resource Management in UAV Enabled MEC Networks', Computers, Materials & Continua, vol. 74, no. 3, pp. 4847-4860. https://doi.org/10.32604/cmc.2023.030242

APA

Abrar, M., Almohaimeed, Z. M., Ajmal, U., Akram, R., Masroor, R., & Hussain, M. (2023). Resource Management in UAV Enabled MEC Networks. Computers, Materials & Continua, 74(3), 4847-4860. https://doi.org/10.32604/cmc.2023.030242

Vancouver

Abrar M, Almohaimeed ZM, Ajmal U, Akram R, Masroor R, Hussain M. Resource Management in UAV Enabled MEC Networks. Computers, Materials & Continua. 2023 Mar 31;74(3):4847-4860. Epub 2022 Dec 28. doi: 10.32604/cmc.2023.030242

Author

Abrar, Muhammad ; Almohaimeed, Ziyad M ; Ajmal, Ushan et al. / Resource Management in UAV Enabled MEC Networks. In: Computers, Materials & Continua. 2023 ; Vol. 74, No. 3. pp. 4847-4860.

Bibtex

@article{1ae1c15f4a50493c84ef6a8e135790b7,
title = "Resource Management in UAV Enabled MEC Networks",
abstract = "Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem.According to simulation results, both local and offloading schemes give optimal solution.",
keywords = "Mobile edge computing, internet of things, UAVs, ground mobile users",
author = "Muhammad Abrar and Almohaimeed, {Ziyad M} and Ushan Ajmal and Rizwan Akram and Roha Masroor and Muhammad Hussain",
year = "2023",
month = mar,
day = "31",
doi = "10.32604/cmc.2023.030242",
language = "English",
volume = "74",
pages = "4847--4860",
journal = "Computers, Materials & Continua",
issn = "1546-2218",
publisher = "Tech Science Press",
number = "3",

}

RIS

TY - JOUR

T1 - Resource Management in UAV Enabled MEC Networks

AU - Abrar, Muhammad

AU - Almohaimeed, Ziyad M

AU - Ajmal, Ushan

AU - Akram, Rizwan

AU - Masroor, Roha

AU - Hussain, Muhammad

PY - 2023/3/31

Y1 - 2023/3/31

N2 - Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem.According to simulation results, both local and offloading schemes give optimal solution.

AB - Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem.According to simulation results, both local and offloading schemes give optimal solution.

KW - Mobile edge computing

KW - internet of things

KW - UAVs

KW - ground mobile users

U2 - 10.32604/cmc.2023.030242

DO - 10.32604/cmc.2023.030242

M3 - Journal article

VL - 74

SP - 4847

EP - 4860

JO - Computers, Materials & Continua

JF - Computers, Materials & Continua

SN - 1546-2218

IS - 3

ER -