An UAV-Assisted Edge Computing Resource Allocation Strategy for 5G Communication in IoT Environment

Liu, Hao

doi:10.1155/2022/9397783

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Hao Liu et al, [31] proposed a computing resource allocation strategy for 5G communication in the Internet of Things (IoT) environment by applying UAV-assisted edge computing. First, they constructed a system model with the UAV deployed with mobile edge computing (MEC) servers to provide assisted computing services for multiple users on the ground.…”

Section: Task Classification II Task Offloading Decisionmentioning

confidence: 99%

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Safaa Alali,

Abdulkarim Assalem

2023

ARASET

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In recent years, edge computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited devices. The main feature of edge computing is to push computation, networking, and storage facilities closer to the network edge. This enables user equipment (UE) to profit from the edge computing paradigm by mainly offloading their intensive computation tasks to edge resources. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in Metaheuristic -based computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the Metaheuristic -based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: genetic algorithm (GA)-based mechanisms, Partical Swarm Optimization(PSO)-based mechanisms, and Hybrid GA with PSO -based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded.

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Section: Task Classification II Task Offloading Decisionmentioning

confidence: 99%

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Safaa Alali,

Abdulkarim Assalem

2023

ARASET

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“…Literature [11] investigates the joint radio and computational resource allocation problem, aiming to optimize the system performance and customer satisfaction, and finally applies the UOC strategy, which effectively improves the performance of the system. Literature [12] proposes a computational resource allocation strategy based on drone-assisted edge computing for IoT 5G communication, which improves the allocation efficiency compared with the traditional method. A new distributed block-based q-learning algorithm is conceptualized in [13] for slot scheduling of smart devices and machine-based communication devices (mtcd) in clustered IoT networks, and the convergence of the optimized allocation mechanism is improved as confirmed by simulation experiments.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Scheduling Optimization Based on Internet of Things under Cloud Platforms

Xie

2024

Arch. Sci.

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In this paper, we explore how to optimize IoT-based resource allocation and scheduling in a cloud platform environment, focusing on improving computing resource utilization and quality of service, while reducing latency and packet loss. a model is adopted, which contains a number of edge servers and randomly generated computational tasks, taking into account the network conditions between the servers and the tasks. an objective function is established, aiming to maximize the computational resource utilization and QoS, and the corresponding constraints are proposed. Simulations are conducted using CloudSim, and the experimental results show that the total number of VoCS increases from 243.63 to 1397.71 when the scheduling demand is increased from 8 to 64, demonstrating the adaptability and efficiency of the algorithm under different demands. In addition, the algorithm is effective in dealing with both small-scale (200 tasks) and large-scale (6000 tasks) tasks. In addition, the algorithm demonstrates low load imbalance and short task completion time when dealing with both small-scale (200 tasks) and large-scale (6000 tasks) task sets, which proves its effectiveness. Ultimately, the scheduling method proposed in this study not only improves resource utilization and quality of service, but also reduces task completion time and cost.

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Systematic Literature Review on the Machine Learning Techniques for UAV-Assisted mm-Wave Communications

Sugesh,

Vairavel

2024

Lecture Notes in Electrical Engineering

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An UAV-Assisted Edge Computing Resource Allocation Strategy for 5G Communication in IoT Environment

Cited by 3 publications

References 26 publications

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Resource Allocation Scheduling Optimization Based on Internet of Things under Cloud Platforms

Systematic Literature Review on the Machine Learning Techniques for UAV-Assisted mm-Wave Communications

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