Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Wang, Hong

doi:10.1155/2021/3965689

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Hong Wang et al, [23] proposed a collaborative task offloading strategy for Unmanned Aerial Vehicle (UAV) clusters in Mobile Edge Computing (MEC) environments. The strategy used an improved Genetic Algorithm (IGA) to optimize the task offloading process by considering various factors such as task priority, computing resources, and communication overhead.…”

Section: Overview Of (Ga)-based Offloading Mechanismsmentioning

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: Overview Of (Ga)-based Offloading Mechanismsmentioning

confidence: 99%

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Safaa Alali,

Abdulkarim Assalem

2023

ARASET

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“…When a threat is encountered, one of the UAV nodes in the sub-swarm may be destroyed or lose its function, resulting in higher access control cost for the swarm. The massive, dynamic, distributed, and lightweight nature of the current UAV swarm likewise makes the UAV swarm access control mechanism suffer from insufficient stability, low performance of privilege judgments, and reduced security [3][4][5][6][7][8] . The complex urban environment has the problems of path loss, incomplete channel state, and swarm access security, therefore, it is important to study an air-space integrated radio wave swarm anti-interference communication.…”

Section: Introductionmentioning

confidence: 99%

Research on a new improved UAV anti-jamming and optimization model

zhang¹,

Zhang²,

Yang³

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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In complex environments, UAV wireless sensing networks suffer from path loss, incomplete channels, swarm access security and other problems, and the signal fading and packet loss rates are very obvious. In order to solve the problem of user control data security, this paper gives full play to the characteristics of blockchain technology and builds a blockchain-based UAV cluster anti-interference communication network in UAV cluster applications to achieve antiinterference performance. Meanwhile, Markov decision process is used to optimize and realize the research of UAV airground integrated radio wave anti-interference.

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“…By introducing UAVs into the MEC system, the flexible deployment of UAVs allows users to jointly collaborate on computing and communication, which improves the performance of the MEC system [6,7]. However, the relationship between the trajectory control strategy and the capacity and energy consumption of traditional communication systems is unclear due to the fast mobility of UAVs.…”

Section: Introductionmentioning

confidence: 99%

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

Liu¹

2022

Journal of Robotics

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As the computing capacity of existing mobile devices cannot fully meet the needs of users for communication quality, a computing resource allocation strategy for 5G communication in the Internet of Things (IoT) environment is proposed by applying UAV-assisted edge computing. First, a system model is constructed with the UAV deployed with mobile edge computing (MEC) servers to provide assisted computing services for multiple users on the ground. Based on the optimization of the UAV trajectory, communication scheduling, and the energy consumption model of the UAV, the problem of the total computational cost minimization is formulated. Then, the genetic algorithm is improved by introducing a penalty function to solve this problem, in which selection, crossover, and mutation operations are iterated to obtain the optimal allocation strategy for computational resources. Finally, a simulation platform is constructed to analyze the proposed method. The results show that the total cost and total time of the proposed strategy are better than other comparison strategies.

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Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

Cited by 7 publications

References 26 publications

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

Research on a new improved UAV anti-jamming and optimization model

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

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