Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System

Huang, Shuqi; Zhang, Jun; Wu, Yi

doi:10.3390/s22208061

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Context-aware task allocation [52] DFD DAG [69] GT IoT, 5G [78] LPA MEC Energy-efficient task allocation [68] MAPE-K IoT [11] Lyapunov MEC [19] Bi-Level MEC [25] DRL IoV [30] QT 5G [43] PSO IoT [47] ILP MEC [71] JTORA MEC [73] hybrid RF-FSO Industrial IoT Dynamic task allocation [13] MPSO VVECNs, VANETs [79] MH IoV [20] AA Fog [23] DP MEC [41] DRL IoT [44] ECTA EC [46] AA Fog [49] ILP [56] GA, GEN IoT [60] PSO EC [64] BPSO Table 1. Cont.…”

Section: Collaborative Task Allocationmentioning

confidence: 99%

See 1 more Smart Citation

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Patsias,

Amanatidis,

Karampatzakis

et al. 2023

Future Internet

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Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

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Section: Collaborative Task Allocationmentioning

confidence: 99%

“…A novel UAV-assisted MEC system was presented by Shuqi Huang et al [47]. The authors proposed an architecture for providing computing services to users with low latency requirements by maximizing the UAV's flight height and workload allocation ratio.…”

Section: Energy-efficient Task Allocationmentioning

confidence: 99%

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Patsias,

Amanatidis,

Karampatzakis

et al. 2023

Future Internet

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“…Computation offloading technology makes it possible for users to utilize the computing capabilities at the edge of the network [ 8 ]. Due to MEC, the delay can be reduced and the quality of service can also be improved [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Inter-Satellite Cooperative Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite–Terrestrial Networks

Tong

Wang

et al. 2023

Sensors

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Mobile edge computing (MEC)-enabled satellite–terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications’ tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite’s MEC server are insufficient, but the computing resources of neighboring satellites’ MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.

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UAV-enabled fair offloading for MEC networks: a DRL approach based on actor-critic parallel architecture

Li,

Shi

et al. 2024

Appl Intell

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Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System

Cited by 8 publications

References 38 publications

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Inter-Satellite Cooperative Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite–Terrestrial Networks

UAV-enabled fair offloading for MEC networks: a DRL approach based on actor-critic parallel architecture

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