Stackelberg-Game-Based Intelligent Offloading Incentive Mechanism for a Multi-UAV-Assisted Mobile-Edge Computing System

Wang, Maoli; Zhang, Lu; Zang, Xiaodong; Yang, Xiaoli; Wang, Kang; Yang, Kunlun

doi:10.1109/jiot.2023.3265432

Cited by 16 publications

(3 citation statements)

References 37 publications

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“…In order explore the impact of parameters in the UAV-MEC system, we conduct all simulation experiments using Matlab 2017a running on AMD 11th Gen Intel(R) Core(TM) i7-1165G7, CPU @ 2.80GHz, 16.0GB RAM and 512GB disk. In the simulation experiments, by referring to [35,36], some parameters required are summarized in Table 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Multi-Layer Heterogeneous Task Scheduling Strategy in UAVs-assisted MEC Networks

Pang,

Zhu,

Gui

et al. 2024

Preprint

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This paper studies the task scheduling problem in the multiple unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system. In order to reflect the complexity and diversity of the system, we propose a multi-layer heterogeneous task scheduling strategy. The strategy allows system model to simulate arrival and service of tasks by applying heterogeneous M/M/1, M/M/c, and M/M/c/K classical queues. In order to better evaluate the proposed strategy, we obtain various performance measures. By trading off delay and energy consumption, a single-objective optimization problem with constraints is formulated. Then we introduce the penalty function method to transform the problem into a single-objective optimization problem without any constraints, and propose a hierarchical heterogeneous task scheduling optimization (HHTSO) algorithm to optimize the formulated problem. Theoretical analysis and simulation results show that the proposed algorithm is more effective in ensuring low delay and low energy consumption.

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Section: Simulation Resultsmentioning

confidence: 99%

A Multi-Layer Heterogeneous Task Scheduling Strategy in UAVs-assisted MEC Networks

Pang,

Zhu,

Gui

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In this section, we carry out the simulation experiments we assume that the 15 TDs are randomly distributed in a 1000m × 1000m square area, the 3 UAVs' flight altitude is 30 meters, and the UAV's flight energy consumption parameters are set according to reference [18]. Other simulation parameters are set according to reference [24], as shown in Table 1.…”

Section: Simulation Analysismentioning

confidence: 99%

“…Bowen et al [17] presented a game-based task offloading and resource allocation algorithm to obtain optimal offloading decision for TDs. Wang et al [18] designed a multi-round iterative game algorithm based on arithmetic descent to achieve the optimal solution and enhance overall user satisfaction. The aforementioned researches illustrate that the game theory performs well in addressing the TDs offloading problem.…”

Section: Introductionmentioning

confidence: 99%

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

Qiu,

Li,

2024

Preprint

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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.

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Deep Reinforcement Learning Method for Task Offloading in Mobile Edge Computing Networks Based on Parallel Exploration with Asynchronous Training

Chen,

Jin,

Yao

et al. 2024

Mobile Netw Appl

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Stackelberg-Game-Based Intelligent Offloading Incentive Mechanism for a Multi-UAV-Assisted Mobile-Edge Computing System

Cited by 16 publications

References 37 publications

A Multi-Layer Heterogeneous Task Scheduling Strategy in UAVs-assisted MEC Networks

A Multi-Layer Heterogeneous Task Scheduling Strategy in UAVs-assisted MEC Networks

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

Deep Reinforcement Learning Method for Task Offloading in Mobile Edge Computing Networks Based on Parallel Exploration with Asynchronous Training

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