Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

Qijie Qiu, Lingjie Li (), Zhijiao Xiao (), Yuhong Feng, Qiuzhen Lin and Zhong Ming ()
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Qijie Qiu: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Lingjie Li: Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China
Zhijiao Xiao: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Yuhong Feng: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Qiuzhen Lin: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Zhong Ming: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Mathematics, 2024, vol. 12, issue 13, 1-18

Abstract: With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

Keywords: mobile edge computing; unmanned aerial vehicle; deployment optimization; task offloading; evolutionary algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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