Terrain-Aware UAV-Enabled Mobile Edge Computing in Urban Environments: A Constrained Multi-Objective Approach With Task-Adaptive Mechanism
With the increasing and multifaceted demands for wireless communication in smart cities, Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems have garnered substantial interest due to their potential to enhance connectivity and computational capabilities. In these systems, UAVs...
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| Vydáno v: | IEEE transactions on vehicular technology s. 1 - 14 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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2025
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| ISSN: | 0018-9545, 1939-9359 |
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| Abstract | With the increasing and multifaceted demands for wireless communication in smart cities, Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems have garnered substantial interest due to their potential to enhance connectivity and computational capabilities. In these systems, UAVs are deployed to predetermined locations to establish communication with nearby user devices (UDs), thereby providing network coverage and computational offloading services. Nevertheless, the presence of complex urban terrains, including rough topographies and dense buildings, results in significant path loss in UAV-to-UD communication links and increases the risk of collisions during UAV navigation, ultimately degrading the quality of service (QoS) and compromising UAV operational safety. To mitigate these challenges, this study introduces a terrain-aware channel model capable of precisely evaluating the effects of terrain characteristics on UAV-to-UD link performance. To improve QoS for UDs while ensuring UAV flight safety, we formulate a constrained multi-objective optimization problem (CMOP) to jointly optimize UAV trajectory, destination, and resource allocation, aiming to minimize task completion time and enhance UAV flight safety. To solve this CMOP, we propose a multi-task constrained multi-objective evolutionary algorithm with a task-adaptive mechanism, promoting the search for Pareto optimal solutions. Numerical results indicate that the proposed method outperforms baseline algorithms in achieving a well-converged set of feasible solutions with enhanced diversity. |
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| AbstractList | With the increasing and multifaceted demands for wireless communication in smart cities, Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems have garnered substantial interest due to their potential to enhance connectivity and computational capabilities. In these systems, UAVs are deployed to predetermined locations to establish communication with nearby user devices (UDs), thereby providing network coverage and computational offloading services. Nevertheless, the presence of complex urban terrains, including rough topographies and dense buildings, results in significant path loss in UAV-to-UD communication links and increases the risk of collisions during UAV navigation, ultimately degrading the quality of service (QoS) and compromising UAV operational safety. To mitigate these challenges, this study introduces a terrain-aware channel model capable of precisely evaluating the effects of terrain characteristics on UAV-to-UD link performance. To improve QoS for UDs while ensuring UAV flight safety, we formulate a constrained multi-objective optimization problem (CMOP) to jointly optimize UAV trajectory, destination, and resource allocation, aiming to minimize task completion time and enhance UAV flight safety. To solve this CMOP, we propose a multi-task constrained multi-objective evolutionary algorithm with a task-adaptive mechanism, promoting the search for Pareto optimal solutions. Numerical results indicate that the proposed method outperforms baseline algorithms in achieving a well-converged set of feasible solutions with enhanced diversity. |
| Author | Huang, Xumin Wu, Zexiong Wu, Yuan Xie, Qiqi Wang, Zhuoran Peng, Chaoda |
| Author_xml | – sequence: 1 givenname: Zexiong surname: Wu fullname: Wu, Zexiong email: zexiongwu@stu.scau.edu.cn organization: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China – sequence: 2 givenname: Qiqi surname: Xie fullname: Xie, Qiqi email: qiqixie@stu.scau.edu.cn organization: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China – sequence: 3 givenname: Zhuoran surname: Wang fullname: Wang, Zhuoran email: wangzhuoran@stu.scau.edu.cn organization: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China – sequence: 4 givenname: Xumin surname: Huang fullname: Huang, Xumin email: huangxu_min@163.com organization: School of Automation, Guangdong University of Technology, Guangzhou, China – sequence: 5 givenname: Chaoda surname: Peng fullname: Peng, Chaoda email: chaodapeng@scau.edu.cn organization: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China – sequence: 6 givenname: Yuan surname: Wu fullname: Wu, Yuan email: yuanwu@um.edu.mo organization: State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, Macau, China |
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| SubjectTerms | Autonomous aerial vehicles Buildings Channel models communication Computational modeling constrained multi-objective evolutionary algorithm Optimization Quality of service Safety Smart cities Surfaces terrain-aware channel model Trajectory UAV-enabled MEC |
| Title | Terrain-Aware UAV-Enabled Mobile Edge Computing in Urban Environments: A Constrained Multi-Objective Approach With Task-Adaptive Mechanism |
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