Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing
Aiming at the problems of high computing energy consumption and long time in traditional UAV-assisted edge computing research work, a computing resource allocation strategy using biological evolutionary algorithms in UAV-assisted mobile edge computing is proposed by introducing UAV swarms and geneti...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2022/4029558 |
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| _version_ | 1832553085877092352 |
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| author | Li Wang Xiang Yao Zhenqi Yuan |
| author_facet | Li Wang Xiang Yao Zhenqi Yuan |
| author_sort | Li Wang |
| collection | DOAJ |
| description | Aiming at the problems of high computing energy consumption and long time in traditional UAV-assisted edge computing research work, a computing resource allocation strategy using biological evolutionary algorithms in UAV-assisted mobile edge computing is proposed by introducing UAV swarms and genetic algorithms. Firstly, it analyzes the communication model for uplink transmission, the calculation model for local computing tasks, and UAV to perform computing tasks. Secondly, the objective function and overall model of system are constructed by comprehensively considering multiple constraints. Then, improved genetic algorithm is introduced into the model. On the basis of data encoding, crossover, mutation, and termination operations, the optimization performance of algorithm is greatly improved by multiple iterations of fitness function. Finally, the energy consumption of proposed algorithm and other two algorithms under the same number of iterations are compared and analyzed by simulation experiments. The experimental results show that the optimal solution, average, and variance of proposed algorithm for energy consumption are 52.354, 50.326, and 0.224, respectively, and its performance is better than other two comparison algorithms. |
| format | Article |
| id | doaj-art-c381592bdcb84704a2aa4f7a45a6899b |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-c381592bdcb84704a2aa4f7a45a6899b2025-02-03T05:57:09ZengWileyJournal of Robotics1687-96192022-01-01202210.1155/2022/4029558Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge ComputingLi Wang0Xiang Yao1Zhenqi Yuan2School of Internet of Things EngineeringSchool of Internet of Things EngineeringSchool of Internet of Things EngineeringAiming at the problems of high computing energy consumption and long time in traditional UAV-assisted edge computing research work, a computing resource allocation strategy using biological evolutionary algorithms in UAV-assisted mobile edge computing is proposed by introducing UAV swarms and genetic algorithms. Firstly, it analyzes the communication model for uplink transmission, the calculation model for local computing tasks, and UAV to perform computing tasks. Secondly, the objective function and overall model of system are constructed by comprehensively considering multiple constraints. Then, improved genetic algorithm is introduced into the model. On the basis of data encoding, crossover, mutation, and termination operations, the optimization performance of algorithm is greatly improved by multiple iterations of fitness function. Finally, the energy consumption of proposed algorithm and other two algorithms under the same number of iterations are compared and analyzed by simulation experiments. The experimental results show that the optimal solution, average, and variance of proposed algorithm for energy consumption are 52.354, 50.326, and 0.224, respectively, and its performance is better than other two comparison algorithms.http://dx.doi.org/10.1155/2022/4029558 |
| spellingShingle | Li Wang Xiang Yao Zhenqi Yuan Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing Journal of Robotics |
| title | Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing |
| title_full | Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing |
| title_fullStr | Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing |
| title_full_unstemmed | Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing |
| title_short | Computing Resource Allocation Strategy Using Biological Evolutionary Algorithm in UAV-Assisted Mobile Edge Computing |
| title_sort | computing resource allocation strategy using biological evolutionary algorithm in uav assisted mobile edge computing |
| url | http://dx.doi.org/10.1155/2022/4029558 |
| work_keys_str_mv | AT liwang computingresourceallocationstrategyusingbiologicalevolutionaryalgorithminuavassistedmobileedgecomputing AT xiangyao computingresourceallocationstrategyusingbiologicalevolutionaryalgorithminuavassistedmobileedgecomputing AT zhenqiyuan computingresourceallocationstrategyusingbiologicalevolutionaryalgorithminuavassistedmobileedgecomputing |