Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing
Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introd...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/3965689 |
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| _version_ | 1832566446247378944 |
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| author | Hong Wang |
| author_facet | Hong Wang |
| author_sort | Hong Wang |
| collection | DOAJ |
| description | Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally. |
| format | Article |
| id | doaj-art-e863697a71b544e191f9ff38987350c0 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-e863697a71b544e191f9ff38987350c02025-02-03T01:04:12ZengWileyJournal of Robotics1687-96192021-01-01202110.1155/2021/3965689Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge ComputingHong Wang0Department of Electronic Information TechnologyAiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.http://dx.doi.org/10.1155/2021/3965689 |
| spellingShingle | Hong Wang Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing Journal of Robotics |
| title | Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing |
| title_full | Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing |
| title_fullStr | Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing |
| title_full_unstemmed | Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing |
| title_short | Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing |
| title_sort | collaborative task offloading strategy of uav cluster using improved genetic algorithm in mobile edge computing |
| url | http://dx.doi.org/10.1155/2021/3965689 |
| work_keys_str_mv | AT hongwang collaborativetaskoffloadingstrategyofuavclusterusingimprovedgeneticalgorithminmobileedgecomputing |