A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System
<p dir="ltr">This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs (variable number of UAVs) are deployed to serve Internet of Things devices (IoTDs). We aim to minimize the sum of hovering and flying energies of UA...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2021
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إضافة وسم
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| _version_ | 1864513505640579072 |
|---|---|
| author | Muhammad Asim (2235472) |
| author2 | Wali Khan Mashwani (9449980) Samir Brahim Belhaouari (9427347) Saima Hassan (14918003) |
| author2_role | author author author |
| author_facet | Muhammad Asim (2235472) Wali Khan Mashwani (9449980) Samir Brahim Belhaouari (9427347) Saima Hassan (14918003) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Asim (2235472) Wali Khan Mashwani (9449980) Samir Brahim Belhaouari (9427347) Saima Hassan (14918003) |
| dc.date.none.fl_str_mv | 2021-09-09T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3111318 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Genetic_Trajectory_Planning_Algorithm_With_Variable_Population_Size_for_Multi-UAV-Assisted_Mobile_Edge_Computing_System/26983138 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Distributed computing and systems software Trajectory Task analysis Energy consumption Resource management Trajectory planning Genetic algorithms Processor scheduling Mobile edge computing unmanned aerial vehicle evolutionary algorithm multi-chrome genetic algorithm |
| dc.title.none.fl_str_mv | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs (variable number of UAVs) are deployed to serve Internet of Things devices (IoTDs). We aim to minimize the sum of hovering and flying energies of UAVs by optimizing the trajectories of UAVs. The problem is very complicated as we have to consider the deployment of stop points (SPs), the association between UAVs and SPs, and the order of SPs for UAVs. To solve the problem, this paper proposed a novel genetic trajectory planning algorithm with variable population size (GTPA-VP), which consists of two phases. In the first phase, a genetic algorithm (GA) with a variable population size is used to update the deployment of SPs. Accordingly, a multi-chrome GA is adopted to find the association between UAVs and SPs, an optimal number of UAVs, and the optimal order of SPs for UAVs. The effectiveness of the proposed GTPA-VP is demonstrated through several experiments on a set of ten instances with up to 200 IoTDs. It is evident from the experimental results that the proposed GTPA-VP outperforms the benchmark algorithms in terms of the energy consumption of the system.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3111318" target="_blank">https://dx.doi.org/10.1109/access.2021.3111318</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3e223c20b7e61e3047651d2799baad79 |
| identifier_str_mv | 10.1109/access.2021.3111318 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26983138 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing SystemMuhammad Asim (2235472)Wali Khan Mashwani (9449980)Samir Brahim Belhaouari (9427347)Saima Hassan (14918003)Information and computing sciencesDistributed computing and systems softwareTrajectoryTask analysisEnergy consumptionResource managementTrajectory planningGenetic algorithmsProcessor schedulingMobile edge computingunmanned aerial vehicleevolutionary algorithmmulti-chrome genetic algorithm<p dir="ltr">This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs (variable number of UAVs) are deployed to serve Internet of Things devices (IoTDs). We aim to minimize the sum of hovering and flying energies of UAVs by optimizing the trajectories of UAVs. The problem is very complicated as we have to consider the deployment of stop points (SPs), the association between UAVs and SPs, and the order of SPs for UAVs. To solve the problem, this paper proposed a novel genetic trajectory planning algorithm with variable population size (GTPA-VP), which consists of two phases. In the first phase, a genetic algorithm (GA) with a variable population size is used to update the deployment of SPs. Accordingly, a multi-chrome GA is adopted to find the association between UAVs and SPs, an optimal number of UAVs, and the optimal order of SPs for UAVs. The effectiveness of the proposed GTPA-VP is demonstrated through several experiments on a set of ten instances with up to 200 IoTDs. It is evident from the experimental results that the proposed GTPA-VP outperforms the benchmark algorithms in terms of the energy consumption of the system.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3111318" target="_blank">https://dx.doi.org/10.1109/access.2021.3111318</a></p>2021-09-09T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3111318https://figshare.com/articles/journal_contribution/A_Novel_Genetic_Trajectory_Planning_Algorithm_With_Variable_Population_Size_for_Multi-UAV-Assisted_Mobile_Edge_Computing_System/26983138CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269831382021-09-09T06:00:00Z |
| spellingShingle | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System Muhammad Asim (2235472) Information and computing sciences Distributed computing and systems software Trajectory Task analysis Energy consumption Resource management Trajectory planning Genetic algorithms Processor scheduling Mobile edge computing unmanned aerial vehicle evolutionary algorithm multi-chrome genetic algorithm |
| status_str | publishedVersion |
| title | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System |
| title_full | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System |
| title_fullStr | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System |
| title_full_unstemmed | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System |
| title_short | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System |
| title_sort | A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System |
| topic | Information and computing sciences Distributed computing and systems software Trajectory Task analysis Energy consumption Resource management Trajectory planning Genetic algorithms Processor scheduling Mobile edge computing unmanned aerial vehicle evolutionary algorithm multi-chrome genetic algorithm |