Trajectory Planning of Multiple Dronecells in Vehicular Networks
The agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this paper, multiple UAVs are deployed to provide the needed cellular coverage to vehicles travelin...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2020
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/11797 https://doi.org/10.1109/LNET.2020.2966976 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/8960481 |
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| _version_ | 1864513489320542208 |
|---|---|
| author | Samir, Moataz |
| author2 | Ebrahimi, Dariush Assi, Chadi Sharafeddine, Sanaa Ghrayeb, Ali |
| author2_role | author author author author |
| author_facet | Samir, Moataz Ebrahimi, Dariush Assi, Chadi Sharafeddine, Sanaa Ghrayeb, Ali |
| author_role | author |
| dc.creator.none.fl_str_mv | Samir, Moataz Ebrahimi, Dariush Assi, Chadi Sharafeddine, Sanaa Ghrayeb, Ali |
| dc.date.none.fl_str_mv | 2020-02-04T13:22:07Z 2020-02-04T13:22:07Z 2020 2020-02-04 |
| dc.identifier.none.fl_str_mv | 2576-3156 http://hdl.handle.net/10725/11797 https://doi.org/10.1109/LNET.2020.2966976 Samir, M., Ebrahimi, D., Assi, C., Sharafeddine, S., & Ghrayeb, A. (2020). Trajectory planning of multiple dronecells in vehicular networks: A reinforcement learning approach. IEEE Networking Letters, 2(1), 14-18. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/8960481 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | IEEE Networking Letters |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Trajectory Planning of Multiple Dronecells in Vehicular Networks A Reinforcement Learning Approach |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this paper, multiple UAVs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This work minimizes the number of deployed UAVs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAVs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAVs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAVs adapt their velocities in order to cover the vehicles. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_5b5fc6e0d7628dde4a56434392f7d563 |
| identifier_str_mv | 2576-3156 Samir, M., Ebrahimi, D., Assi, C., Sharafeddine, S., & Ghrayeb, A. (2020). Trajectory planning of multiple dronecells in vehicular networks: A reinforcement learning approach. IEEE Networking Letters, 2(1), 14-18. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/11797 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Trajectory Planning of Multiple Dronecells in Vehicular NetworksA Reinforcement Learning ApproachSamir, MoatazEbrahimi, DariushAssi, ChadiSharafeddine, SanaaGhrayeb, AliThe agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this paper, multiple UAVs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This work minimizes the number of deployed UAVs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAVs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAVs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAVs adapt their velocities in order to cover the vehicles.PublishedN/A2020-02-04T13:22:07Z2020-02-04T13:22:07Z20202020-02-04Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article2576-3156http://hdl.handle.net/10725/11797https://doi.org/10.1109/LNET.2020.2966976Samir, M., Ebrahimi, D., Assi, C., Sharafeddine, S., & Ghrayeb, A. (2020). Trajectory planning of multiple dronecells in vehicular networks: A reinforcement learning approach. IEEE Networking Letters, 2(1), 14-18.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://ieeexplore.ieee.org/abstract/document/8960481enIEEE Networking Lettersinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/117972021-05-27T10:03:39Z |
| spellingShingle | Trajectory Planning of Multiple Dronecells in Vehicular Networks Samir, Moataz |
| status_str | publishedVersion |
| title | Trajectory Planning of Multiple Dronecells in Vehicular Networks |
| title_full | Trajectory Planning of Multiple Dronecells in Vehicular Networks |
| title_fullStr | Trajectory Planning of Multiple Dronecells in Vehicular Networks |
| title_full_unstemmed | Trajectory Planning of Multiple Dronecells in Vehicular Networks |
| title_short | Trajectory Planning of Multiple Dronecells in Vehicular Networks |
| title_sort | Trajectory Planning of Multiple Dronecells in Vehicular Networks |
| url | http://hdl.handle.net/10725/11797 https://doi.org/10.1109/LNET.2020.2966976 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/8960481 |