Joint task offloading, resource allocation, and security assurance for mobile edge computing-enabled UAV-assisted VANETs
| dc.contributor.author | He, Yixin | |
| dc.contributor.author | Zhai, Daosen | |
| dc.contributor.author | Huang, Fanghui | |
| dc.contributor.author | Wang, Dawei | |
| dc.contributor.author | Tang, Xiao | |
| dc.contributor.author | Zhang, Ruonan | |
| dc.date.accessioned | 2022-11-19T16:01:39Z | |
| dc.date.available | 2022-11-19T16:01:39Z | |
| dc.date.copyright | 2021 | en_US |
| dc.date.issued | 2021 | |
| dc.description.abstract | In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multiobjective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | This work was supported in part by the National Natural Science Foundation of China under Grant 61901381, 61901379, and 61102078, in part by the Foundation of the State Key Laboratory of Integrated Services Networks of Xidian University under Grant ISN21-06, in part by the National Key Research and Development Program of China under Grants 2020YFB1807004 and 2020YFB1807003, in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University under Grant 2020D04, in part by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant CX202037, and in part by the Program of China Scholarships Council under Grant 202006290166. Part of the journal has been accepted by the 2021 IEEE International Conference on Computer Communications Workshops (INFOCOM Workshops). | en_US |
| dc.identifier.citation | He, Y., Zhai, D., Huang, F., Wang, D., Tang, X., & Zhang, R. (2021). “Joint task offloading, resource allocation, and security assurance for mobile edge computingenabled UAV-assisted VANETs.” Remote Sensing, 13(8), 1547. https://doi.org/10.3390/rs13081547 | en_US |
| dc.identifier.uri | https://doi.org/10.3390/rs13081547 | |
| dc.identifier.uri | http://hdl.handle.net/1828/14503 | |
| dc.language.iso | en | en_US |
| dc.publisher | Remote Sensing | en_US |
| dc.subject | mobile edge computing (MEC) | |
| dc.subject | unmanned aerial vehicle (UAV) | |
| dc.subject | resource allocation | |
| dc.subject | task offloading | |
| dc.subject | vehicular ad hoc networks (VANETs) | |
| dc.subject.department | Department of Computer Science | |
| dc.title | Joint task offloading, resource allocation, and security assurance for mobile edge computing-enabled UAV-assisted VANETs | en_US |
| dc.type | Article | en_US |