###### 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 multi-objective 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. mobile edge computing (MEC); unmanned aerial vehicle (UAV); resource allocation; task offloading; vehicular ad hoc networks (VANETs) Article  
[table]cap=top
the ground-to-ground counterpart [6]. Therefore, the UAV-assisted VANETs have great potential in improving the quality-of-service (QoS) vehicle-related applications [7]. In addition, the computation ability of most vehicles is limited, and multiple latency sensitive tasks cannot be fully processed within the allowed latency, such as the traffic information, the security applications, etc. [8]. To solve the limitations of vehicle computation ability, mobile edge computing (MEC) can be used. MEC can extend the computation ability to VANETs edge and satisfy the needs of data access demands. The work in [9] proposed a multi-resource allocation scheme to accommodate deadline sensitive tasks in VANETs. [PERSON] et al. in [10] adopted the queuing theory to study multi-user vehicle-mounted MEC systems by jointly considering the system delay and the energy consumption. Moreover, the researchers of [11] devised a MEC infrastructure adopting UAVs, and they evaluated the performance of the MEC server mounted on the UAV in terms of user coverage. Furthermore, the authors of [12] proposed the concept of vehicular fog computing (VFC) by utilizing vehicles and RSUs as the infrastructures for computation. Similarly, [PERSON] et al. [13] proposed a vehicular edge computing (VEC) caching scheme to minimize the average latency to mobile vehicles. The above works in [9; 10; 11; 12; 13] have enhanced VANETs' computation ability to achieve significant performance. Nevertheless, there are still some deficiencies to be tackled. On the one hand, the authors of [9; 10] consider that all mobile users have to offload their tasks to only one MEC server. Thus, all tasks cannot be fully cannot be processed within the allowed latency threshold. On the other hand, the works of [11; 12; 13] cannot balance the load among MEC servers, which are unable to cope with the huge computation demands generated by urban ITS. In addition, the researchers of [9; 10; 11; 12; 13] ignore the impact of the physical layer security performance on the VANETs, which results in great limitations to apply these schemes in practical vehicle networking applications. Motivated by the above discussions, we study jointly the task offloading, the resource allocation, and the security assurance problem for the UAV-assisted VANETs. As shown in Figure 1, we investigate the MEC architecture in UAV-assisted VANETs.