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In this paper, we investigate tags in anti-collision applications of radio frequency identification (RFID) technology in unmanned aerial vehicle (UAV)-aided vehicular ad hoc networks (VANETs). The integration of RFID technology in UAV-aided VANETs can provide reliable traffic-related services for vehicles. However, multiple tags' simultaneous responses to a reader mounted on a UAV, denoted as tag collision, gravely affect the correct tag detection on each vehicle. Therefore, in order to decrease the collision probability and improve the throughput, we propose a multi-frequency tag identification method. In the proposed scheme, we devise a tag grouping method based on adaptive power control to make the reader dynamically match the optimal frame length. Based on the above matching results, we introduce a tag estimation method using the optimal weight to improve the accuracy of tag estimation. We theoretically analyze the closed-form expression of the security outage probability expression. Finally, our simulation results demonstrate that the proposed tag anti-collision scheme achieved significant performance superiority in terms of the throughput and identification time slots. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]