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.
Highlights
With the increasing number of vehicles and the rising popularity of on-board applications, vehicular ad hoc networks (VANETs) have attracted extensive attention in recent years [1]
We consider a unidirectional road, where unmanned aerial vehicle (UAV) are randomly located along the road and investigate the mobile edge computing (MEC) technique for UAV-assisted VANETs that aims at minimizing the task processing delay
We focus on the UAV-assisted VANETs problem by jointly considering the task offloading, the resource allocation, and the security assurance that aims at minimizing the task processing delay
Summary
With the increasing number of vehicles and the rising popularity of on-board applications, vehicular ad hoc networks (VANETs) have attracted extensive attention in recent years [1]. As an important application of intelligent transport systems (ITSs), VANETs have been widely used in the traffic prediction, road safety, and driver behavior detection fields [2]. Vehicles can obtain the traffic-related content directly from the service providers by using a CN, or indirectly from road side units (RSUs) by using IEEE 802.11p protocol. Due to the bulk data transmissions, the CN faces the challenge of network congestion [3]. An efficient method to solve the congestion issue is enabling the vehicles to acquire the traffic-related content from the RSU, instead of obtaining it from the CN
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