Abstract
With the rapid development of mobile computing, mobile-edge computing (MEC) has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this article, we studied a UAV-assisted MEC system with multiplatoon vehicles. Our article aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a 2-D path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the nonconvexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming (SQP) method. The simulation results show that the proposed method significantly surpasses conventional schemes.
Highlights
W ITH the development of V2X communication technologies and the applications of AI-based algorithms, intelligent and networked platooning vehicles are increasingly becoming an essential means to improve road utilization efficiency and alleviate congestion [1]. They are considered to be a critical part of the future intelligent autonomous transportation systems
Mobile edge computing with the implement of unmanned aerial vehicle networks has been widely investigated from the perspective of resource scheduling and unmanned aerial vehicles (UAVs) trajectory design [6], [13]–[17]
Existing works [17], [25]–[30] mostly take the direct addition of computed data and energy consumption as the component of objective function, while it is necessary to consider environmental factors or user preferences in the optimization objective for actual application scenarios. It can be seen from the related works mentioned above that few researches have focused on the resource scheduling of UAV-assisted Mobile edge computing (MEC) system with vehicles in a two-dimensional multi-lane platoon as end users
Summary
W ITH the development of V2X communication technologies and the applications of AI-based algorithms, intelligent and networked platooning vehicles are increasingly becoming an essential means to improve road utilization efficiency and alleviate congestion [1] They are considered to be a critical part of the future intelligent autonomous transportation systems. The UAVs and platooning vehicles can form A2G vehicular network systems to provide computation and communication services to users Users can offload their computationally-intensive tasks to the MEC servers, which dramatically shortens the calculation time and improves users’ quality of experience. The UAV-to-vehicles communication and computation system is regarded as a practical idea to promote the application of intelligent autonomous transport systems [11], [12], there are some challenges to be settled because of the constrained resources and complex dynamics of twodimensional platooning vehicles. The limited energy needs to be well scheduled by optimizing communication and computation resources to improve the energy efficiency of MEC service to guarantee the quality of service (QoS) of the UAV-enabled MEC system
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