Joint Optimization of the Deployment and Resource Allocation of UAVs in Vehicular Edge Computing and Networks

Yuke Zheng; Bo Yang; Cailian Chen

Joint Optimization of the Deployment and Resource Allocation of UAVs in Vehicular Edge Computing and Networks

Signal Processing 2020-06-16 v1 Systems and Control Systems and Control

Authors: Yuke Zheng , Bo Yang , Cailian Chen

Abstract

With the development of smart vehicles, computing-intensive tasks are widely and rapidly generated. To alleviate the burden of on-board CPU, connected vehicles can offload tasks to or make request from nearby edge server thanks to the emerging Mobile Edge Computing (MEC). However, such approach may sharply increase the workload of an edge server, and cause network congestion, especially in rural and mountain areas where there are few edge servers. To this end, a UAV-assisted MEC system is proposed in this paper, and joint optimization algorithm of the deployment and resource allocation of UAVs (JOAoDR) is proposed to decide the location and balance the resource and rewards of the UAVs. We solve a long-term profit maximization problem in terms of the operator. Numerical results demonstrated that our algorithm outperforms other benchmarks algorithm, and validated our solution.

Keywords

uav-assisted mec vehicular edge computing deployment optimization resource allocation joint optimization task offloading profit maximization

Cite

@article{arxiv.2006.08215,
  title  = {Joint Optimization of the Deployment and Resource Allocation of UAVs in Vehicular Edge Computing and Networks},
  author = {Yuke Zheng and Bo Yang and Cailian Chen},
  journal= {arXiv preprint arXiv:2006.08215},
  year   = {2020}
}

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9 pages

Joint Optimization of the Deployment and Resource Allocation of UAVs in Vehicular Edge Computing and Networks

Abstract

Keywords

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