# FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing

arXiv: https://arxiv.org/abs/2305.01536 · HTML: https://mlsanity.com/paper/2305.01536/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2023.

## Built on by (most cited first, 5 of 5)

- [Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model](https://mlsanity.com/paper/2405.01555/) (2024)
- [Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks](https://mlsanity.com/paper/2502.03403/) (2025)
- [Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning](https://mlsanity.com/paper/2507.05722/) (2025)
- [Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network](https://mlsanity.com/paper/2407.07575/) (2024)
- [Joint Resource Optimization, Computation Offloading and Resource Slicing for Multi-Edge Traffic-Cognitive Networks](https://mlsanity.com/paper/2411.17782/) (2024)

## Abstract (arXiv)

Integrating unmanned aerial vehicles (UAVs) into vehicular networks have shown high potentials in affording intensive computing tasks. In this paper, we study the digital twin driven vehicular edge computing networks for adaptively computing resource management where an unmanned aerial vehicle (UAV) named FlexEdge acts as a flying server. In particular, we first formulate an energy consumption minimization problem by jointly optimizing UAV trajectory and computation resource under the practical constraints. To address such a challenging problem, we then build the computation offloading process as a Markov decision process and propose a deep reinforcement learning-based proximal policy optimization algorithm to dynamically learn the computation offloading strategy and trajectory design policy. Numerical results indicate that our proposed algorithm can achieve quick convergence rate and significantly reduce the system energy consumption.