# Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles

arXiv: https://arxiv.org/abs/2201.09076 · HTML: https://mlsanity.com/paper/2201.09076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 28 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Mobile Edge Intelligence and Computing for the Internet of Vehicles](https://mlsanity.com/paper/1906.00400/) (2019)
- [Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections](https://mlsanity.com/paper/2105.01357/) (2021)
- [The Paradigm of Digital Twin Communications](https://mlsanity.com/paper/2105.07182/) (2021)
- [Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles](https://mlsanity.com/paper/2007.04350/) (2020)
- [Joint Offloading and Resource Allocation in Vehicular Edge Computing and Networks](https://mlsanity.com/paper/1807.08718/) (2018)
- [Energy Efficient and Delay Aware Vehicular Edge Cloud](https://mlsanity.com/paper/2004.07170/) (2020)

## Abstract (arXiv)

Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload computing tasks. However, due to the high mobility of vehicles, the dynamics of the wireless conditions, and the uncertainty of the arrival computing tasks, it is difficult for a single vehicle to determine the optimal offloading strategy. In this paper, we propose a Digital Twin (DT) empowered task offloading framework for Internet of Vehicles. As a software agent residing in the cloud, a DT can obtain both global network information by using communications among DTs, and historical information of a vehicle by using the communications within the twin. The global network information and historical vehicular information can significantly facilitate the offloading. In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT. Accordingly, we model the offloading scheduling process as a Markov Decision Process (MDP) to minimize the long-term cost in terms of a trade off between task latency, energy consumption, and renting cost of clouds. Simulation results demonstrate that our algorithm can effectively find the optimal offloading strategy, as well as achieve the fast convergence speed and high performance, compared with other existing approaches.