# Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network

arXiv: https://arxiv.org/abs/2407.07575 · HTML: https://mlsanity.com/paper/2407.07575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 76 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks](https://mlsanity.com/paper/2208.01890/) (2022)
- [Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method](https://mlsanity.com/paper/2208.01283/) (2022)
- [Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons](https://mlsanity.com/paper/2309.10234/) (2023)
- [URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing](https://mlsanity.com/paper/2311.18352/) (2023)
- [Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network](https://mlsanity.com/paper/2401.09886/) (2024)
- [Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications](https://mlsanity.com/paper/2407.06518/) (2024)
- [Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2406.07213/) (2024)
- [A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications](https://mlsanity.com/paper/1807.07301/) (2018)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior](https://mlsanity.com/paper/2211.01294/) (2022)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2107.14772/) (2021)
- [Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR](https://mlsanity.com/paper/2311.04997/) (2023)
- [Time-Dependent Performance Modeling for Platooning Communications at Intersection](https://mlsanity.com/paper/2208.01213/) (2022)
- [FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing](https://mlsanity.com/paper/2305.01536/) (2023)
- [Energy-Efficient Task Offloading for Vehicular Edge Computing: Joint Optimization of Offloading and Bit Allocation](https://mlsanity.com/paper/1910.06652/) (2019)
- [Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks](https://mlsanity.com/paper/2208.14380/) (2022)

## Abstract (arXiv)

As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an emerging technology, can predict, estimate, and analyze real-time states by digitally modeling objects in the physical world. By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles. However, maintaining the vehicle DT model requires ongoing attention from the VEC server, which also needs to offer computing services for the vehicles. Therefore, effective allocation and scheduling of VEC server resources are crucial. This study focuses on a general VEC network with a single VEC service and multiple vehicles, examining the two types of delays caused by twin maintenance and computational processing within the network. By transforming the problem using satisfaction functions, we propose an optimization problem aimed at maximizing each vehicle's resource utility to determine the optimal resource allocation strategy. Given the non-convex nature of the issue, we employ multi-agent Markov decision processes to reformulate the problem. Subsequently, we propose the twin maintenance and computing task processing resource collaborative scheduling (MADRL-CSTC) algorithm, which leverages multi-agent deep reinforcement learning. Through experimental comparisons with alternative algorithms, it demonstrates that our proposed approach is effective in terms of resource allocation.