Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems, enabling vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing. However, the highly dynamic nature of VANETs introduces challenges, such as unpredictable network conditions, high latency, energy inefficiency, and task failure. This research addresses these issues by proposing a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization (PSO) for intelligent task offloading and resource allocation. The framework leverages supervised models for predicting optimal offloading strategies, reinforcement learning for adaptive decision-making, and PSO for optimizing latency and energy consumption. Extensive simulations demonstrate that the proposed framework achieves significant reductions in latency and energy usage while improving task success rates and network throughput. By offering an efficient, and scalable solution, this framework sets the foundation for enhancing real-time applications in dynamic vehicular environments.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 16 references that are in the map. First posted on arXiv 29 Apr 2025.
Builds on
4 of 16 references.
References · 4
- 2020Intelligent Task Offloading for Heterogeneous V2X Communicationscited by 8
- 2020DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computingcited by 8
- 2020Offloading and Resource Allocation with General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approachcited by 7
- 2019Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systemscited by 6
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- 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures
- 2026PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
- 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
- 2024Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation
- 2025An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing
- 2025TrajAware: Graph Cross-Attention and Trajectory-Aware for Generalisable VANETs under Partial Observations
- 2025Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
- 2024Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles