用混合AI方法优化车载网络任务卸载，降低延迟与能耗
Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency
- 融合监督学习、强化学习和粒子群算法做任务卸载决策
- 延迟降低42%，能耗减少38%，任务成功率提升至96%
- 适合车联网实时应用开发与边缘计算系统设计者
车载自组织网络（VANETs）是智能交通系统的核心，使车辆能将计算任务卸载至附近的路边单元（RSUs）和移动边缘计算（MEC）服务器以实现实时处理。然而，VANETs的高度动态性带来了网络状况不可预测、延迟高、能耗大和任务失败等问题。本文提出一种混合人工智能框架，结合监督学习、强化学习与粒子群优化（PSO），实现智能任务卸载与资源分配。该框架利用监督模型预测最优卸载策略，强化学习实现自适应决策，PSO优化延迟与能耗。大量仿真表明，所提框架显著降低延迟与能耗，同时提升任务成功率与网络吞吐量。该方案为动态车载环境中实时应用的高效可扩展实现提供了基础。
原文摘要 · Abstract (English)
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.
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