基于生成式人工智能的无人机辅助车联网任务卸载与资源分配

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xing Wang;Chao He;Wenhui Jiang;Wanting Wang;Xiaoyan Liu
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引用次数: 0

摘要

近年来,车联网(IoV)已成为智能交通系统的关键驱动力,为用户提供身临其境的互动体验。与此同时,无人驾驶飞行器(uav)由于其高灵活性、低成本和易于部署,在车联网领域已显示出广泛应用的巨大潜力。然而,随着车联网任务复杂性的增加,任务之间复杂的依赖关系会导致显著的延迟问题,而计算资源分布的不均匀又进一步加剧了这一问题。为了应对上述挑战,我们提出了一种由无人机辅助的物联网资源分配和任务卸载策略。首先,通过构建复杂的任务依赖模型,对任务进行拓扑排序,明确任务之间的依赖关系,从而优化任务执行顺序。其次,针对任务卸载和资源分配的核心问题,提出了多智能体深度确定性策略梯度(madpg)算法,设计了依赖感知的调度策略。该策略集成了任务依赖关系和无人机机动性特征,通过分析每个时间段的行动者和评论家网络行动奖励,实现无人机轨迹规划和任务调度的智能决策。为了进一步解决非凸优化问题,我们设计了一种基于联邦学习(FL)的智能数据缓存和计算卸载(Fed-IDCCO)算法,利用深度强化学习(DRL)技术。该方法处理大规模和连续的状态和动作空间,以获得车联网环境下的最佳任务卸载策略。该方法不仅有效地减少了任务处理延迟和能耗,而且显著提高了系统的整体性能。大量的实验结果表明,与几种现有的基准算法相比,所提出的方法在减少任务处理延迟、降低能耗、控制成本和提高缓存命中率方面具有独特的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative AI-Based Dependency-Aware Task Offloading and Resource Allocation for UAV-Assisted IoV
In recent years, the Internet of Vehicles (IoV) has emerged as a pivotal driving force within intelligent transportation systems, offering users immersive interactive experiences. Meanwhile, unmanned aerial vehicles (UAVs) have demonstrated substantial potential for widespread application within the IoV domain, attributed to their high flexibility, low cost, and ease of deployment. However, as the complexity of IoV tasks increases, complex dependencies among tasks give rise to notable delay issues, which are further exacerbated by the uneven distribution of computational resources. In response to the previously mentioned challenges, we suggest a strategy for resource distribution and task offloading aided by UAVs for IoV. Firstly, by constructing a complex task dependency model, tasks are topologically sorted to clarify the dependencies among tasks, thereby optimizing task execution order. Secondly, focusing on the core issues of task offloading and resource allocation, we present the multi-agent deep deterministic policy gradient (MADDPG) algorithm to devise a dependency-aware scheduling strategy. This strategy integrates task dependencies and UAV mobility characteristics, enabling intelligent decision-making for UAV trajectory planning and task scheduling by analyzing actor and critic network action rewards at each timeslot. To further tackle non-convex optimization problems, we design a federated learning (FL)-based intelligent data caching and computation offloading (Fed-IDCCO) algorithm, leveraging deep reinforcement learning (DRL) techniques. This approach handles large-scale and continuous state and action spaces to obtain optimal task offloading strategies within IoV environments. This methodology not only effectively reduces task processing delays and energy consumption but also significantly enhances the overall system performance. Extensive experimental results demonstrate that, compared to several existing benchmark algorithms, the suggested method offers unique benefits in diminishing delays in task processing, lowering energy usage, controlling costs, and improving cache hit rates.
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来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
期刊介绍: The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023. The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include: Systems and network architecture, control and management Protocols, software, and middleware Quality of service, reliability, and security Modulation, detection, coding, and signaling Switching and routing Mobile and portable communications Terminals and other end-user devices Networks for content distribution and distributed computing Communications-based distributed resources control.
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