基于深度学习的软件定义车载网络路由超图方法

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ankur Nahar;Nishit Bhardwaj;Debasis Das;Sajal K. Das
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引用次数: 0

摘要

软件定义车辆网络(SDVNs)通过实现动态和适应性通信基础设施,彻底改变了现代交通。然而,由于车辆网络具有复杂的时空动态特征,传统的基于图的模型难以准确捕捉车辆网络的动态通信模式。超图由于能够表示多向关系,因此提供了对这些动态的更细致的表示。在这个超图的基础上,我们提出了一种新的基于超图的路由算法。我们使用深度确定性策略梯度(DDPG)方法联合训练了一个包含卷积神经网络(CNN)和门控循环单元(GRU)的模型。该模型仔细提取空间和时间交通矩阵,捕捉诸如位置、时间、速度、相互依赖关系和距离等元素。集成的注意力机制改进了这些矩阵,确保了捕捉车辆动态的精度。这些组件的最终结果是路由决策既具有响应性又具有预见性。通过使用测试平台进行详细的实证实验,使用omnet++进行模拟,以及基于真实数据集的理论评估,我们展示了我们的方法的独特优势。此外,当对现有解决方案进行基准测试时,我们的技术在模型可解释性、延迟最小化、快速收敛、降低复杂性和最小化内存占用方面表现更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hypergraph Approach to Deep Learning Based Routing in Software-Defined Vehicular Networks
Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements such as location, time, velocity, inter-dependencies, and distance. An integrated attention mechanism refines these matrices, ensuring precision in capturing vehicular dynamics. The culmination of these components results in routing decisions that are both responsive and anticipatory. Through detailed empirical experiments using a testbed, simulations with OMNeT++, and theoretical assessments grounded in real-world datasets, we demonstrate the distinct advantages of our methodology. Furthermore, when benchmarked against existing solutions, our technique performs better in model interpretability, delay minimization, rapid convergence, reducing complexity, and minimizing memory footprint.
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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