Abstract
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.
Recommended Citation
A. Nahar et al., "A Hypergraph Approach to Deep Learning based Routing in Software-Defined Vehicular Networks," IEEE Transactions on Mobile Computing, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/TMC.2024.3520657
Department(s)
Computer Science
Publication Status
Early Access
Keywords and Phrases
Deep reinforcement learning; hypergraph; routing; software-defined networking; vehicular ad-hoc networks
International Standard Serial Number (ISSN)
1558-0660; 1536-1233
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024