A Deep Learning-based Routing Optimization (DLRO) framework that integrates an attention-based DL model with Federated Learning (FL) for efficient and privacy-preserving route prediction in urban vehicular networks is proposed.
Abstract
The increasing advances in smart mobility and connected systems have intensified interest in transforming urban mobility, with Vehicular Ad-hoc Networks (VANETs) technologies emerging as a key enabler for safer and more efficient cities. However, VANETs face significant routing challenges due to high vehicle mobility, dynamic network topologies, and privacy constraints. This paper proposes a Deep Learning-based Routing Optimization (DLRO) framework that integrates an attention-based DL model with Federated Learning (FL) for efficient and privacy-preserving route prediction in urban vehicular networks. The framework adopts a client–server architecture where local training is performed on each vehicle, and only model parameters are shared with the server. We evaluate DLRO using the SUMO simulator with a dataset containing vehicle trajectories across urban scenarios. Compared to baseline non-cooperative routing method, DLRO achieves up to 47.1–75% reduction in distance traveled. The Average Displacement Error (ADE) and Final Displacement Error (FDE) consistently decrease across training epochs, confirming the model’s predictive accuracy. These findings confirm that the integration of DL with FL provides an effective approach for privacy-preserving route optimization in urban vehicular networks.
With the rapid advancement of vehicular communication technologies, maintaining reliable connectivity in Vehicular Ad Hoc Networks (VANETs) has become a critical challenge due to high mobility, dynamic topology, and uneven traffic distribution. Frequent disconnections in Vehicle-to-Vehicle (V2V) communication lead to increased latency and reduced network performance. To address these issues, this research proposes an AI-assisted dynamic Roadside Unit (RSU) deployment framework that leverages real-time traffic density estimation to optimize communication infrastructure. The proposed system utilizes deep learning-based vehicle detection models to analyze real-time traffic images and estimate vehicle density across different road segments. The extracted traffic information is further processed using machine learning techniques to predict communication demand and identify potential connectivity gaps. Based on these predictions, the system dynamically activates, deactivates, or repositions RSUs to ensure continuous network coverage and reduce dependency on unstable V2V links. The optimization model focuses on minimizing communication delay, enhancing packet delivery ratio, and improving overall network reliability through adaptive RSU placement. Additionally, a hybrid communication approach combining V2V and Vehicle-to-Infrastructure (V2I) is employed to overcome connectivity loss in sparse or highly dynamic traffic conditions. Simulation results demonstrate that the proposed AI-driven framework significantly improves network throughput, reduces communication latency, and ensures stable connectivity compared to traditional static RSU deployment strategies. The system effectively adapts to varying traffic patterns, making it suitable for next-generation intelligent transportation systems and smart city applications.
Sayyada Fahmeeda, Shashank, Jyoti et al.· International journal of com...· 0 citations
Vehicular Ad Hoc Networks (VANETs) are a major enabling technology for Intelligent Transportation Systems (ITS), facilitating safety-critical, traffic-efficient vehicle-to-vehicle (V2V) communication. Nevertheless, due to the highly dynamic nature of the vehicular environment, heterogeneous mobility patterns that ensure stable clustering and consistent cluster head (CH) selection pose a challenge. Existing embedding-driven and graph-based vehicle clustering methods rely on localized, one-hop neighbourhood aggregation and assume homogeneous neighbour influence. To overcome these challenges, the paper proposes an attention-guided deep embedding framework that jointly models long-range structural interactions and heterogeneous vehicular influence, rather than restricting learning to immediate neighbours. The proposed algorithm trains latent vehicular representations that understand long-range structural interactions that go beyond immediate one-hop neighbourhoods using Deep Nonnegative Matrix Factorisation (DANMF). To capture the heterogeneous vehicle influence (varying mobility, distance over time of vehicles), the dominant connectivity patterns are highlighted by using an ensemble attention mechanism. This incorporates interaction-factor attention to highlight the high-weighted interaction patterns of latent representation, whereas, through vehicle-level proximity attention, the stable and behaviourally related neighbours are selectively highlighted. Clustering is done according to the learned embeddings, and cluster heads are selected based on the strongest affinity of vehicles to cluster centres, to guarantee that CHs represent varying influence of mobility and structural significance. Extensive simulations reveal that the proposed model outperforms baselines, with a maximum throughput of 7.1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:Mbps$$\end{document}, CH stability of 90%, and a minimum packet delay of 4.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:ms$$\end{document}, which translates to 36% higher throughput, 32% more stable CHs, and 40% reduced packet delay than state-of-the-art techniques. Thus, this way, the proposed model provides a robust solution for stable V2V communication. Not applicable.
A framework for DT-VANET is constructed, along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning, to improve the accuracy of the global model and a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles is developed.
Qasim Zia, Saide Zhu, Haoxin Wang et al.· 0 citations
Urban traffic congestion poses a growing challenge to efficient mobility, and existing forecasting and routing methods often struggle to support timely decisions in dynamic road networks. To address this issue, this study proposes a hybrid framework that combines a spatiotemporal graph attention network with the Quantum Approximate Optimization Algorithm (QAOA) for joint congestion prediction and path optimization. In the prediction stage, a multi-scale spatiotemporal encoder is developed to capture short-term, intra-day, and multi-day traffic variation patterns, while an adaptive graph learning mechanism is introduced to model hidden spatial correlations among road segments. In the optimization stage, the routing problem is formulated as a QUBO model and solved by an improved deep QAOA with hierarchical parameter sharing, which helps stabilize training and improve solution quality. The predicted congestion probabilities are further incorporated into the routing objective, enabling the optimization module to avoid highly congested areas. Experiments on the PeMS-BAY and METR-LA datasets show that the proposed method reduces the RMSE of 15-min traffic prediction by 5.4% compared with the strongest baseline. For 50-node routing tasks, it achieves an optimality ratio of 0.952, outperforming standard QAOA. In congestion-aware routing, the average travel time is further reduced by 13.7%. These results indicate that the proposed framework is effective for integrated traffic prediction and dynamic path planning.
Reliable Quality of Service (QoS) prediction is fundamental to the performance of next-generation Vehicle-to-Everything (V2X) communication, particularly in 5G-enabled cloud and edge environments that support autonomous driving, cooperative safety, and time-critical mobility services. This survey provides a structured review of QoS prediction approaches spanning machine learning, deep learning, reinforcement learning, protocol-level evaluation, and security-trust mechanisms. Existing methods are analyzed with respect to their modeling strategies, required datasets, spatio-temporal dependencies, and scalability under high-mobility conditions. A comprehensive dataset survey synthesizes real-world, open-access, and simulation-based resources, highlighting key variables such as SINR, RSRP/RSRQ, mobility patterns, traffic density, delay, and throughput that influence predictive accuracy. The review identifies persistent challenges, including limited multi-modal datasets, poor cross-scenario generalization, computational overhead, weak interpretability, and the absence of unified multi-metric prediction frameworks. Based on these insights, the paper outlines promising research directions for developing adaptive, explainable, and privacy-preserving QoS prediction models for emerging 5G/6G V2X systems.
Parita Patel, Mahesh M. Goyani· ITEGAM- Journal of Engineeri...· 0 citations
Results across heterogeneous urban scenarios show that the proposed framework improves vehicular communication performance in terms of packet delivery reliability and communication delay, while preserving competitive machine learning performance under deployment-oriented constraints.
Juan Pablo Astudillo León, Leticia Lemus Cárdenas, Luis J. de la Cruz Llopis et al.· IEEE Access· 0 citations
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