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.
Abstract
Multi-hop vehicular wireless networks are highly susceptible to channel congestion due to dynamic topologies, heterogeneous traffic densities, and contention-based medium access. This paper proposes a deployment-aware predictive congestion control framework for vehicular communications, where packet transmission decisions are guided by supervised machine learning models optimized under predictive and computational constraints. Its novelty lies in coupling packet-level congestion prediction with deployment-aware model selection, allowing transmission-control models to be selected according to predictive quality, measured native-inference latency, and serialized model footprint. A packet-level dataset was constructed from realistic vehicle trajectories generated with SUMO using road networks extracted from OpenStreetMap for three topologically diverse cities: Liverpool, Rio de Janeiro, and Houston. These trajectories support vehicular network evaluation under heterogeneous urban conditions, enabling the assessment of predictive transmission decisions across different road topologies. Several supervised learning models were considered, including linear classifiers, neural networks, and ensemble machine learning architectures such as Random Forest, Weighted Soft Voting, and Stacking. Hyperparameter tuning was formulated as a multi-objective optimization problem and solved using the NSGA-II evolutionary algorithm, jointly maximizing predictive performance while minimizing inference latency and serialized model size. Pareto-efficient models were exported to ONNX format and executed through a native inference pipeline to evaluate their suitability for computationally constrained vehicular communication environments. 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.
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
Results show that coordinated MEC-side control of AI model selection and computation, under a shared deadline-aware objective, provides a robust latency–energy trade-off for MEC-assisted vehicular networks.
Inseok Song, S. Kang, Seyha Ros et al.· Italian National Conference...· 0 citations
Autonomous-vehicle perception systems increasingly operate across heterogeneous compute and communication
environments, where the most accurate model is not necessarily the model that provides the best end-to-end service quality. A
high-capacity perception model may improve recognition quality while increasing inference time, CPU consumption, memory
demand, and sensitivity to network conditions. This paper proposes a Quality-of-Service (QoS)-Aware Intelligent Model
Selection framework that predicts the expected QoS of candidate perception models from model, network, and hardware context
and dynamically selects a feasible model according to application priorities. The framework extends a network-aware
autonomous-vehicle benchmarking foundation with a QoS prediction layer, multi-objective utility function, Pareto filtering, and
a hysteresis-based switching controller. Three tabular prediction methods—linear regression, random forest, and gradient
boosting—are compared. A synthetic dataset of 1,800 observations is generated from six candidate model profiles, five network
conditions, three hardware conditions, and repeated measurements. The synthetic evaluation indicates that nonlinear predictors
outperform the linear baseline and that QoS-aware selection can reduce mean response time by approximately 31.8–42.1%
relative to an accuracy-only baseline in the modeled scenarios. Pareto and ablation analyses further show that latency and
resource terms materially influence the selected model. Because no real AV testbed experiment has yet been conducted, these
numerical results are explicitly treated as synthetic validation rather than empirical evidence. The paper therefore provides a
reproducible research design and a publication-oriented methodology whose final claims should be revalidated with measured
AV executions.
S. Singh, A. Mishra· International Journal for Re...· 0 citations
Future sixth-generation (6G)-oriented networks require programmable control that can adapt routing to latency and congestion without unsafe online exploration. This study evaluates offline multi-agent deep deterministic policy gradient (MADDPG) with behavior-adjusted training rewards for latency-aware path control in software-defined networking (SDN). Each traffic pair is modeled as an agent selecting one of three retained candidate paths, while centralized critics learn coordinated decisions from topology-specific Ryu–Mininet transition datasets. Nine policies are compared using ten paired seeds on fat-tree, mesh-grid, and WAN-corridors topologies under a deployed utilization–latency weighting of 0.60/0.40, together with flow-completion, latency, congestion, architectural-comparison, sensitivity, robustness, statistical, and controller-overhead analyses. The utilization-aware path heuristic achieves the strongest overall reward ranking. MADDPG is the strongest learned policy on fat-tree, is not significantly outperformed by any evaluated policy on mesh-grid, and remains statistically tied with completion-matched policies on WAN-corridors. Behavior adjustment is topology-dependent rather than uniformly beneficial. The exported policy requires approximately 52μs per joint decision, whereas complete control-loop timing is dominated by network-statistics polling. These results support offline multi-agent SDN control as a competitive, low-overhead option when interpreted jointly with topology structure, flow completion, and strong heuristic baselines.
A. Kyzyrkanov, Y. Nurakhov, Zhenis Otarbay et al.· Technologies· 0 citations
An Energy-Optimized Federated Aggregation architecture of Predictive Networking in Vehicular Cloud Architectures (EOFA-PNVC) integrating client selection, gradient compression, and an energy-aware weighting scheme with a forecasting head that handles short-horizon state prediction of networks is suggested.
S. Narayanan, Nilesh N. Thorat, Feroz Ahmed et al.· SN Computer Science· 0 citations
A Transmission Control Protocol (TCP) throughput map represents communication quality over road networks and supports communication-aware applications in intelligent transportation systems. Maintaining such a map online is challenging because vehicular measurements are sparse and unevenly distributed, network conditions vary rapidly, and sensing-budget constraints limit the number of vehicles from which onboard communication measurements can be uploaded at each time step. This work addresses online TCP throughput map maintenance under sparse vehicular observations and sensing-budget constraints. To support budget-constrained sensing, we combine discoverability-guided vehicle selection and probabilistic map updating within a digital twin (DT)-assisted vehicular sensing architecture. The resulting sensing-and-mapping method, referred to as Discoverability-aware and Statistical Mapping (DISMAP), maintains a spatio-temporal discoverability map to characterize historical sensing coverage and select vehicles that improve the coverage of under-represented regions. It then uses Gaussian Process Regression (GPR) as a probabilistic mapping engine to estimate the mean TCP throughput and predictive standard deviation, where the standard deviation is adjusted using local vehicle density. Simulation results show that DISMAP reduces the mean absolute error (MAE) and mean standard deviation (MSTD) by up to 23.7% and 37.5%, respectively, and achieves a prediction-interval miss rate (PIMR) of 0.048, which is close to the nominal value of 0.05. These results indicate a favorable balance among prediction accuracy, interval sharpness, calibration, and spatial representativeness across different traffic-density conditions.
Weiwei Hu, Yuichi Ohsita, Hideyuki Shimonishi· Italian National Conference...· 0 citations
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