Search Results (283)

Secure real-time data interaction between vehicles and transportation infrastructure, such as RSUs (V2R), can achieve intelligent and safe driving, as well as efficient travel services, in Internet of Vehicles (IoV), a secure and efficient V2R authentication protocol, which plays an important role. Recently, scholars have proposed a two-factor V2R authentication protocol for the IoV. However, subsequent research has shown that this protocol is vulnerable to insider and ephemeral secret leakage attacks, and cannot achieve perfect forward secrecy. To address these security flaws, an improved scheme was further proposed. Nevertheless, this paper points out that the improved scheme still has shortcomings: it cannot provide anonymity and perfect forward secrecy, exhibits insufficient session key secrecy, and remains vulnerable to password guessing attacks, RSU capture attacks, and suffers from inappropriate pseudo-identity update mechanisms. Therefore, a novel Physical Unclonable Function-based Lightweight V2R Authentication (PUF-LA) scheme is proposed, which uses Elliptic Curve Cryptography (ECC) to achieve perfect forward secrecy, uses PUF to resist devices captured attacks, and achieves two-factor secrecy protection against password guessing attacks. The security performance of PUF-LA is theoretically proved by leveraging the random oracle model. In contrast with relevant authentication schemes, PUF-LA is more secure and has low computation costs. Full article

Text-to-video retrieval offers an intelligent solution for Internet of Vehicles (IoV) users to access desired content on demand. However, the constrained communication channels in IoV, characterized by low signal-to-noise ratios (SNR), pose significant obstacles to retrieval performance. To tackle these issues, this study presents SemTVR, a semantic communication framework dedicated to achieving superior robustness in text-to-video retrieval tasks in low-SNR IoV environments. By integrating the semantic communication paradigm with edge–cloud collaboration, our architecture leverages roadside unit (RSU) features and cloud resources to enable collaborative retrieval. We introduce a multi-semantic interactive reliable transmission mechanism that utilizes historical search records to enhance semantic recovery accuracy under adverse channel conditions. Furthermore, we devise a cross-modal fine-grained matching strategy assigning differentiated weights to video content and query sentences. Experimental results conducted on authoritative datasets demonstrate that SemTVR significantly outperforms baseline methods in terms of search accuracy, particularly in low SNR scenarios, validating its effectiveness for future IoV applications. Full article

This work addresses the increasing complexity of urban mobility by proposing an intelligent optimization and resource-allocation framework for Vehicle-to-Infrastructure (V2I) communications. The model integrates a macroscopic mobility analysis, an Integer Linear Programming (ILP) formulation for optimal Road-Side Unit (RSU) placement, and a Smart Generic Network Controller (SGNC) based on Q-learning for dynamic radio-resource allocation. Simulation results in a realistic georeferenced urban scenario with 380 candidate sites show that the ILP model activates only 2.9% of RSUs while guaranteeing more than 90% vehicular coverage. The reinforcement-learning-based SGNC achieves stable allocation behavior, successfully managing 10 antennas and 120 total resources, and maintaining efficient operation when the system exceeds 70% capacity by reallocating resources dynamically through the $\lambda$-based alert mechanism. Compared with static allocation, the proposed method improves resource efficiency and coverage consistency under varying traffic demand, demonstrating its potential for scalable V2I deployment in next-generation intelligent transportation systems. Full article

Research on Cooperative Driving Automation (CDA) has advanced to overcome the limited perception range of onboard sensors and the difficulty of inferring surrounding vehicles’ intentions by leveraging vehicle-to-everything (V2X) communications. This paper models how an autonomous vehicle receives cooperative sensing and cooperative maneuvering information generated at an edge roadside unit (edge RSU) that integrates roadside units (RSUs) with multi-access edge computing (MEC), and how the vehicle fuses this information with its onboard situational awareness and path-planning modules. We then analyze the performance gains of edge RSU-enabled services across diverse traffic environments. In a highway-merging scenario, simulations show that employing the edge RSU’s sensor sharing service (SSS) reduces collision risk relative to onboard-only baselines. For unsignalized intersections and roundabouts, we further propose a guidance-driven Hybrid Pairing Optimization (HPO) scheme in which the edge RSU aggregates CAV intents/trajectories, resolves spatiotemporal conflicts via lightweight pairing and time window allocation, and broadcasts maneuver guidance through MSCM. Unlike a first-come, first-served (FCFS) policy that serializes passage, HPO injects edge guidance as soft constraints while preserving arrival order fairness, enabling safe concurrent passage opportunities when feasible. Across intersections and roundabouts, HPO improves average speed by up to 192% and traffic throughput by up to 209% compared with FCFS under identical demand in our simulations. Full article

The evolution towards 6G is set to transform Vehicle-to-Everything (V2X) networks by introducing advanced technologies such as Six-Dimensional Movable Antenna (6DMA). This technology endows Roadside Units (RSUs) with dynamic beam-steering capabilities, enabling adaptive coverage. However, traditional RSU deployment strategies, optimized for static coverage, are fundamentally mismatched with these new dynamic capabilities, leading to a critical deployment–optimization mismatch. This paper addresses this challenge by proposing DyDO, a novel Dynamic Deployment and Optimization framework for the utilization of 6DMA-RSUs. Our framework strategically decouples the problem into two modules operating on distinct timescales. On a slow timescale, an offline deployment module analyzes long-term historical traffic data to identify optimal RSU locations. This is guided by a newly proposed metric, the Dynamic Potential Score (DPS), which quantifies a location’s intrinsic value for dynamic adaptation by integrating spatial concentration, temporal volatility, and traffic magnitude. On a fast timescale, an online control module employs an efficient Sequential Angular Search (SAS) algorithm to perform real-time, adaptive beam steering based on immediate traffic patterns. Extensive experiments on a large-scale, real-world trajectory dataset demonstrate that DyDO outperforms conventional static deployment methodologies. This work highlights the necessity of dynamic-aware deployment to fully unlock the potential of 6DMA in future V2X systems. Full article

Network slicing in 5G systems enables different Quality of Service (QoS) for heterogeneous Vehicle-to-Everything (V2X) applications, yet efficiently allocating resource blocks from both 5G base stations and roadside units (RSUs) across multiple slices remains challenging. Existing approaches either pre-assign users to slices or rely on population-based metaheuristic algorithms that cannot guarantee deterministic real-time performance within the stringent 20 ms latency requirements of vehicular networks. This study formulates the resource allocation problem as an integer programming model that jointly optimizes slice selection and resource allocation to maximize weighted system transmission rate while satisfying heterogeneous QoS constraints. We develop a constructive heuristic algorithm that employs a hierarchical allocation strategy prioritizing 5G resources before RSU resources, coupled with a backfilling mechanism to exploit the remaining resource block capacity. Numerical experiments across abundant 5G and limited resource scenarios demonstrate the algorithm’s effectiveness. First, comparing against Random baseline validates the optimization model’s value, achieving 21.4–24.9% higher weighted throughput in an abundant 5G scenario and 42.5–51.0% improvement under a limited resource scenario. Second, performance evaluation with 500 users shows the proposed constructive heuristic achieves optimal solutions in abundant 5G resource scenarios and 3.5–5.7% optimality gaps in limited resource scenarios, while maintaining an execution time of under 20 ms, which satisfies real-time requirements and executes faster than Gurobi, Simulated Annealing and Round-Robin. Third, scalability analyses across 400–700 users demonstrate favorable performance scaling, as the optimality gap decreases from 5.3% to 3.4% with execution times consistently below 20 ms. The proposed heuristic achieves the highest service admission count while maintaining near-optimal system weighted transmission rate performance, ranking second only to Gurobi solver. Compared with other baseline algorithms, the proposed heuristic delivers a superior balance between solution quality and computational efficiency, confirming its real-time feasibility for large-scale V2X network deployments. Full article

This paper proposes a unified fault-tolerant batch authentication scheme for vehicular networks, designed to address key limitations in existing approaches, namely the segregation between in-vehicle and V2I authentication scenarios and the lack of fault tolerance in traditional batch authentication methods. Based on a hardware–software co-design philosophy, the scheme deeply integrates the security features of hardware such as Tamper-Proof Devices (TPDs) and Physical Unclonable Functions (PUFs) with the efficiency of cryptographic primitives like Aggregate Message Authentication Codes (MACs) and the Chinese Remainder Theorem (CRT). It establishes an end-to-end, integrated authentication framework spanning from in-vehicle electronic control units (ECUs) to external roadside units (RSUs), effectively meeting the diverse requirements for secure and efficient authentication among the three core entities involved in Internet of Vehicles (IoV) data collection: in-vehicle ECUs, vehicle gateways, and RSUs. Security analysis demonstrates that the proposed scheme fulfills the necessary security requirements. And extensive experimental results confirm its high efficiency and practical utility. Full article

The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article

The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article

The air–ground integrated Internet of Vehicles (IoV), which incorporates unmanned aerial vehicles (UAVs), is a key component of a three-dimensional intelligent transportation system. Task offloading is crucial to improving the overall efficiency of the IoV. However, blackhole attacks and false-feedback attacks pose significant challenges to achieving secure and efficient offloading for heavily loaded roadside units (RSUs). To address this issue, this paper proposes a reputation-aware, multi-objective task offloading method. First, we define a set of multi-dimensional Quality of Service (QoS) metrics and combine K-means clustering with a lightweight Proximal Policy Optimization variant (Light-PPO) to realize fine-grained classification of offloading data packets. Second, we develop reputation assessment models for heterogeneous entities—RSUs, vehicles, and UAVs—to quantify node trustworthiness; at the same time, we formulate the RSU task offloading problem as a multi-objective optimization problem and employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find optimal offloading strategies. Simulation results show that, under blackhole and false-feedback attack scenarios, the proposed method effectively improves task completion rate and substantially reduces task latency and energy consumption, achieving secure and efficient task offloading. Full article

Urban congestion causes further increases in travel times, fuel consumption and greenhouse-gas emissions. In this regard, we conduct a systematic study of a Genetic Algorithm (GA) for real-time routing in an urban scenario in Bethlehem City, based on a SUMO microsimulation that has been calibrated using real data from the field. Our work makes four main contributions: (i) the implementation of a reproducible GA framework for dynamic routing with explicit constraints and adaptive termination criterion; (ii) design of a weight sensitivity study for studying a multi term fitness function with travel time and waiting time, and optionally fuel usage; (iii) an edge-assisted distributed architecture on roadside units (RSUs) supported by cloud services; and (iv) specifying and refining the data set description and experimental protocol with a planned statistical analysis. Empirical evidence from the Bethlehem case study shows a consistent decline in total travel time under high congestion cases. Variations in the waiting time between different scenarios are exhibited, reflecting the trade-offs in the fitness weighting scheme. We recognize that we have some limitations, including the manual resolution of data and the inherent problem of differences between simulations and real world, and we are proposing a road-map towards a pilot deployment that handles these issues. Rather than proposing a new GA variant, we present a deployment-oriented framework-an edge- assisted GA with explicit protocols and a latency envelope, and a reproducible multi-objective tuning procedure validated on a city-scale network under severe congestion. Full article

In the Internet of Vehicles (IoV), vehicles need to process a large amount of perception data to support tasks such as road navigation and autonomous driving. However, their computational resources are limited. Therefore, it is necessary to explore the combination of vehicle–road cooperation with edge computing. Roadside units (RSUs) can provide data access services for vehicles, and deploying edge servers on RSUs can improve the data processing capability in IoV environments and ensure the sustainability of vehicle communications, thus supporting complex traffic scenarios more effectively. In this work, we study the deployment of RSUs in vehicle–road cooperative systems. To balance the deployment cost of RSUs and the quality of service (QoS) of vehicle users, we propose an RSU deployment optimization model with six objectives, including time delay, energy consumption and security when vehicles offload their tasks to RSUs, as well as load balancing and the number and communication coverage area of RSUs. In addition, we propose a Wasserstein generative adversarial network (WGAN)-based Two_Arch2 (WGTwo_Arch2) to solve this many-objective optimization problem to better ensure the diversity and convergence of the solutions. In addition, a polynomial variation strategy based on Lecy’s flight mechanism and a diversity archive selection strategy with an adaptive Lp-norm are also proposed to balance the exploratory and exploitative capabilities of the algorithm. The effectiveness of the proposed algorithm WGTwo_Arch2 for 6-objective RSU deployment optimization is verified by comparisons with five different algorithms. Full article

Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this challenge, we propose a Dual-Branch Feature Adaptive Gated Fusion Network (DFAGF-Net) that integrates satellite remote sensing imagery with traffic flow time-series data. The framework consists of three components: the Global Contextual Aggregation Network (GCA-Net) for capturing macro spatial layouts from remote sensing imagery, a Sequential Gated Recurrent Unit Attention Network (Seq-GRUAttNet) for modeling dynamic traffic flow with temporal attention, and a Hybrid Feature Adaptive Module (HFA-Module) for adaptive cross-modal feature fusion. Experimental results demonstrate that the DFAGF-Net achieves superior performance in accident hotspot recognition. Specifically, GCA-Net achieves an accuracy of 84.59% on satellite imagery, while Seq-GRUAttNet achieves an accuracy of 82.51% on traffic flow data. With the incorporation of the HFA-Module, the overall performance is further improved, reaching an accuracy of 90.21% and an F1-score of 0.92, which is significantly better than traditional concatenation or additive fusion methods. Ablation studies confirm the effectiveness of each component, while comparisons with state-of-the-art models demonstrate superior classification accuracy and generalization. Furthermore, model interpretability analysis reveals that curved highway alignments, roadside greenery, and varying traffic conditions across time are major contributors to accident hotspot formation. By accurately locating high-risk segments, DFAGF-Net provides valuable decision support for proactive traffic safety management and targeted infrastructure optimization. Full article

Integrated Sensing and Communication (ISAC)-enabled Roadside Units (RSUs) encounter significant performance trade-offs between target sensing and multi-user communication in complex urban environments, where conventional optimization methods are prone to converging to local optima and joint optimization methods often yield sub-optimal results due to conflicting objectives. To address the challenge of trade-off between sensing and communication performance, this paper proposes a hierarchical beamforming optimization solution designed to tackle joint sensing–communication problems in such scenarios. The overall optimization problem is decomposed into a two-level “leader-follower” structure. In the leader layer, we introduce a max–min strategy based on the bisection method to transform the non-convex Signal-to-Interference-plus-Noise Ratio (SINR) optimization problem into a second-order cone constraint problem and solve the communication beamforming vector. In the follower layer, the Signal-to-Clutter-plus-Noise Ratio (SCNR) maximization problem is converted into a Semi-Definite Programming (SDP) problem solved via the CVX toolbox. Additionally, we introduce a “spatiotemporal resource isolation” mechanism to project the sensing beam onto the null space of the communication channel. The hierarchical optimization solution jointly optimizes communication SINR and sensing SCNR, enabling an effective balance between sensing accuracy and communication reliability. Simulation results demonstrate the proposed method’s effectiveness in simultaneously improving sensing accuracy and communication reliability. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article