Vehicular Networks Research Articles

Efficient load distribution in heterogeneous vehicular networks using hierarchical controllers

Vehicle movement poses significant challenges in vehicular networks, often resulting in uneven traffic distribution. Fog computing (FC) addresses this by operating at the network edge, handling specific tasks locally instead of relying solely on cloud computing (CC) facilities. There are instances where FC may need additional resources and must delegate tasks to CC, leading to increased delay and response time. This work conducts a thorough examination of previous load balancing (LB) strategies, with a specific focus on software-defined networking (SDN) and machine learning (ML) based LB within the internet of vehicles (IoV). The insights derived from this research expedite the development of SDN controller-based LB solutions in the IoV network. The authors proposes the integration of a local SDN controller (LSDNC) within the FC tier to enable localized LB, addressing delay concerns. However, the information will be available to the main SDN controller (MSDNC) too. The authors explore the concept mathematically and simulates the formulated model and subjecting it to a comprehensive performance analysis. The simulation results demonstrate a significant reduction in delay, with a 125 ms difference when 200 onboard units (OBUs) are used, compared to conventional software-defined vehicular networks (SDVN). This improvement continues to increase as the number of OBUs grows. Our model achieves the same maximum throughput as the previous model but delivers faster response times, as decisions are made locally without the need to wait for the main controller.

Mobility-aware federated self-supervised learning in vehicular network

The development of the Internet of Things has led to a significant increase in the number of devices, consequently generating a vast amount of data and resulting in an influx of unlabeled data. Collecting these data enables the training of robust models to support a broader range of applications. However, labeling these data can be costly, and the models dependent on labeled data are often unsuitable for rapidly evolving fields like vehicular networks and mobile Internet of Things, where new data continuously emerge. To address this challenge, Self-Supervised Learning (SSL) offers a way to train models without the need for labels. Nevertheless, the data stored locally in vehicles are considered private, and vehicles are reluctant to share data with others. Federated Learning (FL) is an advanced distributed machine learning approach that protects each vehicle’s privacy by allowing models to be trained locally and the model parameters to be exchanged across multiple devices simultaneously. Additionally, vehicles capture images while driving through cameras mounted on their rooftops. If a vehicle’s velocity is too high, the captured images, donated as local data, may be blurred. Simple aggregation of such data can negatively impact the accuracy of the aggregated model and slow down the convergence speed of FL. This paper proposes a FL algorithm for aggregation based on image blur levels, which is called FLSimCo. This algorithm does not require labels and serves as a pre-training stage for SSL in vehicular networks. Simulation results demonstrate that the proposed algorithm achieves fast and stable convergence.

Efficient resource management in direct and indirect transmission in V2X communications

Autonomous driving systems consist of vehicles that are able to communicate not only with other vehicles but also with entities in their environment, forming vehicle-to-everything (V2X) communication. However, the V2X applications have intense requirements posing a significant challenge to the telecommunication infrastructure. In this work, we consider two types of transmission, i.e. direct and indirect, and we utilize analytical traffic-engineering models with a view to conduct a performance analysis of a vehicular network that enables V2X communication. We additionally propose two resource management strategies in order to decrease the request rejection probability and consequently ensure enhanced communication conditions. The results reveal that the proposed resource management strategies constitute a strong asset for improving the system’s service provisioning capability.

A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network

The explosive growth of the interconnected vehicle network creates vast amounts of data within individual vehicles, offering exciting opportunities to develop advanced applications. FL (Federated Learning) is a game-changer for vehicular networks, enabling powerful distributed data processing across vehicles to build intelligent applications while promoting collaborative training and safeguarding data privacy. However, recent research has exposed a critical vulnerability in FL: poisoning attacks, where malicious actors can manipulate data, labels, or models to subvert the system. Despite its advantages, deploying FL in dynamic vehicular environments with a multitude of distributed vehicles presents unique challenges. One such challenge is the potential for a significant number of malicious actors to tamper with data. We propose a hierarchical FL framework for vehicular networks to address these challenges, promising lower latency and coverage. We also present a defense mechanism, LFGuard, which employs a detection system to pinpoint malicious vehicles. It then excludes their local models from the aggregation stage, significantly reducing their influence on the final outcome. We evaluate LFGuard against state-of-the-art techniques using the three popular benchmark datasets in a heterogeneous environment. Results illustrate LFGuard outperforms prior studies in thwarting targeted label-flipping attacks with more than 5% improvement in the global model accuracy, 12% in the source class recall, and a 6% reduction in the attack success rate while maintaining high model utility.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

Joint Accuracy and Latency Optimization for Quantized Federated Learning in Vehicular Networks

Joint Accuracy and Latency Optimization for Quantized Federated Learning in Vehicular Networks

Joint optimization of UAV aided covert edge computing via a deep reinforcement learning framework

In this work, we consider an Unmanned Aerial Vehicle (UAV) aided covert edge computing architecture, where multiple sensors are scattered with a certain distance on the ground. The sensor can implement several computation tasks. In an emergency scenario, the computational capabilities of sensors are often limited, as seen in vehicular networks or Internet of Things (IoT) networks. The UAV can be utilized to undertake parts of the computation tasks, i.e., edge computing. While various studies have advanced the performance of UAV-based edge computing systems, the security of wireless transmission in future 6G networks is becoming increasingly crucial due to its inherent broadcast nature, yet it has not received adequate attention. In this paper, we improve the covert performance in a UAV aided edge computing system. Parts of the computation tasks of multiple ground sensors are offloaded to the UAV, where the sensors offload the computing tasks to the UAV, and Willie around detects the transmissions. The transmit power of sensors, the offloading proportions of sensors and the hovering height of the UAV affect the system covert performance, we propose a deep reinforcement learning framework to jointly optimize them. The proposed algorithm minimizes the system average task processing delay while guaranteeing that the transmissions of sensors are not detected by the Willie under the covertness constraint. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm to decrease the average task processing delay with comparison with other algorithms.

An Efficient Incentive Mechanism for Federated Learning in Vehicular Networks

An Efficient Incentive Mechanism for Federated Learning in Vehicular Networks

Samp-DSGD: Link Reliability Sampling Based Federated Learning for Vehicular Networks

Samp-DSGD: Link Reliability Sampling Based Federated Learning for Vehicular Networks

Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

Dependency-Aware Task Scheduling for Vehicular Networks Enhanced by the Integration of Sensing, Communication and Computing

Dependency-Aware Task Scheduling for Vehicular Networks Enhanced by the Integration of Sensing, Communication and Computing

Learning-based joint recommendation, caching, and transmission optimization for cooperative edge video caching in Internet of Vehicles

In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor–critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize content delivery efficiency and quality of experience. The proposed approach combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery.

Enhancing Vehicular Networks With Hierarchical O-RAN Slicing and Federated DRL

Enhancing Vehicular Networks With Hierarchical O-RAN Slicing and Federated DRL

Anti-Jamming Technique for IRS Aided JRC System in Mobile Vehicular Networks

Anti-Jamming Technique for IRS Aided JRC System in Mobile Vehicular Networks

A 3-D Printed Fully Transparent Liquid Antenna for Future Vehicular Networks

A 3-D Printed Fully Transparent Liquid Antenna for Future Vehicular Networks

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks.

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.

Privacy-preserving estimation of electric vehicle charging behavior: A federated learning approach based on differential privacy

With the popularity of connected electric vehicles, the openness and sharing of charging data between stakeholders allows a more accurate estimation of charging behavior, which is valuable for optimizing energy systems and facilitating travel convenience. However, to enable such an effective mechanism, the challenge of data security and privacy should be addressed. Federated learning in the vehicular network is appealing for utilizing individual vehicle data while preserving data privacy. We propose an improved local differential privacy-based federated learning approach for modeling charging session prediction problems while preserving user privacy against the threat from a honest-but-curious server. In this approach, all vehicles, within the coordination of a cloud server, collaboratively establish a global regression network through parameter exchange. Meanwhile, the servers may belong to third-party model owners and can be semi-honest when inferring private information on the collected model parameters. Hence, local differential privacy is adopted to perturb the parameters. Additionally, a combination of local and global models via elastic synchronization is proposed to improve the accuracy of the learned noisy global model. Through the test on a real data set, the results show the superiority of the proposed algorithm over traditional noisy federated learning methods. Furthermore, the practical value of the proposed method is validated with a real-world charging case. Such an accurate charging session prediction service for electric vehicle drivers facilitates charging and travel convenience in the green transportation world.

Device-free Vehicle Type Identification for Next Generations Intelligent Transportation System

With the substantial progress in the automotive industry, intelligent vehicle identification systems have become imperative tools for next generation transportation system planning. The advent of internet of vehicles and intelligent cellular vehicular networks has entered the transportation system into a new era of technology. In smart transportation system of beyond 5G/6G vehicular networks, vehicle type identification is of vital importance for ensuring safe operability of autonomous driving environ ments, electronic toll, and traffic management. It can be used in high efficiency roadway services, vehicle theft prevention, road condition monitoring, automatic driving, access control security, etc. During the recent years, channel state information (CS I) of WiFi signal has gained escalating traction for activity or gesture recognition, and person identification with improved robustness and classification accuracy. Therefore, in this research work, a WiFi based vehicle type identification system is proposed for beyond 5G/6G vehicular networks. In this novel approach, we start from vehicle detection using the variation in CSI of WiFisignals caused by the moving vehicle. For vehicle detection we simply apply a threshold based algorithm and then extract meaningful features. Afterwards, the vehicle type is identified using machine learning algorithms. This is a wireless model that enables commercial WiFi devices to identify different types of vehicles using CSI measurements. The presented framework can identify six different types of vehicles with an accuracy of 91.8%

A resilient event-triggered control strategy for truck platooning cyber–physical systems against denial-of-service attacks

With the deployment of vehicle-to-everything(V2X) communication technology, Denial-of-Service(DoS) attacks gradually pose potential threats for the truck platooning cyber–physical systems(TPCPS) due to disruption of information exchange in vehicular networks, resulting in instability of truck platooning and even traffic accidents. Motivated by this, the study proposes a resilient event-triggered control strategy to maintain the performance or stability of the TPCPS when DoS attacks happen. First, a resilient event-triggered mechanism is proposed to ensure that the onboard controller can receive and update status information in time after attack intervals, mitigating effect of the vehicle-to-vehicle(V2V) communication disruptions. Subsequently, the sufficient condition is derived which is to confine DoS attacks and makes a key role in maintaining the platoon’s internal stability. To guarantee the consensus control performance of the TPCPS, the switched event-triggered controller is designed by the Lyapunov approach. The controller is expected to output corresponding control based on the updated status information in communication interval. Ultimately, the proposed strategy’s effectiveness is validated through simulations. The proposed resilient event-triggered control strategy is shown to be able to effectively mitigate abnormalities in the TPCPS under DoS attacks, thus ensuring safe and comfortable driving. Compared with event-triggered sliding mode control, the proposed method achieves smaller inter-vehicle distances while ensuring stability, enhancing traffic efficiency.