Internet Of Vehicles Network Research Articles

Advanced System for Optimizing Electricity Trading and Flow Redirection in Internet of Vehicles Networks Using Flow-DNET and Taylor Social Optimization

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework.

XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles

The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100 %, precision of 100 %, recall of 100 %, F1-score of 100 %, and Matthew Correlation Coefficient of 100 % in binary-class situations for internal vehicular (CAN) networks and achieved 99.88 % accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.

A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles.

The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.

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.

TM–IoV: A First-of-Its-Kind Multilabeled Trust Parameter Dataset for Evaluating Trust in the Internet of Vehicles

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network.

Adaptive Caching Strategies for IoV Based on LTE Signal Quality Analysis

The Internet of Vehicles (IoV) enhances vehicular communication, promising better connectivity and user experiences. However, maintaining stable data delivery in dynamic vehicular environments is challenging. This paper introduces Mob-Cach, a novel mobility-aware cache management strategy that leverages real-time vehicular mobility information. Our preliminary study on LTE signal quality shows that mobility patterns significantly impact signal degradation. Based on these findings, we developed a dynamic caching framework that adapts to real-time mobility data, optimizing content delivery and network performance. Simulations demonstrate significant improvements in cache hit rates, reduced latency, and overall network efficiency. This research lays a foundation for future advancements in IoV networks, addressing the challenges of mobile and dynamic network topologies.

Enhancing routing and quality of service using AI-driven technique for internet of vehicles contexts

This paper introduces an Enhancing Routing and Quality of Service (QoS) Using AI-driven Technique for Internet of Vehicles (IoV) Contexts. The proposed approach aims to enhance QoS and overall network performance in connected vehicle networks. Vehicles utilize Road-Side Units and the Internet to aggregate data from neighboring vehicles, optimizing routing and data management. The proposed approach utilizes a graph traversal algorithm, a widely adopted technique in Artificial Intelligence for graph search, which facilitates traversal in routing. The proposed approach integrates a mobility score based on metrics such as velocity, acceleration, and neighboring information, ensuring optimal routing for dynamic vehicular networks. Objectives include improving QoS and network performance by reducing overheads, optimizing load balancing, and extending network lifetime. Implemented in NS-3 and MOVE simulators, results demonstrate significant performance enhancements compared to existing approaches. This approach addresses the challenges of extensive vehicle mobility and frequent topology changes in connected environments. Overall, the paper presents a comprehensive solution for IoV networks, combining AI-driven routing with mobility-aware strategies to advance network efficiency and QoS.

VESecure: Verifiable authentication and efficient key exchange for secure intelligent transport systems deployment

The Intelligent Transportation Systems (ITS) is a leading-edge, developing idea that seeks to revolutionize how people and things move inside and outside cities. Internet of Vehicles (IoV) forms a networked environment that joins infrastructure, pedestrians, fog, cloud, and vehicles to develop ITS. The IoV has the potential to improve transportation systems significantly, but as it is networked and data-driven, it poses several security issues. Numerous solutions to these IoV issues have recently been put forth. However, significant computing overhead and security concerns afflict the majority of them. Moreover, malicious vehicles may be injected into the network to access or use unauthorized services. To improve the security of the IoV network, the Mayfly algorithm is used to optimize the private keys continuously. To address these difficulties, we propose a novel VESecure system that provides secure communication, mutual authentication, and key management between vehicles, roadside units (RSU), and cloud servers. The scheme undergoes extensive scrutiny for security and privacy using the Real-or-Random (ROR) oracle model, Tamarin, and Scyther tools, along with the informal security analysis. An Objective Modular Network Testbed in OMNet++ is used to simulate the scheme. We prove our scheme's efficiency by comparing it with other existing methods regarding communication and computation costs.

Two-Factor Privacy-Preserving Protocol for Efficient Authentication in Internet of Vehicles Networks

Two-Factor Privacy-Preserving Protocol for Efficient Authentication in Internet of Vehicles Networks

Secure and lightweight message dissemination framework for internet of vehicles

AbstractThe Internet of Vehicles (IoV) revolutionizes vehicle communication in dynamic networks. Message dissemination in IoV involves sharing critical information for the safety and convenience of the IoV network. It is very crucial to secure message dissemination due to potential cyber‐attacks, traffic disruptions, and privacy breaches. Data integrity, authentication, and privacy are vital to maintaining trust and safety in the IoV network. This network consists of resource‐constrained IoV devices with limited resources due to the availability of embedded components in vehicular systems. Therefore, optimizing algorithms and protocols is crucial for efficient vehicle‐to‐everything (V2X) communication, enhancing safety and transportation efficiency. Solutions often include lightweight protocols and secure message exchange. This paper proposes a machine learning (ML) based secure and lightweight message dissemination framework for resource‐constrained IoV. First, we present an ML‐based threat classification model capable of effectively categorizing adversarial and nonadversarial data streams and delivering an optimized model with superior accuracy. Furthermore, we also propose a secure message dissemination scheme using lightweight cryptographic primitives, which significantly reduces computational, communication, and energy overhead. To validate the robustness of our proposed ML‐based secure and lightweight message dissemination framework, we evaluate it using various security parameters and performance measures such as computation cost, communication cost, energy cost, accuracy, precision, recall, and F1‐score. Our contributions promise to significantly enhance the security and efficiency of message dissemination in IoV environments and advance lightweight, secure, and reliable transportation systems for the future.

Performance assessment of full-duplex two-way Internet-of-vehicles relay networks under practical imperfect conditions

Contemporary Internet-of-Things (IoT) devices in general and Internet-of-Vehicles (IoV) networks in specific are taking the benefits from using full-duplex (FD) communication with the help of relay nodes to facilitate data transmission. However, self-interference between transmit and receive antennas at the relay node may cause big trouble if self-interference cancellation (SIC) is not applied. In this paper, a rigorous analysis on the performance of FD two-way relay networks (FDTWRNs) for IoV applications under imperfect conditions is provided. Different from earlier studies on the performance of FDTWRNs where one or two imperfect factors were considered, this work simultaneously takes into account many practical issues in the implementation of the IoV networks at the physical layer, including errors in channel state information at all channel links, imperfect SIC and transceiver hardware impairment at all nodes. For relaying protocol, we consider amplify-and-forward with both fixed-gain (FG) and variable-gain (VG) protocols. The exact closed-form expressions (in terms of the system parameters) of key performance factors such as signal-to-interference-plus-noise-distortion-and-error ratio (SINER), outage probability (OP), achievable data rate (ADR), and average symbol error probabilities (ASEP) of the considered system over Rayleigh fading channels and under the combined effect of imperfect CSI, SIC, and transceiver hardware are derived mathematically using probabilistic model analysis. The correctness of the analysis is then verified by Monte Carlo simulations. Drawing upon both analytical and numerical findings, the efficacy of the FDTWRNs under consideration is meticulously and comprehensively assessed in comparison to its performance in ideal conditions characterized by one or multiple critical factors. The disparity between perfect and imperfect scenarios serves as a valuable metric for appraising the practical viability of the considered FDTWRNs when deployed in practical IoV networks.

Recent Survey on Internet of Vehicles: Architecture, Applications, Challenges, and Its Solutions

Abstract The fame of the Internet of vehicles (IOV) increases the complexity of managing the vast network of interconnected vehicles, devices, and systems, which causes the need for seamless connectivity among vehicles and infrastructure. Developing robust and efficient data transmission technologies is imperative to ensure that IOV functions smoothly and can not only handle the substantial volume of data generated by IOV but also address factors of latency, bandwidth, and scalability to meet the evolving demands of modern transportation systems. Therefore, experts are actively exploring novel solutions and advancements to tackle these challenges to promote IOV to new heights in transportation. In this survey paper, the authors surveyed the IOV of different architectures, provided a comprehensive overview of the Internet of things, vehicular ad hoc networks, and IOV networks, and compared the various networks. Similarly, they studied the multiple challenges of IOV and the corresponding solution available in recent literature. This paper describes the IOV evolution and examines several research papers related to the IOV and research guidance, such as high mobility, load balancing, security, heterogeneity, location accuracy, secure routing, etc.

Secure and Reliable Routing in the Internet of Vehicles Network: AODV-RL with BHA Attack Defense

Secure and Reliable Routing in the Internet of Vehicles Network: AODV-RL with BHA Attack Defense

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

Attribute and User Trust Score-Based Zero Trust Access Control Model in IoV

The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, thereby endangering vehicle users’ privacy and personal safety. An Attribute and User Trust Score-based Zero Trust Access Control Model (AU-ZTAC) is proposed, combining the zero-trust and attribute-based access control models to meet network protection requirements while achieving fine-grained dynamic access control and incorporating trust evaluation in the access control process to better reflect users’ intent. Experimental results demonstrate the effectiveness and feasibility of trust assessment through the proposed model. A comparison with the classical schemes illustrates that AU-ZTAC allows for more flexible and fine-grained access control in complex access control environments while improving IoV security.

V2XCom: Lightweight and secure message dissemination scheme for Internet of vehicles

AbstractSmart cities provide a sustainable transport ecosystem to connect smart vehicles through sensors and networking units. Internet‐of‐vehicles (IoV) is vital in disseminating various messages, including road safety, exact location sharing, road accidents and blocks, collision warning, driver assistance, network congestion, or toll payment among vehicle‐to‐anything (V2X) units. Due to the mission‐critical nature of the IoV ecosystem, it requires reliable, lightweight, and real‐time communication for vehicle‐to‐vehicle (V2V) and V2X units. However, due to the availability of insecure wireless channels, an adversary can perform several security attacks such as replay, password guessing, masquerade, trace, message tampering, Man‐in‐the‐middle attack (MIMA), and plain‐text attacks in an IoV environment which may lead to potential disruptions. Motivated by the aforementioned facts, we propose a V2XCom, a lightweight and secure message dissemination scheme for the IoV network using low‐cost cryptographic SHA‐256, XoR operation, and concatenation. We performed security verification of V2XCom using the Scyther and AVISPA tools. Moreover, security proofs are provided for an informal security analysis of the proposed scheme. We have done a comparative analysis of a V2XCom with recent dissemination schemes in the IoV ecosystem concerning security features, communication latency, computational cost, and energy usage.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

Toward Optimal Deployment of UAV Relays in UAV-Assisted Internet of Vehicles

The past decade has witnessed an explosive growth of unmanned aerial vehicles (UAVs), which have attracted great attention in assisting Internet of Vehicles (IoV) communication systems thanks to the advantages of cost-effectiveness, high maneuverability, and flexible deployment. Yet several issues, such as UAVs' transmit power and energy resources as well as the high-speed mobility of IoV user equipment (VUE), pose challenges to the development of UAVs in UAV-assisted IoV networks. To provide reliable and high-throughput wireless links for VUEs in a given ground area, this paper considers a scenario in which a ground VUE is facing difficulties in receiving service from a ground roadside unit (RSU) owing to possible sudden RSU malfunction or poor signal coverage. In this case, UAVs are generally deployed in the air, serving as relays to establish communication links between uncovered VUE and the neighboring RSU. To maximize the system capacity, we investigate the deployment problem of UAV relays under single-UAV relaying and multi-UAV relaying cases upon considering VUE mobility, and exploit the optimal deployment strategy of UAV relays under fixed total power consumption. Comprehensive simulation results verify that the proposed algorithm can outperform benchmark methods on significant critical indicators such as the average achievable user rate, and the outage probability. Also, evaluations and analyses have highlighted the effects of key system parameters (e.g., altitude, number, and transmit power of UAV relays) on optimal UAV relay deployment position and system performance.

Physical layer authentication in the internet of vehicles through multiple vehicle-based physical attributes prediction

The high security and low complexity of physical layer authentication (PLA) make it a promising complement for complex cryptographic authentication approaches, especially for Internet of Vehicles (IoV) systems constrained by limited computational capabilities and facing increasing security threats due to the broadcast nature of their communications. However, existing PLA schemes exploiting geographical location information to predict channel characteristics for authentication face the challenge of location falsifying/spoofing attacks. Moreover, they either require cryptographic-based initial authentication or cannot be applied to moving vehicle scenarios. To tackle these challenges, we propose a PLA scheme based on the Gaussian process (GP) regression that jointly considers the location and speed attributes of vehicles to enhance the reliability of authentication in the IoV network. Specifically, the historical channel state information attributes together with the location and speed information of transmitters are utilized to establish a mapping and train a GP model to predict the next legitimate location and speed of a transmitter for authentication. First, for vehicle-to-road side unit (RSU) authentication, the trained GP model is stored on RSU and used to authenticate vehicles entering its vicinity by cross-verifying their reported location and speed information with the ones predicted by the model. Next, for vehicle-to-vehicle authentication, we propose an RSU-assisted authentication, where the RSU in the location shared by two vehicles is used to assist in verifying the validity of their reported location and speed information. Finally, for RSU-to-vehicle authentication, we leverage the path loss and angle of arrival of a signal from RSU to estimate and cross-verify its location. We utilize QuaDRiGa, a quasideterministic radio channel generator to generate realistic channels for experimental validations. The results of simulation tests conducted demonstrated that our approach significantly improves authentication performance compared with the existing approaches.

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

SummaryThe Internet of Vehicles (IoV) integrates various technologies to enable real‐time data exchange between vehicles, pedestrians, roadside units, unmanned aerial vehicles, road‐added things, and so forth. IoV is an improved internet‐enabled vehicular ad hoc network (VANET), which is similarly affected by data traffic aggravated by time‐variant network density and the network's topological and informational changes. A high density of simultaneous vehicular communication leads to channel saturation and data congestion, which leads to rapid increases in data loss, packet latency, and lower service quality. Due to the importance of real‐time data communications in high dynamic networks, we used the systematic literature review (SLR) approach to identify 54 articles up to May 2023 to discuss the ways to deal with data traffic congestion in VANETs and IoV networks as well as existing and upcoming issues in this area.