Vehicle Network Research Articles

Innovative Application of 6G Network Slicing Driven by Artificial Intelligence in the Internet of Vehicles

ABSTRACTThe rapid growth of vehicle networks in the Internet of Vehicles (IoV) needs novel approaches to optimizing network resource allocation and enhancing traffic management. Sixth‐generation (6G) network slicing, when paired with artificial intelligence (AI), has enormous potential in this field. The purpose of this research is to investigate the use of AI‐driven 6G network slicing (NS) for efficient usage of resources and accurate traffic prediction in IoV systems. A unique network design is suggested, combining data‐driven approaches and dynamic network slicing. Data are acquired from vehicular sensors and traffic monitoring systems, and log transformation is used to handle exponential growth patterns like vehicle counts and congestion levels. The Fourier transform (FT) is used to extract frequency‐domain information from traffic data, which allows for the detection of periodic patterns, trends, and anomalies such as vehicle velocity and traffic density. The Dipper Throated Optimized Efficient Elman Neural Network (DTO‐EENN) is used to forecast traffic and optimize resources. This technology allows the system to predict traffic patterns and dynamically alter network slices to ensure optimal resource allocation while reducing latency. The results show that the suggested AI‐driven NS technique increases forecast accuracy and network performance while dramatically reducing congestion levels. The research indicates that AI‐driven 6G based NS offers a solid framework for optimizing IoV performance.

Mx-TORU: Location-aware multi-hop task offloading and resource optimization protocol for connected vehicle networks

Mx-TORU: Location-aware multi-hop task offloading and resource optimization protocol for connected vehicle networks

Retraction Note: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks

Retraction Note: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks

ConsensusPlus: Blockchain Driven Secure Data Sharing Protocol for Internet of Vehicles

The rapid expansion of the Internet of Vehicles (IoV) creates a distinct set of difficulties and opportunities for improving vehicular communication and data security. "ConsensusPlus" provides a revolutionary blockchain-based protocol for secure data sharing within the IoV. This protocol uses a proprietary consensus mechanism to enable real-time, reliable data sharing and integrity across a network of vehicles and infrastructure components. The integration of blockchain technology, which provides a decentralized framework for managing vehicular data in a transparent, immutable, and secure manner, is key to ConsensusPlus. By utilizing blockchain, the protocol improves data privacy and resilience to manipulation and cyber threats, which is critical in the IoV scenario where data sensitivity and security are key. ConsensusPlus also enables the smooth integration of existing vehicular communication protocols, such as Dedicated Short-Range Communications (DSRC) and Cellular V2X, with blockchain technology. This integration assures that the protocol is not only compatible with existing technologies, but also adaptable to future advances in vehicular communications. The ConsensusPlus protocol's performance has been evaluated through a number of simulations and real-world deployments, which have shown considerable gains in data security and operational efficiency inside the IoV. This protocol not only provides a reliable solution for secure data sharing, but it also promotes a scalable and efficient communication framework for the growing needs of the Internet of Vehicles. The implementation of ConsensusPlus could be a critical step in achieving a completely secure, decentralized, and efficient digital vehicular ecosystem.

Cooperative Localization of Multi-Agent Autonomous Aerial Vehicle (AAV) Networks in Intelligent Transportation Systems

Cooperative Localization of Multi-Agent Autonomous Aerial Vehicle (AAV) Networks in Intelligent Transportation Systems

Resource allocation optimization for effective vehicle network communications using multi-agent deep reinforcement learning

Resource allocation optimization for effective vehicle network communications using multi-agent deep reinforcement learning

Optimal Routing Protocol to Reduce Frequent Disconnection Problem with Improvement of Link Quality in Vehicle Network

Vehicular Ad-hoc Networks (VANETs) are currently receiving significant interest from academics because to the escalating traffic issues, particularly in densely populated nations. The rising incidence of accidents necessitates the implementation of an Intelligent Transportation System (ITS) that can effectively minimize and alleviate this trend. The existing routing protocols for VANETs address various scenarios and techniques for establishing dependable communication between vehicles and infrastructures. However, certain conditions, such as the stability of the link between vehicles during packet exchange, have not been well handled. This work presents the Link Reliability Data Forwarding Routing Protocol (LRDF-RP) as a solution to address the issue of frequent disconnections and improve route selection based on reliability in Mobility prediction in VANET. This study encompasses two essential contributions: (1) Enhancing the prediction routing protocol to transmit packets over a dependable route, and (2) Incorporating more logic in the selection of intermediate nodes towards the destination to get an increased Packet Delivery Ratio (PDR). The LRDF-RP employs the Prims Greedy algorithm for forwarding and the Predictive Perimeter method for recovery, with changes made to both algorithms to fulfil the specific demands of VANETs. The approach is evaluated based on several criteria including packet delivery ratio, throughput, end-to-end delay, consumption of energy, and average hop count.

Improving Transmission in Integrated Unmanned Aerial Vehicle–Intelligent Connected Vehicle Networks with Selfish Nodes Using Opportunistic Approaches

The integration of unmanned aerial vehicles (UAVs) into vehicular networks offers numerous advantages in enhancing communication and coverage performance. With the ability to move flexibly in three-dimensional space, UAVs can effectively bridge the communication gap between intelligent connected vehicles (ICVs) and infrastructure. However, the rapid movement of UAVs and ICVs poses significant challenges to the stability and reliability of communication links. Motivated by these challenges, integrated UAV–ICV networks can be viewed as vehicular delay-tolerant networks (VDTNs), where data delivery is accomplished through the “store-carry-forward” transmission mechanism. Since VDTNs exhibit social attributes, this paper first investigates the opportunistic transmission problem in the presence of selfish nodes. Then, by enabling node cooperation, this paper proposes an opportunistic transmission scheme for integrated UAV–ICV networks. To address the issue of node selfishness in practical scenarios, the proposed scheme classifies the degree of cooperation and analyzes the encounter probability between nodes. Based on this, information is initially flooded, and the UAV is selected for data distribution by jointly considering the node centrality, energy consumption, and cache size. Finally, simulation results demonstrate that the proposed scheme can effectively improve the delivery ratio and reduce the average delivery delay compared to state-of-the-art schemes.

Adaptive Ultra-Wideband/Pedestrian Dead Reckoning Localization Algorithm Based on Maximum Point-by-Point Distance

Positioning using ultra-wideband (UWB) signals can be used to achieve centimeter-level indoor positioning. UWB has been widely used in indoor localization, vehicle networking, industrial IoT, etc. However, due to non-line-of-sight (NLOS) and multipath interference problems, UWB cannot provide adequate position information, which affects the final positioning accuracy. This paper proposes an adaptive UWB/PDR localization algorithm based on the maximum point-by-point distance to solve the problems of poor UWB performance and the error accumulation of the pedestrian dead reckoning (PDR) algorithm in NLOS scenarios that is used to enhance the robustness and accuracy of indoor positioning. Specifically, firstly, the cumulative distribution function (CDF) map of localization under normal conditions is obtained through offline pretraining and then compared with the CDF obtained when pedestrians are moving on the line. Then, the maximum point-by-point distance algorithm is used to identify the abnormal base stations. Then, the standard base stations are filtered out for localization. To further improve the localization accuracy, this paper proposes a UWB/PDR algorithm based on an improved adaptive extended Kalman filtering (EKF), which dynamically adjusts the position information through the adaptive factor, eliminates the influence of significant errors on the current position information and realizes multi-sensor fusion positioning. The realization results show that the algorithm in this paper has a solid ability to identify abnormal base stations and that the adaptive extended Kalman filtering (AEKF) algorithm is improved by 81.27%, 58.50%, 29.76%, and 18.06% compared to the PDR, UWB, EKF, and AEKF algorithms, respectively.

Internet of vehicles intrusion detection method based on CFS-COA feature selection and spatio-temporal feature extraction

Abstract With the rapid spread of the Internet of Vehicles (IoV) technology, vehicle network security is facing increasingly severe challenges. Intrusion detection technology has become a crucial tool for ensuring the information security of IoV. Since the traffic data of the IoV is large and has spatio-temporal characteristics, most previous studies are based on a single deep learning method to extract temporal or spatial features, which does not fully extract features of IoV data. To address the above issues, a spatio-temporal feature extraction model with feature selection is proposed. First, to solve the problem of long detection time with huge data traffic, a new feature selection method is proposed to screen the optimal feature subset by combining the correlation-based feature selection method with the crayfish optimization algorithm (CFS-COA). Second, the selected optimal features are used in a spatio-temporal feature extraction model that combines a Temporal Convolutional Network and a Bidirectional Gated Recurrent Unit (TCN-BiGRU) for classification. Finally, the performance of the model is evaluated using two types of datasets: the NSL-KDD and UNSW-NB15 datasets for external communications, and the Car-Hacking dataset for in-vehicle networks. The experimental results indicate that the proposed model demonstrates high classification performance and lightweight characteristics, achieving 100% accuracy on the Car-Hacking dataset.

Load-Aware and Priority Adaptive Traffic Congestion Control Method in Vehicular Ad Hoc Network

Vehicular ad hoc networks (VANET) are a subset of mobile ad hoc networks communicating between vehicles and infrastructure. During vehicular congestion communication, nodes compete to acquire channels, causing the channels to become congested. The congestion on the vehicle network results in increased delay and packet loss, resulting in reduced VANET performance. To address this problem, we developed a load-aware and priority adaptive traffic congestion control method in vehicular ad hoc networks (VANETs). The proposed scheme identifies less-congested road segments based on the network's load and reduces traffic congestion by suggesting other routes between nearby roadside units (RSUs). This research aims to improve the efficiency of the vehicular environment by utilizing the movement of vehicles with (RSUs) and sharing the traffic load between them. Simulation results demonstrate the effectiveness of the proposed protocol in reducing congestion and enhancing the overall performance of VANETs. To validate the proposed algorithm, we have implemented and tested the proposed algorithm using Network Simulator 3 (NS3) for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios and computed the performance of the algorithm on different parameters of the network. The simulation result of the proposed load aware and priority adaptive traffic congestion control method in VANET improved the packet delivery ratio, packet lost ratio, and end-to-end delay by 96%, 4.1%, and 1102 milliseconds, when compared to TDCCA value of 92%, 5.7%, and 1154 milliseconds, respectively.

Blockchain Integration in UAV Networks: Performance Metrics and Analysis.

Blockchain technology has revolutionized the management of Unmanned Aerial Vehicle (UAV) networks by enhancing security, enabling decentralized control, and improving operational efficiency. This study assesses the efficiency of private blockchain architectures in UAV networks, specifically examining important performance metrics such as throughput, latency, scalability, and packet size. Furthermore, we evaluate the effectiveness of UAV networks when integrating private blockchain technologies, focusing particularly on key performance indicators such as area, altitude, and data rate. The scope of our work includes extensive simulations that employ a private blockchain to assess its impact on UAV operations. In the blockchain network, throughput decreased as the number of UAVs and transactions increased, while delay remained constant up to a certain point. In contrast, the UAV network saw improved throughput but increased delay with more UAVs and transactions. Changes in area and altitude had little impact on the blockchain network but increased delays in the UAV network. Higher data rates enhanced the UAV network by reducing latency and improving throughput, though this effect was less pronounced in the blockchain network. The aforementioned results highlight the potential and limitations of private blockchains in enhancing the durability and efficiency of UAV networks.

Visible Light Communication System Integrating Road Signs with the Vehicle Network Grid

Visible Light Communication System Integrating Road Signs with the Vehicle Network Grid

LDM-Enhanced Computation and Communication Codesign in Unmanned Aerial Vehicle Networks

LDM-Enhanced Computation and Communication Codesign in Unmanned Aerial Vehicle Networks

Corrections to “A Hierarchical Unmanned Aerial Vehicle Network Intrusion Detection and Response Approach Based on Immune Vaccine Distribution”

Corrections to “A Hierarchical Unmanned Aerial Vehicle Network Intrusion Detection and Response Approach Based on Immune Vaccine Distribution”

Blockchain-Aided Secure Communication Framework Intended for UAV Networks

Networks of Unmanned Aerial Vehicles (UAVs) have a lot of promise for use in commercial, military, and civil applications. The communication of UAV networks presents significant cybersecurity challenges as network sizes increase. Including blockchain technology in peer-to-peer UAV networks is one way to offer a safe, scalable communication method. Blockchain enables safe, decentralized, and collaborative communication between several entities. To increase the security and privacy of UAV network communication, this article proposes a method based on Blockchain Technology (BCT). The proposed model employs Elliptic Curve Diffie-Hellman (ECDH) to ensure confidentiality through secure key exchange and utilizes the Secure Hash algorithm (SHA) to maintain data integrity. The cloud platform is used to store technical data, integrity, authentication, availability, and confidentiality. The data is then stored on a public blockchain based on Ethereum to enable seamless BCT transactions. The suggested approach improves in defending private information against a variety of threats, including plaintext and ciphertext attacks.

An Anonymous and Efficient Authentication Scheme with Conditional Privacy Preservation in Internet of Vehicles Networks

The Internet of Vehicles (IoV) is an emerging technology that enables vehicles to communicate with their surroundings, provide convenient services, and enhance transportation systems. However, IoV networks can be vulnerable to security attacks because vehicles communicate with other IoV components through an open wireless channel. The recent related work suggested a two-factor-based lightweight authentication scheme for IoV networks. Unfortunately, we prove that the related work cannot prevent various security attacks, such as insider and ephemeral secret leakage (ESL) attacks, and fails to ensure perfect forward secrecy. To address these security weaknesses, we propose an anonymous and efficient authentication scheme with conditional privacy-preserving capabilities in IoV networks. The proposed scheme can ensure robustness against various security attacks and provide essential security features. The proposed scheme ensures conditional privacy to revoke malicious behavior in IoV networks. Moreover, our scheme uses only one-way hash functions and XOR operations, which are low-cost cryptographic operations suitable for IoV. We also prove the security of our scheme using the “Burrows–Abadi–Needham (BAN) logic”, “Real-or-Random (ROR) model”, and “Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool”. We evaluate and compare the performance and security features of the proposed scheme with existing methods. Consequently, our scheme provides improved security and efficiency and is suitable for practical IoV networks.

Evaluation of Machine Learning Models for Attack Detection in Unmanned Aerial Vehicle Networks

Nowadays, unmanned aerial vehicles (UAVs) are increasingly utilized in various civil and military applications, highlighting the growing need for robust security in UAV networks. Cyberattacks on these networks can lead to operational disruptions and the loss of critical information. This study evaluates five machine learning models—Random Forest (RF), CatBoost, XGBoost, AdaBoost, and Artificial Neural Networks (ANN)—for detecting attacks on UAV networks using the CICIOT2023 (Canadian Institute for Cybersecurity Internet of Things 2023) dataset. Performance metrics such as accuracy, precision, sensitivity, and F1 score were used to assess these models. Among them, CatBoost demonstrated superior performance, achieving the highest accuracy and the fastest prediction time of 6.487 seconds, making it particularly advantageous for real-time attack detection. This study underscores the effectiveness of CatBoost in both accuracy and efficiency, positioning it as an ideal choice for enhancing UAV network security. The findings contribute to addressing cybersecurity vulnerabilities in UAV networks and support the development of more secure network infrastructures.

Performance evaluation for Q-learning based anycast routing protocol in unmanned aerial vehicle networks with multiple base stations

Unmanned Aerial Vehicle (UAV) networks can be used for data transmission in emergency scenarios, relaying data from ground users to base stations (BSs). While UAV networks collaborating with multi-BSs can significantly enhance performance, existing UAV routing protocols predominantly focus on unicast routing and often neglect critical aspects such as base station discovery. In addition, the high mobility of UAVs and rapid changes in network topology also pose great challenges for existing multi-BS routing protocols to maintain efficient data transmission. Aiming at the above problems, this paper abstracts the routing of multi-base station UAV networks as anycast routing for dynamic networks and proposes a distributed anycast routing protocol called QARP to improve the data transmission performance. In QARP, base stations can be discovered automatically and parameters of Q-learning are dynamically adjusted to improve the efficiency of data transmission. The Link Duration Estimation is used to influence routing decision and dynamically adjust the hello message interval. A multiple base stations transmission value function is designed to indicate the performance of data transmission and is used to calculate the reward and update Q-table. The experimental results show that the QARP proposed in this paper outperforms existing multi-BS routing and Q-learning based routing protocols in terms of delay, packet delivery ratio and throughput in single base station and multiple base stations scenarios.

SimSANet: a simple sequential attention-aided deep neural network for vehicle make and model recognition

SimSANet: a simple sequential attention-aided deep neural network for vehicle make and model recognition