Internet Of Vehicles Environment Research Articles

Retraction Note to: An abnormal traffic detection method using GCN-BiLSTM-attention in the internet of vehicles environment

Retraction Note to: An abnormal traffic detection method using GCN-BiLSTM-attention in the internet of vehicles environment

A Double-Threshold Cooperative Spectrum Sensing Algorithm in the Internet of Vehicles

To address the shortage of wireless spectrum resources caused by the rapid development of the Internet of Vehicles, spectrum sensing technology in cognitive radio is employed to tackle this issue. In pursuit of superior outcomes, a double-threshold cooperative spectrum sensing algorithm is introduced. This algorithm enhances traditional energy detection technology to mitigate the high sensitivity to noise interference in the Internet of Vehicles environment. A double-threshold judgment mechanism can be established based on the uncertainty of noise. Varying fusion rules are implemented in the collaborative spectrum sensing scheme according to the density of vehicles and the spectrum resource demand. Simulation results demonstrate that the performance of the double-threshold cooperative spectrum sensing algorithm surpasses that of the traditional single-threshold energy detection scheme, particularly evident under lower Signal-to-Noise Ratio (SNR) conditions. Moreover, the proposed algorithm exhibits superior sensing performance in environments characterized by higher noise uncertainty.

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.

A blind signature scheme for IoV based on 2D-SCML image encryption and lattice cipher

Today’s Internet of Vehicles (IoV) faces many security risks in the data transmission process, and image data is more vulnerable to security threats in the transmission process due to its special characteristics such as large amounts of information and high visibility. Therefore, to guarantee the dependability of data transmission in the IoV environment, this paper designs a blind signature scheme for IoV based on two-dimensional sine cosine cross-chaotic mapping (2D-SCML) image encryption and lattice cipher (BSS-IoV). The innovation of this scheme is that it aims at blind signature of image information, blinds it before sending the information, and combines the lattice public key encryption algorithm to better ensure the safe and reliable transmission of information and reduce the risk of information disclosure. To further ensure the security of the scheme, an image encryption algorithm based on 2D-SCML and pixel splitting (2PS-IEA) is proposed, which is used to blind the information and thus reduce the risk of information leakage on the one hand, on the other hand, it is used in the signature process to ensure the security of the signed information. The 2D-SCML is derived from the cross-model structure proposed in this paper. Through simulation results and experimental analysis, the values of NPCR and UACI, respectively, 99.6094% and 33.4635%, are close to ideal values. And 50% of the cut image can also recover the rough information, which indicates that the signature scheme has the security against differential attacks, cut attacks and noise attacks. Moreover, the security analysis shows that the scheme has the anti-tamper, anti-repudiation and traceability.

Multiple-RIS-Aided Vibrio Foraging Optimization Positioning Algorithm Under Internet of Vehicles Environment

Multiple-RIS-Aided Vibrio Foraging Optimization Positioning Algorithm Under Internet of Vehicles Environment

Hybrid Features-Based Intrusion Detection for The Internet of Vehicles using Dynamic Adaptation

Abstract: The evolving landscape of the Internet of Vehicles (IoV) has brought to the forefront a discernible array of challenges about network security. In response, this study delves into applying deep learning-based intrusion detection techniques to fortify the IoV against potential network threats. Notably, prevailing approaches often rely on a singular deep learning model for either temporal or spatial feature extraction, with a serial sequence of spatial feature extraction followed by temporal feature extraction. Such methodologies tend to exhibit shortcomings in adequately capturing the Spatiotemporal intricacies inherent in IoV dynamics, thereby adversely impacting intrusion detection efficacy, and contributing to an elevated false-positive rate. To address these challenges, this research proposes an innovative intrusion detection method tailored for the IoV, premised on the parallel analysis of Hybrid features. The methodology commences with the construction of an optimal feature subset based on inter-feature correlations within IoV traffic. Subsequently, a parallelized application of Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) architectures is employed to extract spatio-temporal features from IoV traffic. In acknowledgment of the dynamic nature of IoV environments, a novel Dynamic Adaptation Mechanism is introduced. This mechanism continuously monitors the real-time IoV traffic, detecting feature drifts and triggering adaptations in the spatiotemporal feature extraction process. The adaptation requirements are then seamlessly communicated to the intrusion detection model through a Feedback Loop for Model Updating, ensuring that the model remains adept at discerning emerging network threats. The culmination of this process involves the fusion of parallelly extracted spatiotemporal features, facilitated by the self-attention mechanism. Subsequently, intrusion detection is executed utilizing a Multilayer Perceptron, providing a comprehensive framework that dynamically adapts to the evolving IoV environment. Empirical assessments utilizing the NSLKDD dataset demonstrate the efficacy of the proposed method, manifesting in a notable 2.05% reduction in the false-positive rate. Additionally, the proposed method surpasses baseline performance metrics, including accuracy and F1 score, thereby affirming its proficiency in enhancing intrusion detection capabilities within the Internet of Vehicles paradigm, especially in the context of the introduced Dynamic Adaptation Mechanism.

SBM-SA: A Safety Beacon Message Separation Algorithm for Privacy Protection in Internet of Vehicles

A safety beacon message (SBM) plays a pivotal role in the Internet of Vehicles (IoV), broadcasting crucial events and road conditions to nearby vehicles. Given the sensitive nature of the data, such as vehicle identity and location, ensuring privacy is paramount. The significance of this research lies in addressing the pressing need for comprehensive privacy protection in the IoV, especially as most existing schemes focus on safeguarding either vehicle identity or location data during exchanges with core servers. The primary objective of this article is to introduce an SBM separation algorithm, termed SBM-SA, designed to holistically protect both identity and location data. Utilising correlation analysis, the SBM-SA stands as an innovative anonymisation privacy algorithm. Through a simulated IoV environment, the accuracy and efficacy of an SBM-SA are meticulously analysed and juxtaposed against prevailing privacy protection schemes. The findings underscore the SBM-SA’s potential to significantly enhance privacy measures in the IoV. Implications of this research extend to shaping future privacy protection strategies of the SBM, emphasising the need for holistic and robust solutions in an increasingly interconnected vehicular landscape.

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.

Enhancing Privacy Preservation in Vehicular Trust Management Systems through Blockchain Technology

The advent of the Internet of Vehicles (IoV) has led to a significant surge in data generation from vehicles, prompting the innovative utilization of data transactions within the IoV environment. However, due to the inherent trustless nature of data transactions in IoV, concerns have arisen regarding the lack of trust between involved parties and the potential compromise of user privacy. To address these issues, blockchain technology emerges as a suitable decentralized distributed storage and security management solution, offering transparency and security in data transactions. In this study, we leverage blockchain to integrate with the IoV, devising a robust trust management framework, and devising a privacy protection scheme to safeguard user privacy concerns. Additionally, we employ the performance evaluation process algebra (PEPA) method for system modeling and performance analysis to assess the efficacy of our proposed solution. Empirical findings demonstrate that our approach effectively enhances the performance of data transactions within the IoV while ensuring that the privacy of users remains intact.

Retracted: Implementation of Driving Safety Early Warning System Based on Trajectory Prediction on the Internet of Vehicles Environment

Retracted: Implementation of Driving Safety Early Warning System Based on Trajectory Prediction on the Internet of Vehicles Environment

Learning IoV in 6G: Intelligent Edge Computing for Internet of Vehicles in 6G Wireless Communications

Abstract--In sixth-generation (6G) wireless communications, deep learning will still be essential for the Internet of Vehicles (IoV). 6G will bring new opportunities and challenges to current deep learning systems applied in the IoV environment. This article introduces a new framework named Learning IoV in 6G, focusing on the deep learning applications for IoV in 6G. We also apply the proposed framework in a flying base station (FBS) to evaluate the performance for future IoV in 6G.

Simulation based QoS aware dynamic caching scheme for heterogeneous content requests in vehicular edge computing

The Internet of Vehicles (IoV) has witnessed a substantial surge in its expansion during recent years, with the integration of edge servers emerging as an increasingly prevalent practice to cater to content requests that demand a superior quality of service. Nonetheless, conventional cache policies often prove inadequate for IoV applications, primarily due to their incapacity to effectively handle a diverse array of content requests, the high-speed mobility of vehicles, and the inherent instability of network connections. Within the scope of this study, we commence by bifurcating various content requests into two distinct categories: latency-sensitive content request and bandwidth-sensitive content request. Subsequently, we establish a model to evaluate the service quality under the joint consideration of different types of content requests, the mobility of vehicles and storage capacity of edge nodes. Furthermore, we transform the quality-of-service (QoS) penalty function into a system reward function. This adaption enables us to propose an innovative edge cache scheme founded upon the Deep Deterministic Policy Gradient (DDPG) algorithm of Reinforcement Learning (RL) method, which empowers dynamic adjustments in response to the evolving IoV environment. To validate the effectiveness of our proposed approach, we harness the Simulation of Urban Mobility (SUMO) traffic simulation software and construct a traffic road scenario based on a specific part segment of Nanjing Beltway. A wide-ranging set of contrast experiments was performed to ensure the improved performance of the DDPG based deep reinforcement learning method. Simulation experimental results show that the proposed algorithm converges quickly and outperforms existing algorithms in terms of service quality-hit ratio for latency-sensitive content and transmission speed for bandwidth-sensitive content.

Intrusion detection system for cyberattacks in the Internet of Vehicles environment

This paper presents a novel framework for intrusion detection specially designed for cyberattacks, such as Denial-of-Service, Distributed Denial-of-Service, Distributed Reflection Denial-of-Service, Brute Force, Botnets, and Sniffing, on vehicles that are situated in the Internet of Vehicles environment. We propose an intrusion detection system based on machine learning that is capable of detecting abnormal behavior by examining network traffic to find unusual data flows. In this paper, we have presented a strategy for intrusion detection through a careful evaluation and selection of the most effective techniques for the following steps of the machine learning process: (i) data preprocessing by using Z-score normalization that preserves the data distribution for the proposed method and handles outliers; (ii) feature selection by using a regression model that simplifies the model complexity and reduces the execution time; and (iii) model selection and training – Random Forest, Extreme Gradient Boosting, Categorical Boosting, Light Gradient Boosting Machine – with hyperparameter optimization to control the behavior in the training phase and to prevent overfitting. The effectiveness of the proposed solution is demonstrated by extensive numerical experiments carried out using the well-known standard datasets CIC-IDS-2017, CSE-CIC-IDS-2018, and CIC-DDoS-2019, both separately and merged. We achieved a high accuracy above 99.8% within a running time of 46.9 s and 0.24 s detection time for the three combined intrusion detection system datasets, thereby showing that the proposed intrusion detection system outperforms the previous methods introduced in the literature.

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.

Secure Digital Twin Migration in Edge-Based Autonomous Driving System

Digital twin technology is being applied increasingly in the Internet of Vehicles environment, but it still faces many challenges in terms of efficiency and security. In the field of digital twin-based autonomous driving, many previous works have been done to study efficient migration methods of digital twin models. But these works consider the migration process as a blackbox. We study the efficient migration method of the digital twin model between the edge computing nodes inside the blackbox. We propose three different migration strategies depending on the source of the initial data and the source of the updated data, and evaluate the efficiency of these strategies in terms of migration time in different network environments using the autonomous driving simulation platform CARLA. We then derive methods for selecting migration strategies under different network conditions. During the migration process, there may be external attacks on participating elements or networks. We analyze the security problems that may arise during the migration process and propose corresponding defense methods against such cyber attacks.

Fine-Grained Data Sharing With Enhanced Privacy Protection and Dynamic Users Group Service for the IoV

The Internet of Vehicles (IoV) is expected to play a revolutionary role in improving users&#x2019; driving experience and urban traffic governance. By widely absorbing emerging technologies including cloud computing, the future IoV evolution is leading towards providing more flexible and diversified data services. However, the publicly accessible IoV environment arouses the user&#x2019;s concerns about the leakage of data and personal privacy. Despite some cryptographic solutions have been proposed, they still raise challenges on privacy, efficiency and usability. To cope with these challenges, this paper first presents an efficient scheme PH-ABE-DS, which attains the full policy hiding by implementing the access control with the inner product. Besides, we design an efficient indirect revocation mechanism, to enable the cloud and users to update the ciphertext and user secret key with slight storage and computational overheads. On this basis, we then present the EA-PH-ABE-DS scheme, by resorting to edge computing, it further reduces the overheads of resource-constrained devices. We design a deployment model for EA-PH-ABE-DS in IoV to discuss its usability. Rigorous security proof and security properties analysis show that our proposal is secure and reliable. Finally, through detailed comparisons on theoretical and experimental, both our two schemes show their superiority over the latest related works in terms of functionality and performance. The simulation evaluates and demonstrates the practicality of our solutions in practical IoT scenarios.

An Efficient and Secure Blockchain Consensus Protocol for Internet of Vehicles

Conventional blockchain consensus protocols tailored for the Internet of Vehicles (IoV) usually face low transaction throughput, high latency, and elevated communication overhead issues. To address these issues, in this paper, we propose ESBCP, an efficient and secure blockchain consensus protocol for the IoV environment. Firstly, considering the significant performance differences among nodes in the IoV, we designed a blockchain consensus model for the IoV. Roadside units execute a trust evaluation mechanism to select high-quality vehicle nodes for the consensus process, thereby reducing the likelihood of malicious nodes in the consensus cluster. Secondly, we designed a node partition strategy to adapt to the dynamic feature of the IoV. Finally, addressing the mobility of nodes in the IoV, we introduced a dynamic unique node list. Vehicle nodes can promptly select nodes with high reliability from the list of communicable nodes to join their unique node list, while also promptly removing nodes with low reliability from their unique node list. Combining these strategies, we propose DK-PBFT, an improved Practical Byzantine Fault Tolerance consensus algorithm. The algorithm meets the efficiency and mobility requirements of vehicular networks. Through theoretical analysis, ESBCP could prevent external and internal security risks while reducing communication overhead. Experimental verification demonstrated that ESBCP effectively reduces consensus latency and improves transaction throughput. Our proposed ESBCP can be used in other application scenarios that require high consensus efficiency.

Efficient Anonymous Batch Authentication Scheme With Conditional Privacy in the Internet of Vehicles (IoV) Applications

The Internet of Vehicles (IoV) has emerged as a robust solution to improve real-time road traffic conditions, safety, and driving comfort. However, the road’s dynamic environment and network heterogeneity make communication insecure. It poses many security threats like man-in-the-middle attacks, impersonation attacks, replay attacks, etc. Therefore, the message exchanges and real identities of nodes over the IoV network need to be preserved. The existing authentication schemes are inefficient and incur computational and communication overhead. They are unsuitable for resource-constrained IoV networks due to their highly dynamic nature, open communication, and chaotic road environment. Therefore, this paper proposes an elliptic curve cryptography-based secure and efficient anonymous batch authentication scheme with conditional privacy (EABAS-CP) for the IoV environment. The EABAS-CP scheme achieves conditional privacy by tracing the malicious vehicle efficiently. The RSU’s distributed parameters generate a pseudo-identity for vehicles and unique signatures that do not require secure channels and trusted authorities (TAs) for secure communication. Furthermore, the EABAS-CP scheme supports batch signature verification in which multiple signatures can be verified simultaneously and efficiently. The Random Oracle Model (RoM) provides unforgeability against adaptive message attacks by using the Elliptic Curve Discrete Logarithm Problem (ECDLP). The AVISPA simulation tool is used to evaluate the proposed protocol for its reliability and protection from the attacks like man-in-the-middle and replay attacks. The proposed EABAS-CP scheme is also proven secure against replay, impersonation, stolen verifier, ephemeral secret leakage, and linkability attacks. The network performance of the EABAS-CP scheme has been evaluated in terms of end-to-end delay, throughput, average message delay, average message loss ratio, communication cost, and computation cost through the NS-2 simulator and compared with existing schemes to demonstrate its efficiency.

RETRACTED ARTICLE: An abnormal traffic detection method using GCN-BiLSTM-Attention in the internet of vehicles environment

In-vehicle network intrusion detection tasks, it is usually necessary to simultaneously meet the requirements of low computational power consumption, real-time response, and high detection accuracy. In response to the class imbalance problem in existing vehicle network anomaly flow detection methods, which leads to longer training convergence time and low detection accuracy, an anomaly flow detection method using GCN-BiLSTM-Attention is proposed. Firstly, Graph Convolutional Networks (GCN) is used to obtain spatial correlations between data streams. Secondly, obtaining the time correlation to predict the next time slice flow matrix by capitalizing the variant Bidirectional Long Short-Term Memory (BiLSTM) network. Last but not least, an attention mechanism is designed for extracting key information from the data stream. The results of experiment prove that the binary classification false positive rate, detection rate, and F1 value of the proposed GCN-BiLSTM-Attention-based anomaly flow detection method on the NSL-KDD dataset are 95.87%, 6.31%, and 94.25%, respectively; The false positive rate, detection rate, and F1 value on the CICID2017 dataset are 6.01%, 94.12%,and 94.36%, respectively. The proposed GCN-BiLSTM-Attention model has exceeded the compared methods in detecting abnormal traffic in the context of the Internet of Vehicles, and it can better preserve local features of traffic data.

Robust authenticated key agreement protocol for internet of vehicles-envisioned intelligent transportation system

Internet of Vehicles (IoV) is a wireless network, which employs well-established Internet protocols and networks to share information between vehicles, people, and infrastructure by utilising Vehicular Adhoc Networks (VANETs). IoV aims to increase road user safety and lower accident rates. It is one of the main components of an intelligent transportation system (ITS). ITS appears as the most crucial component since the cities in any country are evolving into digital civilisations as a result of the smart city concept, regardless of whether people are travelling within the city, to work, in school, or for other purposes. It can also be used to improve infrastructure utilisation and road safety (i.e., reduction in road accidents), in addition to enhancing traffic management and easing congestion. However, IoV-driven ITS is susceptible to several forms of attacks because of the insecure communication among the participating entities. We, therefore, propose a secure authentication protocol for IoV-driven ITS to stop these attacks from happening (in short, SAKP-ITS). Its resistance to a number of potential attacks is confirmed by the security analysis of the proposed SAKP-ITS. Additionally, SAKP-ITS is juxtaposed with other comparable competing methods put out in an IoV environment. Additionally, it has been found that SAKP-ITS outperforms competing schemes in terms of critical metrics like communication costs, computation costs, and security and functionality attributes. To test the effect of the proposed SAKP-ITS on the critical performance attributes, a practical implementation of SAKP-ITS is also presented.