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As a growing up-and-coming branch of the Internet of Things and traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) is intended to perform as a core information carrying and processing platform for Intelligent Transport Systems. However, owing to its dynamic topological structures, large network scale, and mobile limitation, IoV systems still struggle with many unresolved challenges, especially those concerning security and privacy. Therefore, researchers consider security a significant concern due to the variety of vulnerabilities. Recently, existing security researchers have worked extensively to guarantee IoV security. However, multiple challenges in terms of security and privacy are generated by various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, and data authenticity attacks. In this context, we provide a systematic review of emerging security and privacy vulnerabilities IoV and identify current challenges and the remaining open issues. Furthermore, we will discuss the future of this area. The paper contributes by presenting significant issues and solutions in a clear, smooth, comprehensive, and integrated way. [ABSTRACT FROM AUTHOR]

As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency. [ABSTRACT FROM AUTHOR]

In fifth-generation (5G)-based vehicular networks, vehicles share information about the road's condition to make driving safer and alleviate traffic congestion. Given the public nature of the medium, addressing issues of confidentiality and security is of paramount importance. Existing systems are vulnerable to a distributed denial-of-service (DDoS) attack from an attacker or high traffic in a dense area because of the time-consuming and complex operations based on bilinear pair and elliptic curve cryptographies used. In order to protect vehicle-to-vehicle communication in 5G-based vehicular networks from DDoS attacks, a strategy based on the Chebyshev polynomial is proposed in this study. The semi-group and chaotic features of the Chebyshev polynomial are utilised in our proposal. Our solutions also meet all applicable standards for security and privacy, such as node authentication, message integrity, preserving identity privacy, traceability, unlinkability, and resistance to attacks. Finally, our innovation decreases the computational burden to produce a message signature by 66.67% and the burden to verify a signature by 33.33%. However, the size of the message-signature tuple is affected by the communication expenses of our work by 5.00%. [ABSTRACT FROM AUTHOR]

Securing vehicle-to-everything (V2X) communications is essential as intelligent transportation system integration progresses to guarantee the dependability and safety of connected vehicles. Our study presents a novel approach aimed at strengthening the security of vehicles in V2X networks. The proposed system utilizes the virtual honeypots technique, referred to as PotRSU, within roadside units (RSU) to gather data from heterogeneous sources. The malicious entities that are drawn from all incoming traffic are recorded by the PotRSU. We utilized machine learning algorithms to effectively identify intrusion. The analysis and experimentation conducted on the proposed system exhibit 99.01% accuracy in identifying malicious nodes. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a prominent short-range wireless communication protocol widely extended for communications and sensor systems in consumer electronics and industrial applications, ranging from manufacturing to retail and healthcare. The BLE protocol provides four generic access profile (GAP) roles when it is used in its low-energy version, i.e., ver. 4 and beyond. GAP roles control connections and allow BLE devices to interoperate each other. They are defined by the Bluetooth special interest group (SIG) and are primarily oriented to connect peripherals wirelessly to smartphones, laptops, and desktops. Consequently, the existing GAP roles have characteristics that do not fit well with vehicular communications in cooperative intelligent transport systems (C-ITS), where low-latency communications in high-density environments with stringent security demands are required. This work addresses this gap by developing two new GAP roles, defined at the application layer to meet the specific requirements of vehicular communications, and by providing a service application programming interface (API) for developers of vehicle-to-everything (V2X) applications. We have named this new approach ITS-BLE. These GAP roles are intended to facilitate BLE-based solutions for real-world scenarios on roads, such as detecting road traffic signs or exchanging information at toll booths. We have developed a prototype able to work indistinctly as a unidirectional or bidirectional communication device, depending on the use case. To solve security risks in the exchange of personal data, BLE data packets, here called packet data units (PDU), are encrypted or signed to guarantee either privacy when sharing sensitive data or authenticity when avoiding spoofing, respectively. Measurements taken and their later evaluation demonstrated the feasibility of a V2X BLE network consisting of picocells with a radius of about 200 m. [ABSTRACT FROM AUTHOR]

Abstract As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency.

Emerging technologies, such as the fifth generation (5G) and the Internet of Things (IoT), increase the communication capabilities of components such as smart vehicles in intelligent transportation systems. Consequently, there is a demand for vehicular services to fulfil the purpose of safe driving and comfort in smart transportation and augmented reality assistants. These vehicular services are delay-sensitive tasks and computation-intensive tasks. Hence, these tasks are not ideal for vehicle processing due to stringent deadlines, finite resource constraints and the battery life of vehicles. Therefore, they are handled by offloading into roadside infrastructures (e.g., roadside units or high power nodes), called fog nodes (FNs), for further processing. However, when the delay-sensitive tasks increase in the network during peak time, the processing of such tasks in FNs poses a challenge regarding meeting deadlines and energy consumption. Therefore, we propose an energy and priority-aware scheduling (EPAS) algorithm to handle the delay-sensitive tasks in the overlap coverage areas of fog-enabled vehicular networks (FEVNs) such that the energy consumption of FNs is reduced while meeting deadlines. Task scheduling among FNs is a multiple 0/1 knapsack, a well-known nondeterministic polynomial (NP)-hard problem. Hence, the EPAS is a greedy-based sub-optimal solution to the task scheduling problem with a finite number of tasks and FNs in FEVNs. The performance of EPAS is evaluated by considering the peak arrival of tasks into the network. The simulation outcomes depict that the EPAS algorithm lowers the FN's energy consumption compared to benchmark algorithms. [ABSTRACT FROM AUTHOR]

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Vehicular networks have become a critical component of modern transportation systems by facilitating communication between vehicles and infrastructure. Nonetheless, the security of such networks remains a significant concern, given the potential risks associated with cyberattacks. For this purpose, artificial intelligence approaches have been explored to enhance the security of vehicular networks. Using artificial intelligence algorithms to analyze large datasets can enable the early identification and mitigation of potential threats. However, developing and testing effective artificial-intelligence-based solutions for vehicular networks necessitates access to diverse datasets that accurately capture the various security challenges and attack scenarios in this context. In light of this, the present survey comprehensively examines the vehicular network environment, the associated security issues, and existing datasets. Specifically, we begin with a general overview of the vehicular network environment and its security challenges. Following this, we introduce an innovative taxonomy designed to classify datasets pertinent to vehicular network security and analyze key features of these datasets. The survey concludes with a tailored guide aimed at researchers in the vehicular network domain. This guide offers strategic advice on selecting the most appropriate datasets for specific research scenarios in the field. [ABSTRACT FROM AUTHOR]

Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario. [ABSTRACT FROM AUTHOR]

We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has made it possible to include everyday objects in a connected network, allowing them to intelligently process data and respond to their environment. Thus, it is expected that those objects will gain an intelligent understanding of their environment and be able to process data more efficiently than before. Particularly, such edge computing paradigm has allowed the execution of inference methods on resource-constrained devices such as microcontrollers, significantly changing the way IoT applications have evolved in recent years. However, although this scenario has supported the development of Tiny Machine Learning (TinyML) approaches on such devices, there are still some challenges that require further investigation when optimizing data streaming on the edge. Therefore, this article proposes a new unsupervised TinyML regression technique based on the typicality and eccentricity of the samples to be processed. Moreover, the proposed technique also exploits a Recursive Least Squares (RLS) filter approach. Combining all these features, the proposed method uses similarities between samples to identify patterns when processing data streams, predicting outcomes based on these patterns. The results obtained through the extensive experimentation utilizing vehicular data streams were highly encouraging. The proposed algorithm was meticulously compared with the RLS algorithm and Convolutional Neural Networks (CNN). It exhibited significantly superior performance, with mean squared errors that were 4.68 and 12.02 times lower, respectively, compared to the aforementioned techniques. [ABSTRACT FROM AUTHOR]

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) play a critical role in the future development of Intelligent Transportation Systems (ITS). These networks facilitate communication between vehicles and roadside infrastructure, establishing a dynamic network capable of sharing and processing traffic data. By harnessing this data, a comprehensive understanding of traffic conditions can be achieved, ultimately improving road safety and efficiency. VANETs have the potential to warn drivers about potential hazards, suggest optimal routes, and coordinate traffic signals. However, the current system design poses a vulnerability where a vehicle can acquire multiple identities, allowing it to launch a Sybil attack by impersonating multiple vehicles. In this attack, Sybil (or fake) vehicles generate and report false data, leading to fabricated congestion reports and corrupting traffic management data. To address this issue, this research proposes a novel Sybil attack detection scheme that leverages Verifiable Delay Functions (VDFs) and location data. The proposed scheme utilizes VDFs iteratively computed by vehicles throughout their journeys, forming a VDF chain where the included data is immutable. A vehicle obtains a signature on its recent VDF state from nearby Roadside Units (RSUs) and other vehicles and incorporates these signatures into its VDF chain. The inclusion of signatures in the VDF chains is time-bound and can't be altered later. Essentially, the VDF chain serves as an immutable storage mechanism for each vehicle. Interactions between vehicles involve the exchange of signatures on VDF states, and these interactions, when compiled in a VDF chain, constitute a vehicle's trajectory. By analyzing these trajectories, we can effectively detect Sybil trajectories. Unlike existing methods that solely rely on vehicle-to-RSU interactions, resulting in high false positive rates, our approach introduces vehicle-to-vehicle interactions using VDF chains, thereby increasing the detection rate. Extensive experiments and simulations are conducted to evaluate the proposed scheme's performance in detection. The results demonstrate that our approach can accurately detect Sybil attacks while achieving low rates of false negatives and false positives when compared to existing models. [ABSTRACT FROM AUTHOR]

Road traffic in metropolitan cities is increasing at enormous rates resulting in congestion. Vehicles rendering emergency services like ambulances, fire engines, law enforcement vehicles, etc., act as lifelines and should be looked into with the highest priorities on the road. Such emergency vehicles (EmVs / EVs) are seen stuck many times in traffic, especially during peak hours of the day. The vehicles that block the EmVs on the road are unaware of the arrival of the same. Hence this work proposes a system that uses a central server that receives the location of the EmV and shares it with the civilian vehicles around. This is achieved through pinpointed accuracy systems like the Indian Regional Navigation Satellite System (IRNSS), Global Positioning System (GPS), and Global System for Mobile Communication (GSM), etc., The objective here is to help the EmVs reach their target location earlier thus saving lives.

Cooperative Vehicular Platooning (Co-VP) is a prime example of Cooperative Cyber-Physical Systems (Co-CPS), offering great potential for enhancing road safety by reducing human involvement in driving. However, this domain presents significant challenges, incorporating control theory, communications, vehicle dynamics, security, and traffic engineering. This survey explores recent advancements in Co-VP, covering control strategies, communication infrastructures, and cybersecurity. It also examines testing and validation methods, such as simulation tools, hardware-in-the-loop setups, and vehicular testbeds. Lastly, it outlines open challenges within the Co-VP field. This comprehensive overview serves as a guide for further developments in this complex and critical area. [ABSTRACT FROM AUTHOR]

Summary: Rapid intelligent transportation systems (ITS) innovations need a reliable MAC protocol to enable massive nonsafety message delivery and high‐priority safety broadcasts. The significant rise in spectrum need is regulated by collaboration in cognitive vehicular networks (CVNs). For QoS improvement, a reliable cooperative MAC for CVNs (CCVN‐MAC) is presented in this study. CCVN allows vehicles to collaborate and share channel status information, allowing for proactive channel switching in case of a legacy user (LU) appearance. To enable transmission mode selection, additional control signals are included. Using the suggested cooperative makeup technique, the helper nodes resend failed transmission. A Markov chain represents the protocol, and NS‐2 is used to evaluate it for several performance characteristics. Compared with conventional MAC techniques, the proposed protocol depicts improved performance, including depreciation of 70% in average latency and an increment of 42.4% in throughput. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) wireless communication system is envisioned be cable of providing highly dependable services by integrating with native reliable and trustworthy functionalities. Zero-trust vehicular networks is one of the typical scenarios for 6G dependable services. Under the technical framework of vehicle-and-roadside collaboration, more and more on-board devices and roadside infrastructures will communicate for information exchange. The reliability and security of the vehicle-and-roadside collaboration will directly affect the transportation safety. Considering a zero-trust vehicular environment, to prevent malicious vehicles from uploading false or invalid information, we propose a malicious vehicle identity disclosure approach based on the Shamir secret sharing scheme. Meanwhile, a two-layer consortium blockchain architecture and smart contracts are designed to protect the identity and privacy of benign vehicles as well as the security of their private data. After that, in order to improve the efficiency of vehicle identity disclosure, we present an inspection policy based on zero-sum game theory and a roadside unit incentive mechanism jointly using contract theory and subjective logic model. We verify the performance of the entire zero-trust solution through extensive simulation experiments. On the premise of protecting the vehicle privacy, our solution is demonstrated to significantly improve the reliability and security of 6G vehicular networks. [ABSTRACT FROM AUTHOR]

Effective means for transportation form a critical city infrastructure, particularly for resource-constrained smart cities. Rapid advancements in information and communication technologies have paved the path for intelligent transportation system (ITS), specifically designed for optimal effectiveness and safety with existing transportation infrastructure. A key function of ITS is its ability to aggregate large volumes of data across various sources for event detection. However, prediction accuracy remains a challenge since ITS event detection is characterized by very stringent latency requirements necessitating the use of lightweight detection schemes, thus seriously compromising the efficiency of ITS. This paper attempts to tackle this problem by introducing an IoT-integrated distributed context-aware fog-cloud ensemble that intelligently manages context instances at fog nodes ensuring availability of context instances for ITS. This system enhances prediction accuracy by utilizing a hybrid convolutional neural network (CNN) where each vehicle within the system retains only local information, while adjacent fog nodes gain access to global events via continual federated learning, updating regularly between fog and cloud models. Experiments presented herein illustrate the superiority of the CNN model, yielding an accuracy of more than 95%, which is an improvement of around 3% compared to the LeNet with same RGB input images. [ABSTRACT FROM AUTHOR]

The automotive industry is undoubtedly taking giant strides toward a paradigm shift. In essence, wireless network communication and artificial intelligence technologies are stimulating the gradual evolution of the autonomy of intelligent vehicles. This shift causes a divergence in vehicle architecture to become assembled with software-driven rather than mechanical-driven components, producing an integrated connected central unit that perceives and processes the surrounding environment, makes autonomous decisions, and controls the entire vehicle. The emerging vehicular network technologies, including vehicle-to-everything and in-vehicle channels, facilitate wired and wireless bidirectional communication within the vehicle and to other vehicles, infrastructure actors, and the Cloud to integrate it with its surrounding environment in real-time. Despite the promised potential benefits of intelligent vehicles, including improved mobility, driving safety, and economic and environmental gains, such increased network connectivity and complexity expose them to a vast attack surface. Researchers have identified a wide range of internal and external security threats due to connectivity and automation vulnerabilities within the vehicle-to-everything wireless network channels and the lack of core security measures, such as authentication, authorization, and encryption within the in-vehicle network. The dynamic nature of these vehicular networks and their ever-changing threat landscape originate new pressing security challenges that can cause severe safety destruction. In this paper, we propose BoostSec, a novel online security analytics solution that leverages advanced incremental ensemble learning to provide robust, rapid, and adaptive protection of vehicular networks against known and unknown attacks. We further augment the proposed solution with an agnostic interpretability analysis of the results. We conducted extensive experiments on three publicly available benchmark datasets representing vehicular environments in various contexts. The experimental evaluation proves that the proposed framework outpaces current baseline approaches and meets the challenges with remarkable performance, demonstrated by its (1) generalization covering a wide range of attacks across various vehicular network contexts; (2) real-time analysis reflected in efficient computation footprint; (3) adaptability against unseen attacks; (4) robustness against adversarial attacks; and (5) augmented interpretability analysis. [ABSTRACT FROM AUTHOR]

Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation. [ABSTRACT FROM AUTHOR]

The vehicular networks has spurred research into intelligent traffic management systems to alleviate congestion and enhance safety. However, existing approaches often face challenges in capturing the complex dynamics of urban traffic flow efficiently. In this study, we propose an innovative framework integrating Deep Radial Basis Function (DRBF) networks into vehicular networks for intelligent traffic management. Our approach aims to address the limitations of conventional methods by leveraging the representational power of deep learning while incorporating the flexibility of radial basis function networks. The problem addressed in this research lies in the inadequacy of traditional traffic management systems to adapt to the dynamic nature of urban traffic flow. Existing methods often rely on simplistic models or predefined rules, which may fail to capture the intricate patterns and interactions among vehicles on the road. Consequently, these systems may struggle to provide real-time and accurate traffic management solutions, leading to increased congestion and safety hazards. To bridge this research gap, we propose the integration of DRBF networks, which offer a unique combination of deep learning capabilities and radial basis function interpolation. This hybrid architecture enables the model to learn complex spatial and temporal dependencies from vehicular network data while maintaining computational efficiency and interpretability. By training the DRBF network on historical traffic data and real-time sensor inputs, our methodology can effectively predict traffic flow, identify congestion hotspots, and optimize route recommendations in urban environments. Experimental results on real-world traffic datasets demonstrate the effectiveness of the proposed approach in enhancing traffic management performance. Compared to traditional methods, our DRBF-based framework achieves higher accuracy in traffic flow prediction and generates more efficient routing strategies, leading to reduced travel times and improved overall traffic conditions. [ABSTRACT FROM AUTHOR]

Rapid development in Information Technology (IT) has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle (V2V) transmission. Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data. The communication occurs directly between V2V and Base Station (BS) units such as the Road Side Unit (RSU), named as a Vehicle to Infrastructure (V2I). However, the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time. Therefore, the scheme of an effectual routing protocol for reliable and stable communications is significant. Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment. Therefore, this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing (FOA-EECPCR) technique in VANETS. The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET. To accomplish this, the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy, distance, and trust level. For the routing process, the Sparrow Search Algorithm (SSA) is derived with a fitness function that encompasses two variables, namely, energy and distance. A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method. The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods. [ABSTRACT FROM AUTHOR]

Intelligent transport systems (ITS) include a broad range of applications that require proactive strategies and predictive data driven by artificial intelligence and big data. The objective of this paper is to improve the accuracy of traffic flow prediction by utilizing a novel approach that combines feedforward neural-networks with the Quasi-Newton (QN) optimization method. The proposed method decreases the error factor based on the lagrange multiplier and Jacobian vector. This enhancement has resulted in a faster convergence during the learning process. The sample was chosen utilizing the dataset provided by the England Highway (HE) traffic monitoring systems in 2023. In order to assess the proposed model, the research findings are compared to other standard prediction techniques. As a regression model, the proposed optimized multi-layer perceptron neural network method achieves an average root-mean-squared-error of 0.143 compared to 0.319, 0.459, and 0.406 achieved by (random forest, Naïve bays, k-nearest neighbour) respectively. That is, the proposed model achieved an average percentage of improvement in prediction of are approximately 55.17%, 68.81%, and 64.78%, respectively, compared to other standard techniques. Finally, the superiority of the proposed model was evaluated by the coefficient of determination (R²) and mean-absolute-error measure, and its performance was better than other forecasting techniques as well. [ABSTRACT FROM AUTHOR]

As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario. [ABSTRACT FROM AUTHOR]

The functional characteristics of existing inter-vehicle communication networks are used to offer a certificateless authentication scheme that integrates fifth-generation (5G) communication and fog computing. Thus, CLA-FC5G, a novel certificateless authentication scheme for 5G-assisted vehicular networks equipped with fog computing and device-to-device communication in this paper. As opposed to prior schemes, which relayed on 802.11p-based inter-vehicle communication, our CLA-FC5G employs D2D technology, enabling vehicles to communicate directly while ensuring their respective safety and lightening the communication overhead. Our scheme contains six polynomial-time algorithms to handle the system setup, key generation, and message signing and verification processes. The results demonstrate that our method is both secure and efficient due to its lack of communication and computational overhead. We have discovered that our proposed CLA-FC5G system can lower overhead and is extremely efficient and scalable, which is well-suited for mass vehicular networks. The output reveals that CLA-FC5G, which affects both security and effectiveness, meets practical safety requirements for a believable 5G setting.

The Internet of Things (IoT) and advanced human-centred technologies have seen rapid expansion in smart city applications, including smart enterprises, homes, and vehicular networks. The IoT architecture underpins technological support for both human- and device-centric secure solutions, which are essential to the functioning of smart cities. Within this evolving landscape, Vehicle-to-Everything (V2X) technology has emerged as a key innovation, integrating vehicular communication with internet connectivity. This integration facilitates seamless data exchange between vehicles, infrastructure, and other entities, contributing to enhanced road safety, optimized traffic flow, reduced emissions, and the development of more intelligent transportation systems. As V2X technology becomes increasingly widespread, ensuring the security and privacy of the information exchanged within these networks is critical. While much of the existing research has concentrated on general IoT security frameworks and standalone vehicular communication protocols, our study addresses the unique security challenges inherent in V2X environments. This paper identifies and examines various security threats, including data privacy breaches, malicious attacks, and traffic manipulation, and explores strategies to mitigate these risks within V2X communications. In addition, this paper investigates the potential of blockchain technology as an innovative solution for enhancing the security and trustworthiness of information exchanges in V2X networks. The paper also includes a case study that presents a proposed solution demonstrating how blockchain technology could strengthen the security and integrity of data transfers within V2X systems. Through comprehensive analysis, evaluation, and recommendations for future research, this work contributes to the current understanding of secure data exchange in V2X networks.

Millimeter-wave communications (mmWave) are gaining significant attention for their diverse applications across various domains, being key for the development of ultra-fast, low-latency wireless systems. This paper surveys the main application use cases where mmWave technologies can be adopted, focusing not only on scenarios where they are used for communication purposes, but also on other applications such as imaging and sensing. Each use case is described and characterized in order to provide a general overview of the foreseen mmWave application scenarios. The document also surveys existing standardization activities regarding mmWave frequencies across several application areas and discusses recent works regarding mmWave propagation, including both channel modeling as well as electromagnetic field exposure assessment and dosimetry, in different application domains. The document serves as a reference for a quick overview of all use cases where mmWave will have a practical impact.

Standalone autonomous vehicles primarily rely on their onboard sensors and may have blind spots or limited situational awareness in complex or dynamic traffic scenarios, leading to difficulties in making safe decisions. Collective perception enables connected autonomous vehicles (CAVs) to overcome the limitations of standalone autonomous vehicles by sharing sensory information with nearby road users. However, unfavorable conditions of the wireless communication medium it uses can lead to limited reliability and reduced quality of service. In this paper, we propose methods for increasing the reliability of collective perception through real-time packet delivery rate monitoring and a dual-channel hybrid delivery approach. We have implemented AutowareV2X, a vehicle-to-everything (V2X) communication module integrated into the autonomous driving (AD) software Autoware. AutowareV2X provides connectivity to the AD stack, enabling end-to-end (E2E) experimentation and evaluation of CAVs. The Collective Perception Service (CPS) was also implemented, allowing the transmission of Collective Perception Messages (CPMs). Our proposed methods using AutowareV2X were evaluated using actual hardware and vehicles in real-life field tests. Results have indicated that the E2E network latency of the perception information sent is around 30ms, and the AD software can use shared object data to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information. This enabled the reliable transmission of CPMs even when there was significant packet loss on one of the transmitting channels.

Machine Learning (ML) has emerged as a promising tool for addressing complex challenges in multiple domains. In the context of Vehicular Ad-Hoc Networks (VANETs), ML has gained much more attention due to its ability to solve major known problems in areas such as traffic management, road safety and communication infrastructure management. In a VANET, vehicles generate a significant amount of data, which can be explored to, for example, enhance the network management regarding the connectivity between the vehicles and the infrastructure. This work studies the performance of ML models regarding the estimation of the Quality-of-Service of different network access technologies (ITS-G5 and 5G) in urban vehicular environments. To this end, data collection campaigns were carried out throughout the city of Aveiro, Portugal, which included vehicular and network performance data for ITS-G5 and 5G cellular technologies. After an initial characterization of the data collected, several ML algorithms were trained, considering different combinations of features (represented by the collected metrics). The results have shown that, for the same configurations, similar estimation errors were obtained by the Random Forest Regression and the Extreme Gradient Boosting algorithms, with the last one presenting a shorter estimation time. The results also show that location-independent configurations, i.e., when no geographic positions are used in the ML model, are slightly worse than GPS-based ML models, creating the possibility of being applied in different urban environments, making them quite versatile.

Delivering real-time multimedia content for safety applications over vehicular networks presents significant challenges due to rapidly changing network topology, high node mobility, and fluctuating traffic demands, compounded by substantial data volumes and network resource limitations. This paper proposes the Multi-Path Transmission Protocol for Video Streaming (MPTP-VS) to address these issues by optimizing video transmission in road incident scenarios. Utilizing a Fog Computing architecture, the protocol enhances video delivery efficiency while maintaining high Quality of Service (QoS) and Quality of Experience (QoE) through effective management of network resources such as bandwidth, jitter, and latency. Leveraging Ant Colony Optimization (ACO), MPTP-VS establishes an adaptive multi-path routing strategy that dynamically discovers network topology and promptly transmits data, ensuring seamless video streaming without prior knowledge of the network topology. Experimental results within an OMNET++ simulation environment demonstrate that MPTP-VS reduces latency by up to 83%, increases throughput by up to 43%, and achieves a 75% improvement in path discovery time compared to Ad Hoc On-Demand Distance Vector (AODV) and Dynamic MANET On-demand (DYMO) protocols. Additionally, MPTP-VS achieves a 35% higher Peak Signal-to-Noise Ratio (PSNR) compared to current Content Delivery Network (CDN) systems employing Adaptive Bitrate Streaming (ABS). These findings highlight the significant enhancement in video streaming performance and reliability in vehicular environments using the proposed protocol.

In Intelligent Transportation Systems (ITS), ensuring road safety has paved the way for innovative advancements such as autonomous driving. These self-driving vehicles, with their variety of sensors, harness the potential to minimize human driving errors and enhance transportation efficiency via sophisticated AI modules. However, the reliability of these sensors remains challenging, especially as they can be vulnerable to anomalies resulting from adverse weather, technical issues, and cyber-attacks. Such inconsistencies can lead to imprecise or erroneous navigation decisions for autonomous vehicles that can result in fatal consequences, e.g., failure in recognizing obstacles. This survey delivers a comprehensive review of the latest research on solutions for detecting anomalies in sensor data. After laying the foundation on the workings of the connected and autonomous vehicles, we categorize anomaly detection methods into three groups: statistical, classical machine learning, and deep learning techniques. We provide a qualitative assessment of these methods to underline existing research limitations. We conclude by spotlighting key research questions to enhance the dependability of autonomous driving in forthcoming studies.

Network Slicing (NS) was proposed as a viable solution in Release 15 of Third Generation Partnership Project (3GPP) to allocate the limited resources among different service types for improving their Quality-of-Service (QoS). However, the advanced vehicular applications such as autonomous driving, platooning, remote driving, etc. have stringent QoS demands and the standard NS architecture is not sustainable for these services. Therefore, we propose a solution compatible with the standard 3GPP NS architecture that implements an Actor-Critic based Deep Reinforcement Learning (DRL) algorithm in the Network Slice Subnet Management Function (NSSMF). The algorithm allocates and manages the limited resources among different slices based on their real-time traffic demands. We generate real-time traffic for each service type and train the algorithm to improve the QoS of each service type in the network. The proposed method is evaluated for the training performance of the proposed algorithm as well as the Service level agreement Satisfaction Ratio (SSR) of each slice. The results exhibit that the proposed method not only improves SSR of each slice, but also performs well in case of increased node density in the network.

Abstract In intelligent transportation systems, real-time video streaming via vehicle networks has been seen as a vital difficulty. The goal of this paper is to decrease the computational complexity of the versatile video coding (VVC) encoder for VANETs. In this paper, a low-complexity enhancement VVC encoder is designed for vehicular communication. First, a fast coding unit (CU) partitioning scheme based on CU texture features is proposed in VVC, which aims to decide the final type of CU partition by calculating CU texture complexity and gray-level co-occurrence matrix (GLCM). Second, to reduce the number of candidate prediction mode types in advance, a fast chroma intra-prediction mode optimization technique based on CU texture complexity aims to combine intra-prediction mode features. Moreover, the simulation outcomes demonstrate that the overall approach may substantially reduce encoding time, while the loss of coding efficiency is reasonably low. The encoding time can be reduced by up to 53.29% when compared to the VVC reference model, although the average BD rate is only raised by 1.26%. The suggested VVC encoder is also hardware-friendly and has a minimal level of complexity for video encoders used in connected vehicle applications.

In this paper, we propose an optimization algorithm for RIS-assisted multiple-input single-output vehicle communication systems, Given a vehicle-to-vehicle user signal to interference plus noise ratio requirement, we optimize the transmit beamforming vector and phase shift matrix of RIS to obtain the maximum transmission rate of vehicle to infrastructure user. To deal with the coupled variables in the optimization problem, the alternate iterative algorithm is exploited to divide the original optimization problem into two sub-problems, each with a single variable. Moreover, the first-order Taylor expansion and the semidefinite relaxation methods are used to transform the nonconvex sub-problems into convex optimization problems. The simulation results are presented to validate the superiority of the proposed method compared to the benchmark schemes. Additionally, the simulation results also reveal that there exits an optimal vehicle speed under different path loss exponents so as to achieve the maximum transmission rate if the RIS is used by our proposed beamforming method. [ABSTRACT FROM AUTHOR]

Fog computing is a key component of future intelligent transportation systems (ITSs) that can support the high computation and large storage requirements needed for autonomous driving applications. A major challenge in such fog-enabled ITS networks is the design of algorithms that can reduce the computation times of different tasks by efficiently utilizing available computational resources. In this paper, we propose a data-enabled cooperative technique that offloads some parts of a task to the nearest fog roadside unit (RSU), depending on the current channel quality indicator (CQI). The rest of the task is offloaded to a nearby cooperative computing vehicle with available computing resources. We developed a cooperative computing vehicle selection technique using an artificial neural network (ANN)-based prediction model that predicts both the computing availability once the task is offloaded to the potential computing vehicle and the link connectivity when the task result is to be transmitted back to the source vehicle. Using detailed simulation results in MATLAB 2020a software, we show the accuracy of our proposed prediction model. Furthermore, we also show that the proposed technique reduces total task delay by 37% compared to other techniques reported in the literature. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100% for the binary classification task of identifying normal and malicious traffic. An innovative aspect of this study is the incorporation of XAI methodologies, specifically the Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, this research also considered key features identified by mutual information-based feature selection for the task at hand. The major findings from this study reveal that the XAI-based intrusion detection methods offer distinct insights into feature importance. Key features identified by mutual information, LIME, and SHAP predominantly relate to Transmission Control Protocol (TCP), Hypertext Transfer Protocol (HTTP), Domain Name System (DNS), and MessageQueuing Telemetry Transport (MQTT) protocols, highlighting their significance in distinguishing normal andmalicious network activity. This XAI approach equips cybersecurity experts with a robustmeans of identifying and understanding VANETmalicious activities, forming a foundation for more effective security countermeasures. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV), a vital component in intelligent transportation systems, supports diverse applications ranging from periodic safety message exchanges to on-demand multimedia streaming, gaming, and infotainment. Unicast routing is one of the primary techniques that support many such applications. Due to its highly dynamic nature and variable channel conditions, routing is extremely challenging in IoVs. Multiple contrasting metrics comprising cross-layer information need to be jointly examined to achieve an effective and reliable routing decision. In this paper, we propose a new unicast V2V routing protocol that consolidates and harmonizes the conflicting cross-layer and positional metrics such as distance, direction, link quality, link lifetime, available bandwidth, and queue information to select the most suitable next-hop nodes while forwarding packets. We combine these contrasting metrics using Fuzzy Logic to present a new intelligent routing protocol. Extensive simulation shows that our proposed protocol outperforms the standard existing routing techniques in terms of packet delivery ratio and average end-to-end delay across diverse vehicular environments. [ABSTRACT FROM AUTHOR]

In vehicular communication networks, optimizing connectivity and efficiency is paramount for ensuring seamless and reliable communication among vehicles. The identified problem centers on the inadequacies of traditional optimization approaches in addressing the dynamic and complex nature of vehicular networks. The absence of a comprehensive solution that combines the adaptive capabilities of the KESHTel algorithm with the cognitive insights gained from Leabra models. Existing methodologies often fall short in adapting to real-time changes and fail to capitalize on cognitive principles for efficient decision-making. This research addresses the need for enhanced vehicular network optimization by proposing the utilization of the KESHTel algorithm, coupled with insights derived from Leabra models. The method details the integration of the KESHTel algorithm, known for its adaptive learning capabilities, with insights from Leabra models, which are inspired by the neural architecture of the brain. This hybrid approach leverages machine learning and cognitive principles to optimize communication routes, minimize latency, and allocate resources intelligently within the vehicular network. Results from simulations and experiments demonstrate the effectiveness of the proposed approach in improving communication reliability, reducing congestion, and enhancing overall network performance. The findings indicate a significant advancement in vehicular network optimization, showcasing the potential of the KESHTel algorithm and cognitive insights from Leabra models in addressing the complex challenges inherent in dynamic vehicular environments. [ABSTRACT FROM AUTHOR]