Search

With the development of wireless network, smart devices, and vehicles, Vehicular Ad-hoc Networks (VANETs), a special application of the Internet of Things, have emerged as a prominent research focus. Communication within VANETs is typically group-based, necessitating a robust group key management scheme to secure communication in such an ad-hoc network. In this work, we take into consideration the fast movement feature of vehicles and propose a location-updating based group key management scheme that empowers vehicle nodes with self-healing function. We apply the location information into the key computation for the group key management scheme. Furthermore, we conduct comprehensive security and performance analyses, demonstrating the efficiency of our scheme and its scalability for large-scale VANETs. Lastly,, we discuss how to advance our proposed scheme to achieve greater capacity with some modifications. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communication has many benefits. It improves fuel efficiency, road safety, and traffic management. But it raises privacy and security concerns. These include the risk of cyberattacks and the loss of drivers' personal data. Eavesdropping, data manipulation, and unauthorized vehicle monitoring are major problems that need immediate attention. This paper proposes a new approach to intrusion detection in V2X communications. It uses machine learning-based cryptographic protocols for intrusion detection (ML-CPIDSs). The goal is to improve privacy and security in vehicular ad hoc networks (VANETs). The ML-CPIDS combines advanced cryptographic protocols with machine learning. It provides strong authentication, encryption, and real-time threat detection. Robust authentication and encryption techniques in modern cryptographic systems protect sensitive information. Using machine learning algorithms, it is feasible to identify and address security risks in real-time. The proposed technology solves key privacy and security issues. It has applications in many areas, including autonomous vehicle networks, urban traffic management, and vehicle communication systems. Extensive simulations show the ML-CPIDS works in different VANET environments. Privacy, security, and the ability to identify threats in real time are some of the areas that are evaluated in these simulations. The proposed ML-CPIDS approach outperforms current methods on several metrics. It has better privacy and authentication, lower latency, and stronger threat detection. It also improves the integrity and efficiency of V2X communications in VANET networks. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks (VANETs) stand at the forefront of advancing vehicular and infrastructural communication, ushering in a new era of connectivity on the move. As VANETs become increasingly saturated with varied applications, the demand for multicast routing protocols that ensure reliable and scalable communication to designated groups of vehicles intensifies. Multicast communication emerges as the preferred method in vast vehicular networks, optimizing computational resources and achieving desired quality of service levels. Yet, the dynamic nature of VANETs, characterized by high mobility and swift changes in network topology, presents significant hurdles in group management, path creation, and maintenance for multicast routing. This paper offers an in-depth examination of multicast routing methodologies developed expressly for VANETs, starting with an analysis of VANET-specific attributes and challenges. It proceeds to critique the latest multicast routing protocols, evaluating their advantages and drawbacks, and spotlighting progressive trends and prospective avenues for research. Through a meticulous review of the extant literature, this survey aspires to furnish critical insights into the formulation, execution, and refinement of multicast routing protocols within the VANET landscape. [ABSTRACT FROM AUTHOR]

Safety-related applications in Vehicular Ad Hoc Networks (VANETs) have stringent Quality of Service (QoS) requirements, including scalability, reliability, and low dissemination delay. Multi-cast and unicast communication have emerged as promising approaches to mitigate broadcast storms and hidden terminal problems in these applications. This paper introduces a novel mechanism called the Route Based Emergency Message Dissemination Scheme using a Multi-hop Wireless Network, designed to reduce latency and minimize flooding. The proposed scheme features a new message-forwarding route that leverages directions based on the Emergency Vehicle (EV). It employs a single-node structure for initial emergency message forwarding, followed by a dynamic multicast/unicast design tailored to the EV's state. The scheme considers five EV cases: (i) EV as Cluster Head (CH), (ii) EV as Bridge Node (BN), (iii) EV as Cluster Member (CM), (iv) EV as both CH and BN, and (v) Road Side Unit (RSU) as an emergency node. The EV determines the uni-cast or multi-cast communication for the first node, and the forwarding structure is subsequently followed. This approach ensures the timely delivery of emergency messages, addresses broadcast storm issues and maximizes coverage in single-lane highway scenarios. Omnet++ simulations demonstrate improved performance compared to existing methods, with lower end-to-end delay and enhanced coverage in high-density vehicle environments. The evaluation section highlights the scheme's ability to achieve maximum information coverage, reduced average waiting time, and rapid emergency message delivery. [ABSTRACT FROM AUTHOR]

The advancement of Internet of Vehicles (IoV) technologies has significantly enhanced road safety and transportation efficiency through smart traffic management and precise control systems. With the advent of 5G and beyond, vehicles within the IoV ecosystem can seamlessly communicate with various smart entities (X) using V2X (Vehicle-to-Entity) communications. However, the openness of IoV networks and the exponential growth of V2X links have expanded potential attack surfaces, increasing the risk of security and privacy breaches. In response to these challenges, this article proposes a privacy-preserving and secure communication framework for IoV networks, addressing critical security challenges in V2X communication. By leveraging lightweight cryptographic mechanisms such as hash functions, quadratic residuosity, and Legendre symbols, the proposed scheme ensures secure authentication, group key sharing, and pseudonym management within IoV networks. The proposed scheme’s security and privacy features, along with its correctness, have been rigorously validated against various security threats where other state-of-the-art schemes fail. Comprehensive performance analysis demonstrates that our scheme completes authentication in a fraction of a millisecond, significantly outperforming existing approaches. The design simplicity and efficiency of the proposed authentication structure make it highly suitable for real-world IoV applications.

As the capabilities of mobile devices and mobile communication techniques expand, vehicular ad hoc networks (VANETs) play an increasingly significant role in transportation and traffic management, particularly in Intelligent Transportation Systems (ITS). However, the rapid topology changes, high vehicle mobility, and frequent link disconnections make routing in VANETs a challenging task, necessitating the development of efficient routing protocols. Moreover, as VANET services provided in our daily lives become more extensive and situations in which they are provided simultaneously to an increasing number of users become more frequent, numerous data delivery optimization algorithms have been researched to address these issues. Alongside this, the research related to Unnamed Aerial Vehicles (UAVs) has also been utilized as a solution to solve the physical data link disconnection problem of VANETs. In this paper, we propose a routing protocol called PSUV (Particle Swarm UAV-VANET). First, we have solved the software-related issues of VANETs by measuring data transmission availability of vehicles existing in the topology and conducting topology optimization by creating a vehicle routing table, utilizing a mathematical value calculation formula that combines Particle Swarm Optimization (PSO) from Swarm Intelligence (SI) and the directional vector information of the vehicle. Furthermore, by utilizing UAVs, we address the physical disconnectivity issues that arise during data transmission in VANETs, thereby minimizing the delay and maximizing the total data rate under realistic conditions. Finally, extensive simulations are conducted to validate the superiority of the proposed protocol in various circumstances in an NS-3 simulation. Simulation results indicate that the proposed protocol solves the challenging issues in realistic VANET with significant improvement.

Vehicular Ad-hoc Networks (VANET) help to increase traffic safety, manageability, and efficiency. VANET facilitates effective communication in the vehicular nodes. Here, the network attack is one of the significant consequences of VANET, which may affect network security. The high mobility of the nodes may become difficult to transfer the data through the vehicle. In this article, a novel method of Elliptic Curve Cryptography (ECC) is proposed. The key is generated by Key Generation Unit (KGU), and also the key is fed into the Road Side Unit (RSU). The keys in Enhanced ECC (EECC) are optimized using the Adaptive Horse herd Optimization Algorithm (AHOA). During decryption, the key of source node K1 is to be taken from RSU, where the key is processed by the Digital Signature method to obtain EECC key K2. With the generated K2, the destination node can retrieve the data by the proposed model. Thus, the simulation analysis of the proposed AHOA-EECC method has attained less time complexity which was compared against the high value of 5%, 12.4%, 50%, and 13.7% for GWO-EECC, PSO-EECC, AOA-EECC, and HOA-EECC. Finally, the outcome of the proposed system enhances the security level and performs better data transmission. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks are a type of ad-hoc network that lacks a fixed infrastructure. The network's nodes are vehicles that self-organize and perform various network operations, including packet routing and network management. These networks enable intelligent autonomous behavior in vehicles, particularly in situations such as accidents. Each vehicle in a vehicular ad-hoc network acts as a network node, and these nodes can collaborate to enhance network efficiency. Nowadays, technologies like vehicular ad-hoc networks are widely employed to enhance traffic flow and transportation in urban areas. Routing in vehicular ad-hoc networks remains a fundamental challenge in these networks. This article presents a routing method specifically designed for vehicular ad-hoc networks operating in urban environments. Given that urban environments consist of numerous roads and intersections, the proposed approach is divided into three phases. The first phase introduces an intersection detection method that does not require a city map. It classifies the network nodes into two categories: those located at intersections and those outside intersections. The second phase presents a routing method for nodes outside intersections, while the third phase outlines a method for determining routes for nodes within intersections. To evaluate the performance of the proposed method, key parameters such as packet delivery ratio, routing overhead, throughput, and end-to-end delay have been analyzed. The results indicate that the proposed method outperforms other existing methods. [ABSTRACT FROM AUTHOR]

Since Leonardo da Vinci's creation of a self-propelled cart in the 1500s (Palmer. in Significant figures in world history p. 75--7, 2018), the evolution of Autonomous Vehicles (AVs) has aimed to revolutionize transportation. While AVs promise improved safety, traffic efficiency, and industrial optimization by reducing human intervention, ensuring their security remains paramount. This paper provides a thorough literature review spanning from historical milestones to contemporary advancements in AV technology. It delves into the significance of Vehicular Ad-hoc NETworks (VANETs) for safety applications and underscores the critical role of speed harmonization and string stability in safeguarding AV platoons. Furthermore, the paper addresses cybersecurity threats targeting platoon networks, advocating for research into encryption mechanisms, road-side units, control algorithms, hybrid communications, and on-board system security to bolster communication security within platoons. By advocating for a balance between AV technological advancements and robust security measures, this paper facilitates safe and reliable AV platooning operations. [ABSTRACT FROM AUTHOR]

Reliable communication is always one of the requirements of vehicular ad-hoc networks (VANETs). However the high speed mobile nodes and dynamic topology structure of VANETs means it is difficult to get a stable link between vehicles. Toward this end, we propose an optimized mobile similarity and link transmission quality routing (OMSLTQR) to select routing nodes and maintain the quality of communication link. Mobile similarity and link transmission quality are two basis for routing selection. To get the speed information for mobile similarity calculation, we modify the frame structure. The local information frame is redesigned to save and transmit link selection information. We also propose an algorithm based on OMSLTQR to find the routing neighbor set. Simulation results shows that our OMSLTQR achieves better system performance than optimized link state routing in terms of end-to-end delay and successful transmission rate. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks have increased the opportunity to use Cognitive Radio (CR) to cope with spectrum scarcity and increase reliability. Despite all efforts to improve the reliability of CR-VANETs, there are no mathematical and scalable methods for modelling and evaluating CR-VANETs. This paper investigates the problem of assessing the reliability of a multi-hop and multi-channel CR-VANET in safety applications. Two formal and scalable methods, Boolean notation and the Petri Net model, are provided to model and evaluate the reliability of CR-VANET. We give a detailed simulation study with NS3 to support our theory. Our analytical and simulation results indicate the effectiveness of CR in increasing the reliability of VANET. By these results, one can evaluate the network's reliability and tune the network parameters for better reliability. [ABSTRACT FROM AUTHOR]

Effective communication between smart transportation and smart vehicles is carried out using Vehicular Ad-Hoc Networks (VANETs). Here, the VANET systems that exist nowadays have issues regarding user privacy and authentication. In internal vehicles, the fake message broadcasting should be stopped to protect the vulnerability of these vehicles from privacy issues. Additionally, the traditional manner of storing transmitted data lacks a decentralized and distributed security system, making it easily vulnerable for third parties to provoke malicious activities within the VANET system. VANET is an autonomous and open-access network, so, privacy and security are the main issues. Hence, it is essential to rectify the complications that are present in the traditional security and privacy preservation models in the VANET system. Thus, an innovative privacy preservation and security scheme with fog enabled VANET system is implemented by considering the complications in the existing models. The major that take place in the recommended framework are (a) Node Authentication, (b) Privacy Preservation, and (c) Message Verification. Initially, the node authentication is performed in the recommended framework using an Adaptive Deep Bayesian network (ADBN) in order to ensure an enhanced permissibility rate in the vehicular node. Then, messages are authenticated to protect the virtue of the messages. The parameters in the ADBN are tuned using the aid of Integrated Fire Hawk with Tunicate Swarm Algorithm (IFHTSA). Next, the privacy preservation procedure in the VANET model is carried out using Hybrid Attribute-Based Advanced Encryption Standard (HABAES) encryption techniques. The keys obtained on the encryption of the messages are signed digitally. Moreover, the suggested model utilized the fog node for the analysis instead of Road-Side Units (RSUs), because of its effectiveness in minimizing the latency rate with an increased throughput rate. In the message verification node, once the Fog Edge Node (FEN) receives the signed message from the vehicles, then it checks the validity of the vehicle node by comparing it with the signed messages. Finally, the experimentation is done based on various standard performance metrics. However, the developed model achieves 94% and 93% in terms of accuracy and precision. Hence, the suggested technique offers minimal computation and communication overhead in different experimental observations over the classical technique. [ABSTRACT FROM AUTHOR]

Electronic toll collection system (ETCS) devices on vehicles communicate wirelessly with road head devices at toll stations. ETCS is preferred in countries because it is more convenient and efficient than manual toll collection. However, the scattered and constant innovations in this sector require a bridge between worldwide ETCS usage and advancements and a single page to grasp this sector's developments to ensure further developments. Following a systematic review, this study examined ETCS worldwide and technology adaption considering the scopes, limitations, usages, and related technology of the ever‐developing electronic toll collection system. Existing ETCS technologies have been investigated and synchronized with ingrowing tech disparity to overcome the research synchronization gap. The study also included a bibliometric assessment to identify the important areas of research, technologies most used, degree of knowledge generated, growth/trend of the researches, most well‐known authors, keywords, countries, documents, source of publication, and so on considering time‐to‐time changes. The study provided bibliometric solutions to the gap of lack of synchronization of research worldwide and even ETCS implementation difference within a country, which complicated collaboration with the central system and linked organizations and made it difficult for users to adapt the system. [ABSTRACT FROM AUTHOR]

Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.

With the recent technological developments, massive vehicular ad hoc networks (VANETs) have been established, enabling numerous vehicles and their respective Road Side Unit (RSU) components to communicate with one another. The best way to enhance traffic flow for vehicles and traffic management departments is to share the data they receive. There needs to be more protection for the VANET systems. An effective and safe method of outsourcing is suggested, which reduces computation costs by achieving data security using a homomorphic mapping based on the conjugate operation of matrices. This research proposes a VANET-based data outsourcing system to fix the issues. To keep data outsourcing secure, the suggested model takes cryptography models into account. Fog will keep the generated keys for the purpose of vehicle authentication. For controlling and overseeing the outsourced data while preserving privacy, the suggested approach considers the Trusted Certified Auditor (TCA). Using the secret key, TCA can identify the genuine identity of VANETs when harmful messages are detected. The proposed model develops a TCA-based unique static vehicle labeling system using cryptography (TCA-USVLC) for secure data outsourcing and privacy preservation in VANETs. The proposed model calculates the trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6% accuracy for data encryption to provide security. The proposedmodel achieved 98.5% accuracy in data outsourcing and 98.6% accuracy in privacy preservation in fog-enabled VANETs. Elliptical curve cryptography models can be applied in the future for better encryption and decryption rates with lightweight cryptography operations. [ABSTRACT FROM AUTHOR]

In high-speed and dynamic Vehicular Ad-hoc Networks (VANETs), cooperative transmission mechanism is a promising scheme to ensure the sustainable transmission of data. However, due to the possible malicious behavior of vehicles and the dynamic network topology of VANETs, not all vehicles are trustworthy to become relays and perform the cooperative transmission task reliably. Therefore, how to ensure the security and reliability of the selected vehicles is still an urgent problem to be solved. In this paper, we propose a risk-aware relay selection scheme (ARSL-V) using reinforcement learning in VANETs. Specifically, we design a risk assessment mechanism based on multiple parameters to dynamically assess the potential risk of relay vehicles by considering the reputation variability, abnormal behavior, and environmental impact of vehicles. Also, we model the relay selection problem as an improved Kuhn-Munkres algorithm based on the risk assessment to realize relay selection in multi-relay and multi-target vehicle scenarios. Besides, we use a reinforcement learning algorithm combined with feedback data to achieve dynamic adjustment of the parameter weights. Simulation results show that compared with the existing schemes, ARSL-V can improve the detection rate of malicious behavior and cooperative transmission success rate by about 25% and 6%, respectively. [ABSTRACT FROM AUTHOR]

Recently, the use of traffic monitoring applications is very common. Many of these, for example, have algorithms that seek the fastest way for the driver to complete the journey. Vehicular networks can be used to collect information in real time, such as traffic flow, accidents and congestion, helping to decongest the already chaotic traffic in cities. Thus, the use of vehicular ad-hoc networks (VANETs) in this case adds greater reliability, robustness and automation to traffic control systems. Practical experiments using VANETs are scarce; therefore, it is of great importance to validate simulations and ensure that they are explored and studied by researchers in this area. According to our surveys, works that address real measurements are fewer in number compared to software simulations. One of these factors, for example, is the development of drivers and software for the equipment and the high complexity of making such experiments available. Another point that also justifies the low number of actual measurements is the high cost of the equipment that supports the IEEE 802.11p standard, as it is still not widespread in the market. The main objective of this work is to carry out a study on vehicle ad-hoc network simulators and identify the advantages and disadvantages of each simulator covered. Particularly, a VANET network capable of operating in the IEEE 802.11p standard is simulated, evaluating performance in certain traffic characteristics. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems are rapidly expanding to meet the growing demand for safer, more efficient, and sustainable transportation solutions. These systems encompass various applications, from traffic management and control to autonomous vehicles, aiming to enhance mobility experiences while addressing urbanization challenges. This paper examines key components of Intelligent Transportation Systems, including Vehicular Ad-hoc Networks, Intelligent Traffic Lights, Virtual Traffic Lights, and Mobility Prediction, emphasizing their role in improving transportation efficiency, safety, and sustainability. It explores recent advancements in communication systems that enable real-time Intelligent Transportation Systems operations, contributing to the realization of environmentally friendly smart cities.Moreover, the paper addresses security challenges associated with Intelligent Transportation Systems deployment, particularly concerning public transit privacy, and presents case studies illustrating the benefits of Intelligent Transportation Systems integration in specific urban areas, emphasizing its role in fostering Sustainable Smart Cities. Additionally, it examines proactive initiatives by automotive manufacturers in adhering to Intelligent Transportation Systems standards, ensuring mutual benefits for drivers and urban centers.

Vehicles of the future will have built-in means of communication that will allow them to exchange data with one another. One use of this kind of communication between vehicles that ultimately results in an Intelligent Transport System (ITS) is used by Vehicular Ad hoc Networks (VANETs). Parameters, such as environmental changes, that impact the performance of actual VANET applications were mostly disregarded by most techniques. Both VANET performance and throughput are susceptible to environmental variations. The great dynamism of VANETs makes data routing in such a network a difficult challenge. There are constraints in VANET regarding processing power, bandwidth, time, and high-speed mobility in vehicles. In VANETs, fog computing can significantly improve fog-based service quality. The evolution of cloud computing, the Internet of Things (IoT), and intelligent transportation systems has elevated the significance of fog computing’s integration with VANETs. While transferring tasks to the fog layer, it is essential to meet the vehicles’ needs for low latency and quick response times. With the goal of reducing task offloading delay and improving enhanced reaction time, a VANET-based task scheduling system is proposed after selecting an optimal route in the VANET. Taking into account the limited resources of vehicle nodes, the suggested technique efficiently moves workloads to the fog computing layer. The requirement for means to guarantee the constant and secure functioning of vehicles is directly proportional to their numbers. This research proposes a Vehicle Authentication based Resilient Routing Algorithm with Dynamic Task Allocation (VARRA-DTA) Scheme for selecting the optimal and secured route in VANET with effective task allocation strategy that reduces the latency in the network. The proposed model exhibits better results when contrasted with the traditional models. The proposed model achieved 98.7% accuracy in task scheduling, 99.1% accuracy in route selection and 98.8% accuracy in vehicle authentication. The proposed model exhibits better results in secured route selection and in dynamic task allocation.

Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.

The increasing need for smarter means of transportation has brought a greater focus on Vehicular Ad-Hoc Networks (VANETs). VANETs are characterized by frequent topology changes due to the high node mobility, and most of the existing network protocols for mobility management are not well prepared for persistent horizontal handovers. This work studies the integration of Software-Defined Networking (SDN) in the Intelligent Transportation Systems (ITS) universe for the mobility management of VANETs. The idea is to explore the Cooperative Intelligent Transport Systems (C-ITS) messages in the SDN universe allowing the SDN controller to have a deeper view of the topology state. This global view gives the controller proactiveness, anticipating handovers even before they happen, reducing handover delays, and thus, improving the user experience. To evaluate the proposed approach, two types of environments were considered, laboratory and real-world at city scale with connected vehicles and infrastructure. The performance results have shown great improvements compared with a non-proactive approach: exploring the benefits of integrating the ITS and SDN universes, we obtained an almost zero packet loss rate, a reduction in the average delay and an increase in the connectivity time.

In vehicular ad hoc networks (VANETs), data aggregation is pivotal as it consolidates data from multiple vehicles for further analysis. Malicious users may launch attacks during the aggregation process to threaten the security and privacy of vehicles. Therefore, it is essential to ensure the security of vehicle data aggregation in vehicular networks. In order to deal with the security risks and challenges related to data aggregation in VANETs, this paper proposes a secure and efficient multi-dimensional perception data aggregation solution. The proposed solution integrates cloud computing with blockchain, presenting a blockchain-based data aggregation system for vehicular networks, enabling efficient and secure data collection and analysis tasks. This solution utilizes an enhanced Paillier cryptosystem to protect location privacy when aggregating sensor data. Additionally, it constructs multi-dimensional sensor data from different locations. A central control centre can fully recover and analyze the aggregated data results. The security analysis has demonstrated the security and effectiveness of the solution, while the performance evaluation has verified that the solution incurs low computational and communication overheads.

Trust detection and node authentication within fog computing for the Vehicle Ad-hoc Networks (VANETs) are used to determine whether the automobiles and other infrastructure elements including Roadside Units (RSUs) are valid and secure before permitting them to function on the system. It is essential for ensuring the privacy and authenticity of automobile networks, particularly for systems that require safety-critical communication. However, the distributed and decentralized design of fog computing increases difficulties in establishing security measures and coordination between various nodes that are responsible for trust detection and authentication. In fog-enabled VANETs, trust and privacy remain a key challenge. To overcome the existing challenges, a new system for node authentication and trust detection in fog computing for VANETs is developed. Initially, node authentication and trust detection in vehicular networks is conducted using Adaptive Deep Neural Networks (ADNN). Verification of the node’s authenticity and evaluating its trust scores will considerably minimize the chance of cyber attacks and fraudulent behavior across the system, thus enhancing the security of the entire system. Node authentication on the VANET model promotes safe interaction between vehicles. Trust detection in VANET guarantees the integrity of information transferred between vehicles. The parameters of the ADNN are optimally tuned with the help of an Enhanced Garter Snake Optimization Algorithm (EGSOA) to enhance the performance. Some of the models focus only on node authentication and do not consider privacy issues. Thus, it affects the users’ identities and personal information. So, in our model after completing the node authentication and trust detection, privacy preservation of data is performed using Optimal Key-aided Data Sanitization (OPDS). Here, the same EGSOA strategy is employed to get the sanitized key to increase security. The effectiveness of this newly developed fog computing framework for VANETs is evaluated against traditional models, with an expectation of achieving superior accuracy.

Intelligent Transportation Systems have become integral to daily life, with VANETs (vehicular ad-hoc networks) playing the pivotal role. VANETs, the subsets of MANETs, employ vehicles as nodes to establish intelligent transport systems. However, due to critical applications such as military use, these networks are susceptible to attacks. With features like high mobility, dynamic network topology, and coverage issues, security breaches are a concern. This necessitates a secure routing algorithm to mitigate attacks and ensure message delivery. In our study, we utilize the UNSW-NB15 intrusion detection dataset to develop training and testing models. Our proposed novel intrusion detection system employs a feature selection algorithm that prioritizes significant arriving traffic attributes. This algorithm enhances abnormal activity detection while minimizing associated features. To achieve this, we modify the Conditional Random Field algorithm with fuzzy-based rules, resulting in a more efficient selection of influential and contributing features for detecting attacks such as DoS, Worms, Fuzzers, and Shellcode. Through appropriate feature selection using the modified Conditional Random Field and Support Vector Machine classification system in our experiments, we demonstrate a notable increase in security by reducing the false positive rate. Additionally, our approach excels in detecting accuracy of Fuzzers (98.86%), DoS (98.80%), Worms (34.45%), and Shellcode (89.308%), ultimately enhancing network performance. These findings underscore the effectiveness of our proposed method in enhancing intrusion detection and overall network efficiency. [ABSTRACT FROM AUTHOR]

Considering the high-speed mobility of nodes, the need for low latency and high reliability of information transmission in vehicular ad-hoc networks (VANET), multi-network convergence has become an inevitable trend in the deployment of VANET architecture. Since information transmission only needs to occur between vehicles within a certain range in VANET, D2D technology was introduced into the communication mode of VANET for message transmission, which fully combines the self-organizing and hybrid networks within the coverage range of vehicles. An interrupt mode information transmission mechanism was proposed, where unless the driving state of the vehicle changes, other vehicles are notified, and at other times, only a confirmation message is sent to other vehicles at certain intervals in order to determine whether the vehicle is still in the protection circle or whether the link is unblocked. Simulation results show that the transmission mechanism can effectively increase the number of vehicles that transmit information to each other, save signaling overhead in order to avoid network congestion, and also increase the channel capacity of all nodes in the coverage circle. [ABSTRACT FROM AUTHOR]

Cooperation is an emerging paradigm for improving spatial differentiation in Vehicular Ad-Hoc Networks (VANETs). Hence, many network solutions are motivated through cooperative communications for boosting VANET efficiency regarding specific network constraints like energy efficiency, network capacity, and outage probability. The main intention of this paper is to design and develop an optimization model for selecting the minimum number of multi-hops between the source and destination for the cooperative VANET networks. The first phase has a first-time slot, where a signal is transmitted by the source to the cooperative nodes (its relays) and their equivalent destination. The main problem considered here is to optimally select the number of hops or relays that are adaptable for communication, to solve the multi-objective functions concerning the target throughput and outage probability. The adoption of new Dimension-based Cat Swarm Optimization (D-CSO) is the main contribution here to optimally select the multi-hops among source and destination. Through the performance evaluation, it is observed that the designed model has achieved networks-wide fairness performance and a good convergence rate. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) allow spontaneous communication between vehicle-to-vehicle and Road-Side Units (RSU) using wireless interfaces and road-side infrastructure to form an Intelligent Transportation System (ITS). VANET, prime objective is to provide information about accidents or unwanted situations and traffic conditions to the drivers. As VANETs are dynamic in nature, the vehicle nodes to discover the best possible route to broadcast an emergency message is a challenge. To study different highway and urban road scenarios, there is a need to simulate and evaluate the network performance analysis of routing protocols beforehand since the real-time implementation is rather challenging or expensive. This paper focuses on the VANET routing protocols evaluation, to find an appropriate protocol that provides suitable Quality of Service (QoS) to end users in both safety and non-safety conditions. To simplify the QoS evaluation problem, the Hierarchical Fuzzy Inference System (HFIS) model has been designed and simulated in MATLAB R2018b which analyzes the network performance metrics of VANET routing protocols. From the simulations, it is observed that the OLSR routing protocol for both safety and non-safety applications, outperforms other routing protocols as its global QoS reaches approximately 84% efficiency. [ABSTRACT FROM AUTHOR]

Vehicular communication is gaining popularity, and seamless handover is critical to maintaining a stable and uninterrupted network connection between vehicles and roadside units. This paper investigates the advancements in handover approaches in vehicular networks, with a specific focus on fifth generation (5G) technology. Vehicular Ad-hoc networks (VANETs) face challenges due to high mobility, dynamic network topology, and frequent information exchange. The paper discusses handover issues in 5G-VANET environments, such as too-late and too-early handovers, wrong handover decisions, and unnecessary handovers. It also explores key performance indicators (KPIs) used in handover evaluation. The advancements in handover approaches presented in this paper pave the way for enhanced connectivity and communication management in 5G-VANETs, contributing to the development of safer and more efficient intelligent transportation systems. [ABSTRACT FROM AUTHOR]

A unique system that offers traffic management, mobility management, and proactive vulnerability identification is the vehicular ad hoc network (VANET). With the use of efficient deep learning algorithms, intrusion prevention practices can improve their reliability. Many assaults, like Sybil, Blackhole, Wormhole, DoS attack, etc. expose them to risk. These intrusions compromise efficiency and dependability by taking advantage of network connectivity. The use of amazingly precise learning models to anticipate a variety of threats in VANET has not yet been thoroughly explored. To categorize numerous attacks on the VANET scenario, we develop a novel efficient integrated Long Short Term Memory (LSTM) paradigm. The system employs the Panthera Leo Hunting Optimization (PLHO) method to modify the hyper-parameters of the systems to enhance the LSTM model’s detection rate under different threat situations. SUMO-OMNET++and Veins, two well-known modeling programs were utilized to gather the various VANET variables for both normal and malicious scenarios. The improved LSTM model was evaluated using actual information that had been recorded. The outcomes from the various learning models were merged with performance measures to show the algorithm’s efficiency and individuality. As the space between nearer vehicles reduces abruptly, a collision happens. So, to provide a realistic collision prevention system, it is necessary to collect exact and detailed information on the distance between every vehicle and all of the nearby vehicles. We suggest using a Carbon Nanotube Network (CNT) combined with the other Nanodevices to achieve reliability on the scale of millimeters. Modeling findings that the proposed novel approach succeeded with strong recognition capabilities. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is a technique that uses cars moved in cities or highways as nodes in wireless networks. Each car in these networks works as a router and allows cars in the range to communicate with each other. As a result of this movement, some cars will become out of range, but these networks can connect to the internet and the cars in these networks can connect to each other. This research proposes a unique clustering strategy to improve the performance of these networks by making their clusters more stable. One of the biggest problems these networks face is traffic data, which consumes network resources. Agent based modeling (ABM) evaluates better networks. The evaluation showed that the proposed strategy surpasses earlier techniques in reachability and throughput, but ad hoc on-demand distance vector (AODV) (on-demand/reactive) outperforms it in total traffic received since our hybrid approach needs more traffic than AODV. [ABSTRACT FROM AUTHOR]

The field of vehicular ad hoc networks (VANETs) has made significant advances in recent years by merging software-defined networking (SDN) with reinforcement learning (RL). VANET networking services may be remotely coordinated, and network traffic can be monitored by combining SDN with RL. However, multi-hop communication in VANETs is difficult due to inadequate connectivity, low network utilization, unexpected vehicle movement, and frequent disconnection. This study provides a novel VANET routing protocol known as SDCast, based on ad hoc on-demand distance vector routing that utilizes an SDN-based Q-learning algorithm and considers various constraints (link availability duration, link latency, and bandwidth). The protocol maintains vehicle coordination by employing a clustering architecture that considers the vehicles' relative motions and velocities. Furthermore, SDCast finds the best path by combining a probability distribution function with a global search approach. More stable clusters, reduced cluster transition rates, and a longer cluster head lifetime improve the current routing approach's inadequacies. Since SDCast does not rely on lower-level networking, it is portable and an excellent solution for VANET communication needs. [ABSTRACT FROM AUTHOR]

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics. [ABSTRACT FROM AUTHOR]

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN's simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly. [ABSTRACT FROM AUTHOR]

Most fatal road accidents in urban areas involve vulnerable road users. New solutions for fighting against these accidents can be considered by leveraging connected, intelligent vehicles and smart cities connecting all parts of an urban environment. This work proposes a multi-sensing and communication approach to prevent potential accidents between vehicles and VRUs, by predicting and notifying both about potential collisions before they happen. This approach leverages and aggregates information from smart city sensors, dispersed in the vehicles (and aggregated by the On-Board Units, OBUs), in the VRUs (e.g., smartphones and smartwatches), and on the road itself (e.g. video cameras, radars, lidars). These elements communicate through several message standards and wireless access technologies (e.g. ITS-G5, C-V2X, LTE, 5G and, in the future, 6G). Using both sensing and communication, fusion and collision detection algorithms, this system predicts and notifies potential hazardous situations involving Vulnerable Road User (VRU)s and vehicles. The results in a real scenario with sensors, VRUs and vehicles on the road show that the system predicts potential collisions with high accuracy and low delay. Results also point to some vital deployment decisions that must be made to ensure proper notification timings, such as the usage of multi-homing, 5G and edge computing.

The importance of road safety is felt nowadays more than ever, where various technologies, including self-driving cars, have become abundant. Nowadays, it has more demand to build autonomous and electrical vehicles with information retrieval systems within the received sensory data not only from the local sensors but also the online and live streaming data over networks. To increase road safety dissemination of critical information, including the possibility of an obstacle or danger being in the middle of the road, automotive navigation and control systems are required. A novel method is proposed to make this critical communication possible over a specially designed vehicular ad-hoc network, where natural or urban barriers can prevent signal propagation. The network is implemented using the LoRaWAN interface and SX127x LoRa Radio module. The SX1272MB2xAS is fitted with the SX1272 transceiver, which added to a high-performance FSK/OOK RF transceiver modem. Additionally, LoRa long-range modem provides highly power-efficient communication. For this aim, two new mechanisms have been proposed. The first mechanism enables the nodes to receive data from a suggested communication link. While the second mechanism is designed to extract vital information such as establishing the connection, closing the connection, successful data transmission, errors, etc. The findings demonstrate that the proposed mechanisms have successfully enabled LoRaWAN to operate in IoV environment. The evaluation reveals that metrics such as battery consumption and covering range outperform similar technologies. Finally, this paper proposes a message-passing strategy based on Belief Propagation (BP) which provides more accurate marginal probabilities to overcome the low data rate as a foundation for our future work.

This paper presents a high-reliability Grouping-Based communications trust model in Vehicular Ad-hoc Networks (VANET). The proposed solution consists of two distinct components: First, the Dynamic Group Head Selection (DGHS) scheme to improve the Group Head’s (GH) stability by considering the reliability of the communication link with the Road Side Unit (RSU). Second, a hybrid Dynamic Trust Model (DTM) scheme improves trustworthiness by considering the node’s dynamic conditions and records. Average GH Lifetime and Average Query Success Rate were used as metrics to evaluate the performance of the grouping algorithms to those of Cluster-Based Location Service (CBLS) and Trust-based Security for Message Exchange (TSME). The proposed DTM was compared to TSME and MiTM Attack Resistant Trust Model (MARINE). With a maximum density of 900 vehicles, DTM improves the average GH lifetime by 43% compared to CBLS and 19% compared to TSME. Similarly, DTM increases the query success rate by 10% compared to the CBLS and 23% compared to the TSME, even at the slowest speed (40 km/h). In terms of node density precision, DTM decreases by around 5% for MARINE and 10% for the TSME approach. In conclusion, it has been shown that the proposed schemes are reliable for various position-based VANET applications that need precise positioning and trusted messaging.

In a vehicular ad‐hoc network, vehicles periodically broadcast safety messages to neighboring vehicles containing information about their identity, location, speed, and other relevant information. The time‐critical nature of these messages means they are typically sent without encryption, which can compromise the privacy of the vehicle/driver. The use of pseudonyms, rather than the actual identity, has been suggested as a way to implement location privacy for drivers. However, such pseudonyms need to change frequently in order to prevent vehicle tracking. A pseudonym changing strategy (PCS) determines how frequently and under which conditions a vehicle should change its pseudonym. Many different PCS have been proposed in the literature over the last decade. However, there is a lack of a systematic analysis of the performance of these techniques, using a set of well‐defined privacy metrics. In this article, we provide a thorough comparison of four main types of PCS, in terms of standard privacy metrics, and identify the advantages and limitations of each approach under different traffic conditions and attacker capabilities. The ultimate aim of this study is to help in the determination of the best PCS to use in different situations, to maximize the location privacy. [ABSTRACT FROM AUTHOR]

Designing a reliable routing protocol for Vehicular Ad hoc Network (VANET) poses considerable challenges due to certain unique challenges inherently present in Vehicular Ad hoc Network (VANET) topology. Some of them are needed for vehicles acting as nodes having to abide by traffic rules, uncertain inter-vehicular speed variations that may affect link stability etc. Designing a routing protocol capable of dealing with multiple limiting conditions such as long congestion periods, link failures and handoffs is a challenging task, where most of the existing multipath routing protocol shows poor performance. In this paper, the proposed Multipath Route Restoration Protocol (MRRP)is aimed at providing a robust communication channel in case of link failure between nodes. This is realized by focusing on better route maintenance for the protocol. In a wireless network, a routing protocol determines the particular ways in which routers connect. In a wireless network, as the number of hops in a wireless communication path increases, various signal factors such as interference and path loss degrade the network performance. however, sending data over a longer distance will reduce throughput. Furthermore, link stability is substantially impacted by the unpredictable movement of vehicles. Multipath routing is regarded as a potential solution to improve packet delivery and end-to-end delay in VANETs.