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Vehicular ad hoc networks (VANETs), which are the backbone of intelligent transportation systems (ITSs), facilitate critical data exchanges between vehicles. This necessitates secure transmission, which requires guarantees of message availability, integrity, source authenticity, and user privacy. Moreover, the traceability of network participants is essential as it deters malicious actors and allows lawful authorities to identify message senders for accountability. This introduces a challenge: balancing privacy with traceability. Conditional privacy-preserving authentication (CPPA) schemes are designed to mitigate this conflict. CPPA schemes utilize cryptographic protocols, including certificate-based schemes, group signatures, identity-based schemes, and certificateless schemes. Due to the critical time constraints in VANETs, efficient batch verification techniques are crucial. Combining certificateless schemes with batch verification leads to certificateless aggregate signature (CLAS) schemes. In this paper, cryptanalysis of Xiong's CLAS scheme revealed its vulnerabilities to partial key replacement and identity replacement attacks, alongside mathematical errors in the batch verification process. Our proposed CLAS scheme remedies these issues by incorporating an identity authentication module that leverages chameleon hashing within elliptic curve cryptography (CHAM-CLAS). The signature and verification modules are also redesigned to address the identified vulnerabilities in Xiong's scheme. Additionally, we implemented the small exponents test within the batch verification module to achieve Type III security. While this enhances security, it introduces a slight performance trade-off. Our scheme has been subjected to formal security and performance analyses to ensure robustness. [ABSTRACT FROM AUTHOR]

The aim of the intelligent transport system (ITS) is the improvement of road safety. This system is based on intelligent vehicles composing a network called Vehicular Ad hoc NETwork (VANET). This network suffers from a disconnection problem due to its dynamic topology. Therefore, a framework of collaboration between the Wireless Sensor Network (WSN) and VANET called Hybr1id Sensor and Vehicular Networks (HSVN) has been proposed. In HSVN, the WSN can play the role of a relay between disconnected vehicles. This paper aims to propose a strategy allowing the exchange of messages between the two networks. That reduces the number of accidents and improves the management of road traffic. We can summarize our proposition in three essential points. First, an algorithm is proposed to decompose the vehicular network into clusters. This algorithm takes into account the mobile aspect of vehicles and the road model. Second, a data encoding model and a message model have been proposed to improve the quality of messages. That reduces the response time of drivers to a critical situation. Finally, an exchange algorithm is proposed to ensure the transmission of road messages between vehicles and sensors. Its principle of work is based on several scenarios defined relative to the network condition. Obtained results show an improvement in the delivery delays of road messages and the number of exchanged road messages between the vehicles. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel approach to enhance the security and efficiency of Vehicular Ad-Hoc Networks (VANETs) by integrating user authentication and cluster-based routing. The proposed method is divided into two segments. The first segment focuses on user authentication using a curve fitting technique, implemented via MATLAB simulation. Nodes are randomly deployed with geostationary coordinates and node keys. These nodes are clustered based on their geographical locations, and their legitimacy is verified using curve fitting. This ensures that only authenticated nodes participate in the network, thereby enhancing security and reliability. The second segment employs a modified Ad hoc On-Demand Distance Vector (AODV) protocol for routing, adapted to the clustered network structure. Route Requests (RREQs) are sent to Zone Heads (ZH) for validation and then forwarded to Cluster Heads (CH), where idle and execution costs are calculated based on buffer states and execution capacities. The proposed method also incorporates fog computing to enable localized data processing, reducing latency and improving scalability. The performance of the proposed method was evaluated through extensive simulations, measuring key metrics such as throughput, Packet Delivery Ratio (PDR), and latency. Results show that the proposed method achieves a throughput of 8367.141811 packets per second, a PDR of 0.83336875, and a latency of 6.606503751 seconds, outperforming state-of-the-art algorithms by significant margins. Specifically, the proposed method demonstrates a 7.5% improvement in throughput over Khudhair et al. and a 12.9% improvement over Ahmad et al. In terms of PDR, it shows an 8.8% increase over Khudhair et al. and a 7.1% increase over Ahmad et al. The latency reduction compared to these algorithms is 9.1% and 10.5%, respectively. These enhancements are attributed to the efficient clustering and authentication mechanisms, along with the integration of fog computing. The proposed method thus provides a comprehensive solution for secure and efficient VANET communication, paving the way for advanced intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Routing in vehicular ad hoc networks (VANETs) enables vehicles to communicate for safety and non-safety applications. However, there are limitations in wireless communication that can degrade VANET performance, so it is crucial to optimize the operation of routing protocols to address this. Various routing protocols employed the expected transmission count (ETX) in their operation as one way to achieve the required efficiency and robustness. ETX is used to estimate link quality for improved route selection. While some studies have evaluated the utilization of ETX in specific protocols, they lack a comprehensive analysis across protocols under varied network conditions. This research provides a comprehensive comparative evaluation of ETX-based routing protocols for VANETs using the nomadic community mobility model. It covers a foundational routing protocol, ad hoc on-demand distance vector (AODV), as well as newer variants that utilize ETX, lightweight ETX (LETX), and power-based light reverse ETX (PLR-ETX), which are referred to herein as AODV-ETX, AODV-LETX, and AODV-PLR, respectively. The protocols are thoroughly analyzed via ns-3 simulations under different traffic and mobility scenarios. Our evaluation model considers five performance parameters including throughput, routing overhead, end-to-end delay, packet loss, and underutilization ratio. The analysis provides insight into designing robust and adaptive ETX routing for VANET to better serve emerging intelligent transportation system applications through a better understanding of protocol performance under different network conditions. The key findings show that ETX-optimized routing can provide significant performance enhancements in terms of end-to-end delay, throughput, routing overhead, packet loss and underutilization ratio. The extensive simulations demonstrated that AODV-PLR outperforms its counterparts AODV-ETX and AODV-LETX and the foundational AODV routing protocol across the performance metrics. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures. [ABSTRACT FROM AUTHOR]

Advances in autonomous transportation technologies have profoundly influenced the evolution of daily commuting and travel. These innovations rely heavily on seamless connectivity, facilitated by applications within intelligent transportation systems that make effective use of vehicular Ad- hoc Network (VANET) technology. However, the susceptibility of VANETs to malicious activities necessitates the implementation of robust security measures, notably intrusion detection systems (IDS). The article proposed a model for an IDS capable of collaboratively collecting network data from both vehicular nodes and Roadside Units (RSUs). The proposed IDS makes use of the VANET distributed denial of service dataset. Additionally, the proposed IDS uses a K-means clustering method to find clear groups in the simulated VANET architecture. To mitigate the risk of model overfitting, we meticulously curated test data, ensuring its divergence from the training set. Consequently, a hybrid deep learning approach is proposed by integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. which results in the highest training, testing, and validation accuracy of 99.56, 99.49, and 99.65% respectively. The results of the proposed methodology is compared with the existing state-of-the-art in the same domain, the accuracy of the proposed method is raised by maximum of 4.65% and minimum by 0.20%. [ABSTRACT FROM AUTHOR]

The emergence of the intelligent city, in the late 2000s, was a worldwide success that has invited itself to the national political agendas of several countries in world association of the major metropolises as well as in the multinational strategy. The success of this global phenomenon is indisputable; however, the smart city remains enigmatic, without precise definition and vague terms. The smart cities projects are characterized by the ability to interconnect urban systems between themselves, including transport system, electricity system, waste system, water system, gas system, etc. and to make each system develop itself; For instance, the field of urban and interurban transport has experienced a spectacular boom over the last few decades mainly with the appearance of vehicular ad hoc networks (VANETs) having promoted the concept of smarts cities mentioned by the birth of intelligent transport systems (ITS). The notion of routing is important and necessary for ITS in order to establish an efficient transmission process in VANETs. A large number of routing protocols exist in VANETs that are classified by different methods and according to determined criteria. This paper proposes a new geographic routing solution based on an intersection entitled A Geographic Routing Based on Local Traffic Density and Multi-hop Intersections in VANETs (GRBLTD-MI) that is more adapted in the context of smart cities. This new approach will discuss the selection of the next intersection when taking into consideration the connectivity of the multi-hop intersection with the use of vehicle's local traffic density, speed, distance and direction. Simulation comparison of GRBLTD-MI against some strong routing protocols has shown the superiority of our approach in terms of packet delivery ratio, end-to-end delay and packet overhead. [ABSTRACT FROM AUTHOR]

VANET technology is an essential component of Intelligent Transportation Systems, which makes c communication between moving cars and stationary Road Side Units more accessible. It allows vehicle nodes to share crucial data among communication devices. VANET has significant potential to enhance traffic efficiency and road safety. This is accomplished by decreasing the chances of collisions between vehicles and reducing the number of accidents. Man-in-the-middle (MITM) attacks are a crucial issue in VANET which needs significant consideration from researchers. To solve the problem of man-in-the-middle attacks, this article presents a dynamic and optimized routing approach for VANET conversation in smart cities by utilizing a chaotic secure multi-verse optimization algorithm. The strategy that has been proposed seeks to achieve the goal of ensuring safe and effective interaction between vehicles participating in VANETs by dynamically determining the optimal path for the exchange of data. A chaotic protect multi-verse optimization approach is used to generate several random sequences from which the most secure route may be selected. This is done to enhance the security of the VANET transmission network during transmission. The results of the trials indicate that the suggested technique is more successful in avoiding MITM and improving the functioning of VANET connections in settings that are characterized by intelligent cities. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs), which include vehicular ad hoc networks (VANETs), are a promising method for intelligent transportation systems. The major challenges of VANET protocols are symmetric links and changing topology of the networks and hence a proper routing method is necessary for VANET. A crucial issue with VANETs is the transmission of data from a starting point to the base station. To discover the least path with the lowest cost and fewest hops between the source and the destination, existing work uses a novel priority-based directionaware collision avoidance (P-DVCA). To direct the ants to develop a shorter path with fewer hops for data transmission, the suggested method uses the prioritising and scheduling enhanced congestion control-based source ant colony optimization (ECC-ACO) for VANETs. Based on the message type and network situation, this technique provides precedence for privacy messages communications. Privacy messages can also be divided into beacon messages and occurrence messages. At that point, each message is routed into the appropriate queue predicated on its primary consideration, and each message in each queue is planned. When compared to renowned VANET communication architecture, simulation and analytical findings show that proposed ECC-ACO offers a respectable reduction in collisions. [ABSTRACT FROM AUTHOR]

This article discusses the advancements in intelligent vehicular networks and communications, emphasizing the increasing demand for advanced technologies in the transportation sector. It highlights the integration of artificial intelligence, the Internet of Things, and 5G communication technologies in creating smarter and safer mobility solutions. The article also provides summaries of various research papers covering topics such as non-intrusive testing systems, packet reordering, co-simulation platforms, data rate selection strategies, machine learning-based attack detection systems, wireless communication performance, and geographic routing in intelligent vehicular networks. The document is a summary of a special issue aimed at advancing research in this field. [Extracted from the article]

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET's network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach's performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures. [ABSTRACT FROM AUTHOR]

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions. [ABSTRACT FROM AUTHOR]

Software-defined vehicular networks (SDVN) is a promising technology for wireless data transmissions between vehicles. SDVN inherits software-defined networking principles and aims to improve the typical performance of safety and non-safety applications of vehicular adhoc networks. Consequently, enhancing the performance of Intelligent Transportation System (ITS). However, the performance of these ITS applications largely depends on the computational capability of the controller node, which involves creating or destroying a data path from the source vehicle to the destination vehicle and generating flow rules for the requests coming from the data plane elements. As a result, SDVN often suffers from the problems of overburdening the controller node with route requests under heavy traffic generation at vehicles and single-point controller failure. To counter these problems, solutions based on multiple controllers are proposed. In fact, the load-balancing problem remains an important issue. So, routing of data with multiple controllers and load-balancing, both topics in SDVN, go hand in hand. In this paper, we survey this state-of-the-art that discusses the above-mentioned challenges, starting with the SDVN preliminaries. We scrutinize the existing routing methodologies and also discuss load-balancing techniques. Furthermore, we provide real-time applications and services of SDVN, discuss trending research, potential future research directions, and the real-life applicability of SDVN that have not been addressed previously. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Network (VANET) has recently gained significant attention as a means of enhancing the mobility, efficiency, and safety of applications in the intelligent transportation system. However, because of its high-speed mobility, wireless connectivity, and extensive node coverage, security is a more difficult procedure. The Sybil security threat on VANET is a growing problem today. The Road Side Unit (RSU) failed to synchronise its clock with the legal vehicle, then unplanned vehicles are predicted, thereby incorrect messages are transferred to them. In this paper, Competitive Dolphin Echolocation Optimisation (CDEO)-based Deep Residual Network is proposed for Sybil attack and RSU misbehaviour detection. Here, the effective routing process is performed using Fractional Glow-Worm Swarm Optimisation (FGWSO)-based traffic-aware routing protocol. In the base station, the Sybil attack detection is done. The Sybil attack detection process is done using a Deep residual network, which is trained by the proposed CDEO algorithm. The CDEO algorithm is devised by incorporating Dolphin Echolocation Optimisation (DEO) technique and Competitive Swarm Optimiser (CSO). Additionally, using the Deep residual network, the RSU misbehaviour detection is done. The performance of the developed method is compared with certain performance metrics, like precision, F1-measure, and recall of 0.9197, 0.9121, and 0.9046. [ABSTRACT FROM AUTHOR]

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]

The Vehicular Ad Hoc Network (VANET) is a novel and innovative technology which is part of the Intelligent Transportation Systems (ITS). VANET is a network composed of a collection of vehicles and other roadside components that are interconnected wirelessly. The intention underlying the development of this technology is the improvement of the vehicle environment and the enhancement of vehicle and driver safety. Nevertheless, since VANETs operate wirelessly and under complicated conditions, they are susceptible to a variety of attacks by malicious actors. Traditional techniques such as encryption are no longer effective, so new techniques using intrusion detection systems IDS have attracted the attention of a large number of researchers. The IDS scans the entire network and identifies all the possible harmful nodes present in the network. The present paper covers the problem of the identification of attacks in VANET by using XGBoost. The effectiveness analysis of the proposed models has been realized on the NSL-KDD and 5RoutingMetrics datasets combined with various feature selection techniques Boruta and Adaptive Synthetic Sampling Approach (ADASYN). Furthermore, the acquired results are being compared to two of the last most used ensemble methods CatBoost and convolutional neural networks CNN.In comparison with the other IDSs, our model approach achieves high performance in accuracy, recall and precision. [ABSTRACT FROM AUTHOR]

The increasing movement from rural areas to urban areas, along with the widening gap in population, has resulted in metropolitan areas becoming extremely overpopulated. As a result of the high volume of traffic that occurs in these areas, traffic monitoring is an extremely important activity. According to the findings of this study, an improved authentication and communication protocol that is based on clusters could be implemented for Intelligent Transportation Systems in Vehicular Ad Hoc Networks (VANETs). Our number one objective is to enhance the sharing of resources amongst vehicles through improved communication. Cluster-based routing protocols allowed us to increase the scalability, stability, and dependability of fast-moving VANETs. This was accomplished in the context of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. To easing concerns regarding privacy and safety, we arranged for the vehicles to be certified by an independent contractor. Through the utilization of Graph Neural Networks (GNNs), we can reduce the number of instances in which links fail, as well as minimize end-to-end (E2E) delays and route requests. Our approach has resulted in several important benefits, including enhancements to throughput, reductions in the amount of time required for TCP socket initialization, acceleration of TCP handshake response, and DNS lookup. Shortrange peer-to-peer wireless communication is the focus of the protocols that are used within a cluster that is 400 meters in radius. Utilizing new peer-to-peer wireless communications over VANET is what is meant by the term resource-conserving in this context. Within the framework of the suggested protocol secure authentication method, a certifying authority is responsible for the generation of a secure authentication key for the vehicle, which is subsequently provided to the vehicle. [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]

Vehicular networks are the key paradigm of the Internet of Vehicles (IoV) as the extension of the Internet of Things (IoT) notion in Intelligent Transportation Systems (ITS) which can assist in the development of autonomous driving in smart cities. This technology can provide a wide variety of onboard data services, such as road safety, and increase traffic efficiency by connecting vehicles with road infrastructure and pedestrians. However, it is a challenging task to provide a satisfactory quality of service (QoS) to this network due to a number of limiting factors such as resource collision, resource interference, and congested channels because of the network topology and rapid changes produced by the high mobility as well as hardware imperfections and the anticipated growth of vehicular network devices. As a result, it will be essential to ensure that the resources of the available cellular network are allocated and used in the most efficient possible way. To achieve these goals, 3GPP has standardized the cellular vehicle to everything (C-V2X) with two versions, the long-term evolution-V2X (LTE-V2X) in Release 14 and the new radio-V2X (NR-V2X) in Release 16, as prominent technologies to improve resource allocation for vehicular networks. In order to capture the continuous effort for improving resource allocation, we present a systematic literature review (SLR) on distributed resource allocation (DRA) schemes for the two cellular-based vehicular network technologies. First, we discuss the technical configuration of resource allocation in the light of LTE-V2X and NR-V2X technologies and classify the state-of-the-art for each technology. Afterward, we explain the impact of machine learning (ML) and congestion control (CC) on the DRA. Then, we point out the primary performance metrics and simulation tools that were used in the related work. Ultimately, we highlight the challenges, open issues, and opportunities for DRA in C-V2X and outline several promising future research directions. [ABSTRACT FROM AUTHOR]

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. [ABSTRACT FROM AUTHOR]

Convoys composed of autonomous vehicles could improve the transportation and freight industries in several ways. One of the avenues of improvement is in fuel efficiency, where the vehicles maintain a close following distance to each other in order to reduce air resistance by way of the draft effect. While close following distances improve fuel efficiency, they also reduce both the margin of safety and the system's tolerance to disturbances in relative position. The system's tolerance to disturbances is known as string stability, where the error magnitude either grows or decays as it propagates rearward through the convoy. One of the major factors in a system's string stability is its delay in sending state updates to other vehicles, the most pertinent being a hard braking maneuver. Both external sensors and vehicle-to-vehicle communication standards have relatively long delays between peer vehicle state changes and the information being actionable by the ego vehicle. The system presented here, called the Convoy Vehicular Ad Hoc Network (Convoy VANET), was designed to reliably propagate emergency event messages with low delay while maintaining reasonable channel efficiency. It accomplishes this using a combination of several techniques, notably relative position-based retransmission delays. Our results using Network Simulator 3 (ns3) show the system propagating messages down a 20-vehicle convoy in less than 100 ms even with more than a 35% message loss between vehicles that are not immediately adjacent. These simulation results show the potential for this kind of system in situations where emergency information must be disseminated quickly in low-reliability wireless environments. [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]

Vehicular Ad Hoc Network (VANET) is a key component in the Intelligent Transportation System (ITS) with a number of applications for safety, traffic management, emergency services, entertainment, and so on. These applications are implemented by exchanging data among the nodes in an open, dynamic, and distributed fashion, and hence VANETs are vulnerable to different network attacks. For examples, DoS and black-hole attacks can be easily executed in VANETs, and strongly affect the routing protocol and its functionality. Through the nature of VANETs, trust management is considered one of the practical means to secure these networks. In this paper, we propose an up-to-date dynamic entity-centric trust model for VANETs, called T-AODV. We use social science techniques to apply weight and fuzzy logic theory and identify the malicious nodes. Our NS2 simulation results indicate that T-AODV can be more resists in contact with black-hole attacks by boosting the security of AODV routing protocol. T-AODV slightly increases the routing overhead of AODV and provides better performance than the existing I-AODV scheme. [ABSTRACT FROM AUTHOR]

In the contemporary era, Vehicular Ad Hoc Networks (VANETs) have emerged as a vital technology in intelligent transportation systems, substantially enhancing the overall travel experience by providing advanced services to vehicles while ensuring driver safety. Despite the notable improvements, the inherent complexity of VANETs presents persistent security challenges, encompassing issues such as privacy preservation for vehicles, message authentication, and constraints in computational power and network bandwidth. Various authentication protocols have been designed for VANETs. However, many of these protocols exhibit significant vulnerabilities, rendering them insecure and unreliable in the face of diverse security threats, such as denial of service, replay, forgery, and impersonation attacks. Moreover, some existing schemes encounter limitations, including high computational complexity and the introduction of additional communication overhead and computational costs. To tackle these concerns, we designed a lightweight and secure identity authentication protocol based on elliptic curve cryptography with the objective of furnishing an effective and secure data transmission mechanism across a public communication channel for the Internet of Vehicles. In addition, we introduce Physically Unclonable Functions (PUFs) to ensure physical layer security during the communication process. A detailed security analysis demonstrates that the proposed protocol is resilient against various attacks. Through a comparative analysis with existing relevant protocols, in scenarios with a high density of vehicles, the algorithm demonstrates significantly lower computational costs and communication overhead than the related protocols, indicating that the proposed protocol is lightweight and efficient. Consequently, the empirical findings indicate that our protocol surpasses others in terms of reliability, user convenience, and practicality for ensuring secure data transmission within VANETs. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for enhancing privacy in Vehicular Ad Hoc Networks (VANETs) by integrating homomorphic encryption with machine learning applications. VANETs, essential for Intelligent Transport Systems (ITS), face significant challenges in privacy and security due to their highly dynamic and heterogeneous nature. Our framework addresses these challenges by employing a simplified but effective machine learning algorithm, the K-nearest neighbors (KNN), to ensure the security and privacy of the network. The flexibility of the framework allows for the incorporation of other machine learning algorithms, enhancing its adaptability and efficiency in various VANET scenarios. Key to this framework is the use of homomorphic encryption (HE), a cryptographic technique that enables computations on encrypted data without the need for decryption. This feature preserves data confidentiality and allows for secure third-party computations. Our paper discusses the evolution and types of homomorphic encryption, emphasizing the importance of Fully Homomorphic Encryption (FHE) for its ability to evaluate complex polynomial functions. The paper also highlights the different domains of cybersecurity concerns in VANETs, including in-vehicle systems, ad-hoc and infrastructure networks, and data analysis. The proposed framework aims to mitigate these vulnerabilities, particularly focusing on preventing common attacks like DoS and location tracking. A significant advantage of our approach is its general nature, making it applicable to various privacy issues in VANETs. We propose the potential integration of homomorphic encryption with other privacy-preserving techniques, such as differential privacy or secure multi-party computation, to enhance computation times while ensuring robust privacy protection. [ABSTRACT FROM AUTHOR]

In intelligent transport system (ITS) networks, it is very useful to reduce the transmission energy consumption. Device-to-device (D2D) communication is a key technology that can improve the performance of the ITS networks. In this paper, we investigate a D2D-enabled vehicular network and study a power allocation strategy to maximize the spectral efficiency of the network. Also, for safety-critical vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) applications, it is presented a reliable connection during the V2V and V2I links. The performance analysis of the investigated network shows that the studied method can maximize the total spectral efficiency of the network subject to satisfy some constraints when the channel state information (CSI) is perfectly known or not. [ABSTRACT FROM AUTHOR]

Smart transportation may come from 5G-enabled cars. Traffic reports include congestion, roads, and driving. Urbanisation and population growth increase traffic accidents and travel time. Traffic accidents kill and injure most people worldwide. Intelligent transportation systems (ITS) improves driver and pedestrian safety. This study connects the VANET to 5G to create a 5Genabled vehicle network because the road-side unit (RSU) is expensive and unsecure. This study connects numerous automobiles to TA for 5G-BS D2D communication. Data transmissions between autos are risky. Several scholars suggest authentication techniques for safe vehicle-to-vehicle communications. Overhead may enable side-channel attacks with these tactics. A secure and effective efficient and secure authentication-privacy-preserving (ES-APP) system connected TA, 5G-BS, and on-border unit (OBU) was presented. Initialization, vehicle registration, parameter renewal, message signing, single and batch verification are ES-APP steps. The formal evaluation automated verification of internet security protocols and applications (AVISPA) tool with on-the-fly model-checker (OFMC) and attack searcher (ATSE) back-ends secures the suggested ES-APP technique. ES-APP appears impervious to active and passive AVISPA assaults. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is a promising project related to intelligent transportation systems (ITS), which aims at connecting vehicles and providing a set of functionalities for the efficient management of the network. However, the high mobility of the network nodes is considered a significant challenge for implementing a reliable, secure, and efficient exchange system. Furthermore, VANET faces the issue of packet delivery due to the high mobility of the nodes and packet collisions complicate the process of sending and receiving packets. We propose to combine two technologies which are unmanned aerial vehicle (UAV) and information centric networks (ICN) and apply it in VANET architecture as supporting technology. The UAV are more reliable and less affected by channel fading. And can be used in areas where we cannot install network infrastructure. The UAV has many advantages that we have cited in this article and can solve many issues of VANET. Using ICN can solve some of the problems of VANET since ICN has many strategies to capture and retrieve data. This study proposes a new VANET model based on an UAV and ICN, to reduce the overload of the vehicles, which in most cases require more resources and have a limited time to process and act especially in case of an accident or emergency. [ABSTRACT FROM AUTHOR]

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can't be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively. [ABSTRACT FROM AUTHOR]

Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look. Whereas vehicular ad hoc networks (VANETs), which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services, as they are an essential component of modern smart transportation systems. VANETs steganography has been suggested by many authors for secure, reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection. This paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality loss. According to simulations in literature and real-world studies, Image steganography proved to be an effective method for secure communication on VANETs, even in difficult network conditions. In this research, we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding, statistics, spatial domain (SD), transform domain (TD), distortion, masking, and filtering. This study possibly shall help researchers to improve vehicle networks' ability to communicate securely and lay the door for innovative steganography methods. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) communication has played a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research focused on investigating the security protocols that have been employed in vehicle-to-vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers was conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompassed key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delved into the challenges and limitations associated with implementing these protocols. The research aimed to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper proposes improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research contributes to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology. [ABSTRACT FROM AUTHOR]

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies. [ABSTRACT FROM AUTHOR]

Advancements in the automotive industry have led to the development of connected and autonomous vehicles (CAVs), which have the potential to improve traffic safety in urban areas. Environmental sensors such as cameras, radars, and lidars provide critical information about objects and other traffic participants around vehicles. Vehicles can now share this information with each other and with smart infrastructure, allowing for enhanced precision and awareness. This special issue explores various aspects of vehicular sensing in urban mobility, including architecture, emerging sensors, communication technologies, advanced applications, and deployment issues. The ten studies showcased in this special issue contribute to safer and more efficient transportation systems in urban environments. [Extracted from the article]

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time. [ABSTRACT FROM AUTHOR]

The capture effect is a frequently observed phenomenon in vehicular ad hoc networks (VANETs) communication. When conflicts arise during time slot access, failure to access does not necessarily occur; instead, successful access may still be achieved. The capture effect can enhance the likelihood of multiple access and improve communication efficiency. The security of VANETs communication is undoubtedly the primary concern. One crucial approach to enhance security involves the design of an efficient and reliable medium access control (MAC) protocol. Taking into account both aspects, we propose a novel framed slotted Aloha (FSA) MAC protocol model. Firstly, we derive the closed-form expression for the capture probability in the Rician fading channel in this paper. Subsequently, we analyze how the number of vehicles and time slots influence the success probability of vehicle access channels as well as examine the impact of the capture effect on this success probability. Then, under constraints regarding vehicle access channel success probability, we derive optimal values for slot numbers, access times, and transmission power while proposing a comprehensive implementation method to ensure high access channel success probabilities. We verify both theoretical derivations and proposed methods through simulation experiments. [ABSTRACT FROM AUTHOR]

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes' infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles' requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion. [ABSTRACT FROM AUTHOR]

As distributed computing evolves, edge computing has become increasingly important. It decentralizes resources like computation, storage, and bandwidth, making them more accessible to users, particularly in dynamic Telematics environments. However, these environments are marked by high levels of dynamic uncertainty due to frequent changes in vehicle location, network status, and edge server workload. This complexity poses substantial challenges in rapidly and accurately handling computation offloading, resource allocation, and delivering low-latency services in such a variable environment. To address these challenges, this paper introduces a "Cloud–Edge–End" collaborative model for Telematics edge computing. Building upon this model, we develop a novel distributed service offloading method, LSTM Muti-Agent Deep Reinforcement Learning (L-MADRL), which integrates deep learning with deep reinforcement learning. This method includes a predictive model capable of forecasting the future demands on intelligent vehicles and edge servers. Furthermore, we conceptualize the computational offloading problem as a Markov decision process and employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach for autonomous, distributed offloading decision-making. Our empirical results demonstrate that the L-MADRL algorithm substantially reduces service latency and energy consumption by 5–20%, compared to existing algorithms, while also maintaining a balanced load across edge servers in diverse Telematics edge computing scenarios. [ABSTRACT FROM AUTHOR]

Sixth-generation (6G) announcements promise the best performance not only for latency but also for the number of connected objects. These characteristics particularly suit intelligent transport system (ITS) applications involving a large number of moving vehicles with stringent latency constraints. Moreover, in the 6G era, these applications will often operate while relying on direct cooperation and exchanges between vehicles, in addition to centralized services through a telecommunication infrastructure. Therefore, addressing collaborative intelligence for ad hoc routing protocols that ensure efficient management of multihop vehicle-to-vehicle communications is mandatory. Among the numerous organization models proposed in the literature, the chain–branch–leaf (CBL), a virtual backbone-like model, has demonstrated best performance regarding latency against the state-of-the-art approaches. However, its structure, which lacks redundancy, may lead to higher data loss in the case of the failure of one of the relaying branch nodes. This study investigated how the multipoint relay (MPR) technique—which is intrinsically redundant—used in the optimized link state routing (OLSR) protocol can be efficiently adapted to the road traffic context, especially by restricting MPR selection to a single traffic flow direction (TFD-OLSR). The simulation results confirmed that CBL-OLSR obtains the least end-to-end delay for various types of application traffic due to its efficient reduction in the number of relays and the amount of routing traffic. However, despite higher routing traffic, TFD-OLSR improves the delivery rate, especially for more than two-hop communications, thus demonstrating the benefits of its redundancy property. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) is an intelligent technology that enables efficient communication, secure data transmission, and traffic management. The purpose of routing protocols in the VANET network is to route data between vehicles (V2V) and vehicles-to-infrastructure (V2I). Recently, researchers have shown interest in designing effective routing protocols for the VANET network, as not all existing protocols are suitable for all traffic scenarios. Electric vehicles (EVs) are increasingly being adopted and integrated into intelligent transportation systems (ITS). Developed countries are actively promoting sustainable transportation solutions to increase energy efficiency and reduce carbon emissions. Therefore, this research presents an EV charging station (CS) management scheme based on communication between EVs and RSUs, with performance evaluation simulated using VANET network protocols. In this study, the G-MDORA, MDORA, and geographical routing protocol (GRP) protocols were modified to accommodate V2I communication, and RSUs were distributed along the roadmap. Additionally, a scheme for managing electric vehicle CS was presented, focusing on the communication between electric vehicles and RSUs in the EV2I context. Performance was evaluated using the GMDORA, GRP, and MDORA protocols, considering factors such as throughput, communication overhead, packet delivery ratio, and end to end delay. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) provide a robust infrastructure for intelligent transportation system (ITS) applications. VANET communication involves vehicle-to-vehicle and vehicle-to-infrastructure connections, primarily with roadside units (RSUs). Analyzing cognitive radio (CR)- VANET studies revealed two key performance issues: high energy consumption and latency. To address these challenges, we propose a novel approach: link stability and mobility prediction-based clustered CRVANETs, known as LMCCR-VANET. LMCCR-VANET consists of four main components: CR-VANET construction, clustering model, speed-based mobility prediction, and link-based multipath routing. Initially, we establish cluster-based CR-VANETs to analyze and mitigate spectrum scarcity and power utilization problems in VANETs. Mobility prediction evaluates vehicle speed variations and predictions. Finally, employing link stabilitybased multipath routing (LSMR) in conjunction with the fuzzy interference model and ad hoc on-demand multipath distance vector (AOMDV) routing protocol ensures stable and efficient routing. Experimental results showcase the superiority of LMCCR-VANET. It exhibits enhanced energy efficiency, delivery rates, reduced energy consumption, end-to-end latency, and routing overhead when compared to recent works such as SCCR-VANET, CFCRVANET, and MMCR-VANET. [ABSTRACT FROM AUTHOR]

Summary: Mobility pattern recognition is a complex task in vehicle ad hoc networks (VANET) because the driving state of each vehicle is different. An intelligent transportation system on VANET is used for traffic control and accident prevention. For this reason, human driver behaviour is first analysed to identify mobility patterns. A novel driver behaviour prediction model using a Siamese deep learning architecture is proposed to achieve the goal. Here, an image‐based behaviour prediction model is performed to achieve the highly accurate driving state of the driver. A warning message is forwarded to the neighbouring vehicles based on the driver's behaviour. Due to the dynamic properties of real‐time vehicle mobility, a faster data transmission model is achieved using the ad hoc on‐demand distance vector routing protocol. To achieve faster data transmission and nullified retransmission, here a weighted location‐based routing model is framed. The optimization problem in the location‐aided routing protocol is solved using the vector algorithm's weighted mean. As a result, the proposed method improved the throughput of ASHLOSR to 8.1% and AODV to 7.6%. [ABSTRACT FROM AUTHOR]

Summary: In recent days, VANET is considered as the main hopeful equipment in the system of transportation since traffic congestion arises regularly and thus occurred road accidents very easily. Besides, the network traffic is increasing due to the huge quantity of information generated in region of urban. Therefore, one of the primary challenges faced by Intelligent Transportation System (ITS) is ensuring the accurate transmission of information in both vehicle‐to‐vehicle (V2V) and vehicle‐to‐road (V2R) sensing unit communications. So, here, an adaptive routing controller (ARC) is utilized to improve the data transmission between vehicle and road sensing unit (RSU) and to minimize the traffic density in VANET, and the self‐adaptive search optimization is developed in VANET clustering to prioritize vehicles in the lane, in which the multi‐objective function is intended depending on the energy of the node, acceleration, jitter, priority, velocity, and trust factors. The traffic light control is done to facilitate the effective communication in the network. Thus, the proposed technique is calculated in terms of throughput, jitter, quadratic mean of acceleration (QMA), and spatially distributed travel time (SDTT), which acquired the values of 0.53 s, 36.07 kmph, 3.472 s, and 43.572%, respectively, while using 50 vehicles at 50 s. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is one of the developing paradigms that integrates the automotive industry with the Internet of Things (IoT). The evolution of traditional Vehicular Ad-hoc Networks (VANETs), which are a layered framework for Intelligent Transportation Systems (ITS), is employed to provide Quality of Service (QoS) to end users in hazardous situations. VANETs can set up ad-hoc networks and share information among themselves using Peer-to-Peer (P2P) communication. Dynamic properties in VANETs such as dynamic topology, node mobility, sparse vehicle distribution, and bandwidth constraints can have an impact on scalability, routing, and security. This can result in frequent link failures, instability, reliability, and QOS concerns, as well as the inherent complexity of NP-hard problems. Researchers have proposed several techniques to achieve stability; the most prominent one is clustering, which relies on mobility metrics. However, existing clustering techniques generate overwhelming clusters, resulting in greater resource consumption, communication overhead, and hop count, which may lead to increased latency. Therefore, the primary objective is to achieve stability by increasing cluster lifetime, which is accomplished by generating optimal clusters. A nature-inspired meta-heuristic algorithm titled African Vulture Optimization Based Clustering Algorithm (AVOCA) is implemented to achieve it. The proposed algorithm can achieve load optimization with efficient resource utilization by mitigating hidden node challenges and ensuring communication proficiency. By maintaining an equilibrium state between the exploration and exploitation phases, AVOCA avoids local optima. The paper explores a taxonomy of the techniques used in Cluster Head (CH) selection, coordination, and maintenance to achieve stability with lower communication costs. We evaluated the effectiveness of AVOCA using various network grid sizes, transmission ranges, and network nodes. The results show that AVOCA generates 40% less clusters when compared to the Clustering Algorithm Based on Moth-Flame Optimization for VANETs (CAMONET). AVOCA generates 45% less clusters when compared to Self-Adaptive Multi-Kernel Clustering for Urban VANETs (SAMNET), AVOCA generates 43% less clusters when compared to Intelligent Whale Optimization Algorithm (i-WOA) and AVOCA generates 38% less clusters when compared to Harris Hawks Optimization (HHO). The results show that AVOCA outperforms state-of-the-art algorithms in generating optimal clusters. [ABSTRACT FROM AUTHOR]

The main advantage of Vehicular Adhoc Networks being an inherent part of Intelligent Transportation Systems is to ensure the safety and comfort of commuters on the roads. In VANETs, due to the wide open nature of wireless communications, data transmission has been subjected to variety of security and privacy threats which causes disruptions in the network. Hence data has to be authenticated prior to communication. To address this problem enhanced elliptic curve cryptography based certificate-less signature aggregation scheme has been proposed to ensure full privacy preservation and to provide efficient security to the VANETs. Through formal and informal security analysis the proposed scheme has been tested and proven to be secure under suppositions made using Diffie-Hellman assumptions by using Random Oracle Model. The proposed scheme is implemented using MIRACL C + + library and by using Veins Integrated Simulation Framework. The proposed scheme has been analyzed and compared with the existing standards which prove that the proposed scheme is efficient in terms of computation cost and communication costs. The major novelty of the proposed scheme is the use of aggregation and point addition which does not cause delay in verification and also reduces overhead in the road-side units by employing elliptic curve cryptography based distributed data authentication scheme. Hence, the traditional problems of certificate management and key escrow problem is solved by the proposed scheme in an efficient manner. This makes the proposed scheme to be much suitable for high volatile and resource-constrained vanet environments. [ABSTRACT FROM AUTHOR]