Communication In Vehicular Ad Hoc Networks Research Articles

Comparative analysis of reactive routing protocols for vehicular adhoc network communications

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.

Design of CSKAS-VANET model for stable clustering and authentication scheme using RBMA and signcryption

A public key infrastructure-enabled system authentication model is developed to provide essential security functions for Vehicular ad hoc networks (VANETs). An intelligent transportation system is provided by VANET, an emerging technology. Dedicated short-range communication is used to disseminate messages wirelessly. Communications may be hacked, and messages can be stolen or fabricated. Hence, authenticated communication is crucial in the VANET environment. Some parameters such as trust, authentication, privacy, and security are at high risk. This article suggests a VANET with secure authentication and trust-based clustering mechanisms to provide stable and secure communication. Initially, the Restricted Boltzmann Machine learning algorithm (RBMA) is used to select the cluster head, which depends upon trust, vehicle lifetime, and buffer level. Then, cluster members are formed, followed by grouping. Diffie–Hellman Hyperelliptic Curve Cryptography and cryptographic hash functions are used by signcryption for secure communication in VANET. Therefore, the essential component of the key agreement strategy that will give superior authentication is this signcryption mechanism. Over the medium access protocol layer, all of these security characteristics are updated. The proposed method of clustering signcryption key agreement scheme (CSKAS) approach reduces time complexity and increases packet delivery ratio which is vital in providing stable, secure communication.

Transforming Vehicular Networks: How 6G can Revolutionize Intelligent Transportation?

Vehicular Ad-hoc Networks (VANETs) have enabled intelligent transportation systems by facilitating communication between vehicles and roadside infrastructure. However, the current 5G and 4G networks that support VANETs have certain limitations that hinder the full potential of VANET applications. These limitations include constraints in bandwidth, latency, connectivity, and security. The upcoming 6G network is expected to revolutionize VANETs by introducing several advancements. 6G will provide ultra-fast communication with significantly reduced latency, enabling real-time and high-bandwidth data exchange between vehicles. The network will also offer highly reliable and secure connectivity, ensuring the integrity and privacy of VANET communications. Precise localization and sensing capabilities will be enhanced in 6G-based VANETs, enabling accurate positioning of vehicles and improved situational awareness. This will facilitate collision avoidance, traffic management, and cooperative driving applications. Moreover, integrating edge computing in 6G networks will bring computing resources closer to the edge, lowering response times and facilitating faster decision-making in time-critical scenarios. This paper explores the key features and capabilities of 6G technology and how it can revolutionize intelligent transportation, addressing challenges and opportunities for adopting 6G in VANETs.

Prioritization and scheduling in the vehicle-to-infrastructure (V2I) scheme for vanets using enhanced congestion control source based ant colony optimization

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 direction-aware 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.

Increasing Efficiency and Reliability in Multicast Routing based V2V Communication for Direction-Aware Cooperative Collision Avoidance

Mobile ad hoc networks (MANETs), which are a promising method for the intelligent transportation system, include vehicular ad hoc networks (VANETs) (ITS). Developing reliable and strong cooperative collision avoidance (CCA) strategy to mitigate the growing number of road fatalities each year is one of the main difficulties facing vehicular ad hoc networks (VANETs).A proper and successful routing method aids in the successful expansion of vehicular ad hoc networks. This study explains the architecture, interface layers, safety features, and implementation of a novel priority-based direction-aware collision avoidance system (P-DVCA). It distinguishes our study in the collision area of VANETs by accounting for realistic bi-directional traffic. The scheme begins with the development of dynamic clusters, which is difficult because of the bi-directional diverse traffic and the need to avoid collisions within and between clusters. The target node is sent an early warning message that includes the safe speed and the likelihood of a collision in order to notify it of an impending danger. To determine the least expensive, shortest one with the fewest hops between the source and the endpoint. A crucial problem with VANETs is the transmission of data from a source node to the base station. Cross-layer issues must be solved for a robust and stable collision avoidance programme to function properly in a VANET communication architecture. The results of the simulation show that the suggested scheme significantly outperforms CCM and C-RACCA in terms of cluster stability, fewer collisions, low latency, and low communication overhead. According to the findings, P-DVCA offers stable clustering, minimises network congestion, and lowers communication overhead and latency.

A Novel Framed Slotted Aloha Medium Access Control Protocol Based on Capture Effect in Vehicular Ad Hoc Networks

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.

Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network

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 CR-VANETs, 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 stability-based 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, CFCR-VANET, and MMCR-VANET.

Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc Networks

This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective.

RelCLAS: A Reliable Malicious KGC-Resistant Certificateless Aggregate Signature Protocol for Vehicular Ad Hoc Networks

Vehicular ad hoc network (VANET) enables a more efficient and secure traffic environment by integrating several emerging technologies. The technology of certificateless aggregate signature (CLAS) is widely utilized to guarantee secure communications in VANET. However, the legitimation of vehicles’ public keys cannot be verified effectively in existing CLAS protocols for VANET. Thus, a malicious key generation center (KGC) can masquerade as any vehicle and generate legitimate signatures. In this paper, a reliable malicious KGC-resistant CLAS protocol, called RelCLAS, is proposed to eradicate this problem. RelCLAS solves the key escrow problem owning to the combination of registration-based encryption (RBE) and CLAS. Partial public keys selected by vehicles are stored in a key accumulator to prevent malicious KGC from forging valid signatures. Formal security proof demonstrates that RelCLAS satisfies the fundamental security requirements of VANET. Extensive simulations illustrate that RelCLAS is efficient, reliable, and feasible.

A clustering approach for attack detection and data transmission in vehicular ad-hoc networks

ABSTRACT Vehicular ad-hoc networks (VANETs) are increasingly pivotal for empowering applications in smart cities and intelligent traffic systems. However, the reliability and stability of VANET communications face formidable obstacles. Natura 2000 (N2k), the largest globally coordinated network of protected areas, has drawn significant criticism for its conservation-centric management structure lacking a strategic vision. This study proposes a three-phase strategy to address these concerns, aiming for effective and sustainable N2K site management. The novel approach employs DNN-Assisted Canonical Correlation Analysis (DNN-CCAS), encompassing cluster formation, cluster head selection, and outbreak recognition for enhanced VANET security. Vehicle clustering begins with an amended K-consonance method, emphasizing both position and speed through AKCEM clustering. A cluster head is chosen via a linear measure promenade approach, followed by secure data transmission to the cloud using DNN-CCAS if the cluster head is deemed normal. The proposed method outperforms prevailing techniques, achieving an impressive 91% accuracy. This comprehensive strategy not only addresses VANET communication challenges but also aims to revolutionize the management of N2K sites by integrating a strategic vision into conservation practices.

NextGenV2V: Authenticated V2V communication for next generation vehicular network using (2, n)-threshold scheme

Securing end-to-end communication in Vehicular Ad-hoc Networks (VANET) is crucial due to zero trust between the communicating entities. Additionally, the underlying communication needs to be lightweight in many cases, especially when vehicles are moving at high speeds or deploying applications like Vehicular Sensor Networks (VSN) and next-generation vehicular networks with densely deployed small-cells and micro-cells. The traditional VANET facilitates two types of communication: Vehicle-to-Infrastructure (V2I) communication to establish trust between a Vehicle User (Vi) and roadside infrastructure, followed by Vehicle-to-Vehicle communication for information exchange. Unlike existing protocols that encounter security issues and high authentication delay as they require communication with a Trusted Authority (TA) every time Vi needs authentication or re-authentication, we present a symmetric key-based an authenticated V2V communication for next generation vehicular network using (2, n)-threshold scheme, called NextGenV2V. In NextGenV2V, authentication of a newly visited Vi is performed with the help of TA, but re-authentication of a pre-authenticated Vi is done without the support of TA. This significantly reduces communication overhead during re-authentication and improves the overall authentication delay. NextGenV2V is designed in such a way that at least 2 out of n registered vehicle users must cooperate to compute a shared secret key. This feature ensures improved security, as attackers cannot breach the system’s security unless two or more vehicles are compromised. Furthermore, considering that an attacker cannot physically be present in more than one location simultaneously, the system’s security remains intact even if a current vehicle is compromised. The proposed protocol is also verified to resist known security attacks, both formally and informally. To demonstrate the effectiveness of NextGenV2V, a comparative performance analysis is presented that proves its suitability for next-generation vehicular networks.

Optimizing VANET Communication through Realistic Traffic Simulation

This article explores the optimization of communication in Vehicular Ad-Hoc Networks (VANETs) through the use of realistic traffic simulation in Raipur City, located in the state of Chhattisgarh. VANETs are a specialized type of Mobile Ad-Hoc Networks (MANETs) that enable vehicles to communicate with each other and with roadside infrastructure, improving road safety, traffic efficiency, and providing additional services. However, VANETs encounter challenges due to the dynamic nature of vehicular environments, including high mobility, intermittent connectivity, and varying traffic conditions. To address these challenges, this study proposes the Integration to Message Exchange (IME) method, which incorporates realistic traffic simulation into VANET communication optimization strategies. Realistic traffic simulation models vehicle behavior and interactions with the road network, considering factors such as traffic flow, congestion, and road conditions. By integrating this simulation into VANET communication optimization algorithms, the performance of the proposed IME method and other routing strategies is evaluated under realistic traffic scenarios. The results demonstrate that the IME method significantly improves message delivery rates and reduces message latency compared to the D-LAR and AODV routing methods. In comparison to the DLAR method, the IME method increases the packet delivery ratio by 4.82%, reduces end-to-end delay by 4.0%, increases throughput by 4.85%, decreases routing overhead by 8.79%, and normalizes routing load by 7.71%. The city of Raipur in Chhattisgarh is the geographical context for this research. Raipur is a rapidly growing urban area with diverse traffic patterns and road conditions. By conducting experiments and simulations in this specific setting, the findings of this study can directly address the challenges faced by VANETs in Raipur City.

D-BLAC: A dual blockchain-based decentralized architecture for authentication and communication in VANET

With all the advancements in the last decade in Intelligent Transportation systems (ITS), there has been an exponential increase in the number of applications provided to the Vehicular Ad Hoc Networks (VANETs). These applications provide unmatched security and safety services. As a result, there is a huge surge in the amount of data being generated and transmitted within the VANETs. In this paper, we have proposed a dual blockchain based decentralized architecture for authentication of the vehicles and secure & efficient communication within the network. The use of individual blockchains for authentication and message sharing makes the network more efficient and fast while also helping to increase the network security. Both the blockchains use a different consensus algorithm which adds another layer of security. The security and performance evaluation of the proposed architecture proves that it is resistant to a number of security attacks and has a lower computational cost, higher throughput, lower transmission delay and higher vehicle verification rate by a significant amount when compared to existing schemes.

Blockchain-assisted certificateless signcryption for vehicle-to-vehicle communication in VANETs

In vehicular ad-hoc networks (VANETs), real-time messages are used to guarantee transportation efficiency and passenger safety through the exchange among vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Therefore, the timely acceptance of safety messages is critical to establishing efficient and reliable communication in VANETs, which hinges on an efficient privacy-preserving authentication mechanism. However, these mechanisms face either security or performance problems. Although some state-of-the-art signcryption schemes meet the needs of security requirements of authentication, performance is not efficient in computational overhead for an increase of the bilinear pairing operations in batch unsigncryption verification. Therefore, we have designed an efficient and provably secure blockchain-assisted certificateless signcryption (BACLS) that provides a feasible security communication in theoretical analysis and experiments. The BACLS scheme ensures security against type-I and type-II attackers in regard to existential unforgeability against adaptively chosen message attacks (EUF-CMA) under the hardness assumption of the gap bilinear Diffie–Hellman problem and indistinguishability against adaptive chosen ciphertext attack (IND-CCA2) under the hardness assumption of the elliptic curve discrete logarithm problem in the random oracle model (ROM). The experiments demonstrate that the BACLS scheme significantly reduces unsigncryption computational costs and power consumption in contrast to state-of-the-art schemes, and promotes the development of a decentralized authentication system in VANETs.

LAPEP—Lightweight Authentication Protocol with Enhanced Privacy for effective secured communication in vehicular ad-hoc network

LAPEP—Lightweight Authentication Protocol with Enhanced Privacy for effective secured communication in vehicular ad-hoc network

Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning

A significant subset of ubiquitous computing known as vehicular ad hoc networks (VANETs) serves as a foundational piece of technology for VANET applications. Blockchain technology can be used in microgrids to conduct safe point-to-point transactions between anonymous parties while also addressing needs for security. This research is o proposes novel method in IoT(internet of things) based edge cloud computing architecture with microgrid energy management of VANET. here the VANET communication is carried out based on IoT edge cloud computing module and the smart microgrid architecture is used for energy management in VANET. then each vehicle energy has been analysed using structural reinforcement variational encoder neural networks. experimental analysis is carried out in terms of energy efficiency, network lifetime, training accuracy, QoS, communication overhead. the proposed technique attained energy efficiency of 96%, network lifetime of 85%, training accuracy of 98%, QoS of 95%, communication overhead of 55%.

User grouping and resource management in efficient NOMA‐OMA transmission scheme for infotainment message dissemination in VANETs

AbstractThe data distribution for infotainment services at vehicular ad‐hoc networks (VANETs) has become a research hotspot in recent years. Non‐orthogonal multiple access (NOMA) is accepted as an enabling technology for 5G and beyond due to its improved spectral efficiency. In this work, we consider a VANET communication scenario where users have common data demands such as high‐density maps, local entertainment/shopping options or other popular media content. For such a scenario, we propose an efficient clustering scheme together with a hybrid NOMA‐OMA scheme for improved spectral and power efficiency. For this setup, we also develop an optimal bandwidth allocation strategy to improve the power efficiency further. We formulate a one dimensional nonlinear power minimization problem such that the bandwidth allocation for the system can be optimized. The complexity and optimality analysis of optimization algorithms used for power minimization are performed. The improved performance of the proposed scheme in terms of power efficiency, spectral efficiency and latency performance is showcased in comparison to conventional NOMA and OMA approaches.

FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs

Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score.

Achieving model explainability for intrusion detection in VANETs with LIME.

Vehicular ad hoc networks (VANETs) are intelligent transport subsystems; vehicles can communicate through a wireless medium in this system. There are many applications of VANETs such as traffic safety and preventing the accident of vehicles. Many attacks affect VANETs communication such as denial of service (DoS) and distributed denial of service (DDoS). In the past few years the number of DoS (denial of service) attacks are increasing, so network security and protection of the communication systems are challenging topics; intrusion detection systems need to be improved to identify these attacks effectively and efficiently. Many researchers are currently interested in enhancing the security of VANETs. Based on intrusion detection systems (IDS), machine learning (ML) techniques were employed to develop high-security capabilities. A massive dataset containing application layer network traffic is deployed for this purpose. Interpretability technique Local interpretable model-agnostic explanations (LIME) technique for better interpretation model functionality and accuracy. Experimental results demonstrate that utilizing a random forest (RF) classifier achieves 100% accuracy, demonstrating its capability to identify intrusion-based threats in a VANET setting. In addition, LIME is applied to the RF machine learning model to explain and interpret the classification, and the performance of machine learning models is evaluated in terms of accuracy, recall, and F1 score.

Blockchain-Assisted Privacy-Preserving and Context-Aware Trust Management Framework for Secure Communications in VANETs

Vehicular ad hoc networks (VANETs) are used for improving traffic efficiency and road safety. However, VANETs are vulnerable to various attacks from malicious vehicles. Malicious vehicles can disrupt the normal operation of VANET applications by broadcasting bogus event messages that may cause accidents, threatening people’s lives. Therefore, the receiver node needs to evaluate the authenticity and trustworthiness of the sender vehicles and their messages before acting. Although several solutions for trust management in VANETs have been proposed to address these issues of malicious vehicles, existing trust management schemes have two main issues. Firstly, these schemes have no authentication components and assume the nodes are authenticated before communicating. Consequently, these schemes do not meet VANET security and privacy requirements. Secondly, existing trust management schemes are not designed to operate in various contexts of VANETs that occur frequently due to sudden variations in the network dynamics, making existing solutions impractical for VANETs. In this paper, we present a novel blockchain-assisted privacy-preserving and context-aware trust management framework that combines a blockchain-assisted privacy-preserving authentication scheme and a context-aware trust management scheme for securing communications in VANETs. The authentication scheme is proposed to enable anonymous and mutual authentication of vehicular nodes and their messages and meet VANET efficiency, security, and privacy requirements. The context-aware trust management scheme is proposed to evaluate the trustworthiness of the sender vehicles and their messages, and successfully detect malicious vehicles and their false/bogus messages and eliminate them from the network, thereby ensuring safe, secure, and efficient communications in VANETs. In contrast to existing trust schemes, the proposed framework can operate and adapt to various contexts/scenarios in VANETs while meeting all VANET security and privacy requirements. According to efficiency analysis and simulation results, the proposed framework outperforms the baseline schemes and demonstrates to be secure, effective, and robust for enhancing vehicular communication security.