Communication In Vehicular Ad Hoc Networks Research Articles

ANEL: a novel efficient and lightweight authentication scheme for enhancing security in Vehicular Ad Hoc Networks (VANETs) using elliptic curve cryptography

Vehicular Ad-hoc Networks (VANETs) offer immense potential for revolutionizing modern transportation systems through real-time communication and interaction between vehicles and roadside infrastructure. These networks can significantly enhance traffic management, improve safety, and support various vehicular services. However, ensuring secure communication in VANETs remains a critical challenge due to the highly sensitive nature of the data exchanged, such as location and personal information. Current authentication schemes, including the ANEL scheme, exhibit vulnerabilities like data interception and tracking risks. In this paper, we propose a novel, efficient, and lightweight authentication scheme that leverages elliptic curve cryptography (ECC) and message mapping techniques to address these security issues. The proposed solution strengthens encryption, enhances privacy protection, and mitigates risks such as unauthorized access and man-in-the-middle attacks. Furthermore, the scheme offers scalability and distinguishes between regular and priority users, making it adaptable to various VANET applications. This approach significantly improves the overall security and privacy framework of VANET systems while ensuring the efficient use of computational resources in vehicular environments.

Robust cryptographic scheme for reliable data communication in VANET (RCSRC) using clustering approach

In contemporary times, as accident rates continue to rise, there is a growing interest in vehicular ad hoc networks (VANETs) as a means to reduce accidents and vehicle damage, as well as enhance public safety. VANETs facilitate the transmission of messages within predefined areas to ensure system safety and efficiency. However, ensuring message authenticity in such a dynamic environment presents a significant challenge, with previous research focusing less on security aspects. Some previous protocols send plain message, which leads to the compromise of the message confidentiality. Although few algorithms work on security, they involve the communication to the trusted authority, which lead to increased latency. Thus, in this paper we propose a robust cryptographic scheme for reliable data communication in VANET which minimizes the involvement of trusted authority and uses communication ID of vehicle for communication in place of real entities. Security analysis of the proposed scheme is showing the sturdiness of the proposed protocol again various types of safety spasms. The evaluation of its performance includes assessing communication cost and time, revealing the proposed algorithm's efficiency compared to others.

Mitigating Intermittent Connectivity Problems in Vehicle-to-Vehicle Communication (V2VC): A Sparse Network Computational Model (SNCM)

INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.

Enhancing Black Hole Attack Detection in VANETs: A Hybrid Approach Integrating DBSCAN Clustering with Decision Trees

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project’s overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load.

Escrow-free and efficient dynamic anonymous privacy-preserving batch verifiable authentication scheme for VANETs

To enhance road safety, Vehicular Ad-hoc Networks (VANETs) facilitate the exchange of safety-critical messages between smart vehicles and road traffic authorities. However, VANET’s wireless channels are prone to several attacks, such as replay or modification. Therefore, to protect the links, robust authentication and message integrity mechanisms are required. Previously, several robust authentication schemes have been devised. However, those designs often struggle with complex certificate management, the key escrow problem, and the necessity for secure channels to establish user keys. Additionally, prior methods rely on pseudonyms to ensure user privacy. To implement it, several pseudonyms are stored in the vehicle’s device, which burdens the device. To overcome these limitations, this study introduces an efficient and escrow-free dynamic anonymous authentication scheme tailored for VANETs. By utilizing the paradigm of certificate-based cryptography and fuzzy identity generation, the proposed design eliminates the limitations. Through rigorous security analysis, the proposed design’s effectiveness against various threats is demonstrated. Furthermore, a detailed performance analysis, including computational and communication cost comparisons, showcases the scheme’s feasibility for VANET deployment. An NS-3 simulation further confirms the suitability of the proposed scheme for real-world VANET communication scenarios.

A Blockchain-assisted lightweight privacy preserving authentication protocol for peer-to-peer communication in vehicular ad-hoc network

A Blockchain-assisted lightweight privacy preserving authentication protocol for peer-to-peer communication in vehicular ad-hoc network

Smart AODV Routing Protocol Strategies Based on Learning Automata to Improve V2V Communication Quality of Services in VANET

The Adhoc On-Demand Distance Vector (AODV) protocol faces challenges in selecting the best relay nodes, which requires optimization to improve performance in Vehicular ad-hoc networks (VANETs). This study aims to enhance Vehicle-to-Vehicle (V2V) communication in VANETs by implementing the Learning Automata-Driven Ad-hoc On-Demand Distance Vector (LA-AODV) routing protocol. LA-AODV is designed to achieve higher packet delivery ratios and optimize data transfer rates, even under congested network conditions, by dynamically adjusting to changing network scenarios. The performance evaluation includes six key metrics analyzed under varying node densities and time intervals, comparing LA-AODV against the standard AODV protocol. Results indicate that LA-AODV consistently outperforms AODV, demonstrating improved efficiency in flood identifier management, reduced data loss, higher packet delivery ratios, better throughput, and reduced end-to-end delay and jitter. Specifically, under a 20-node scenario, LA-AODV exhibits lower flood ID scores (54 vs. 88), reduced packet loss (11% vs. 12%), higher PDR (88.0% vs. 87.0%), and superior throughput (85.34 Kbps vs. 47.26 Kbps). Additionally, LA-AODV achieves lower end-to-end delay (6.84E+09 ns vs. 3.76E+10 ns) and jitter (2.52E+09 ns vs. 2.15E+10 ns). These findings suggest that LA-AODV significantly enhances Quality of Service (QoS) in vehicular ad-hoc networks, positioning it as a promising solution for optimizing V2V communication performance.

Intelligent defense strategies: Comprehensive attack detection in VANET with deep reinforcement learning

Vehicular Ad Hoc Network (VANET) facilitates the exchange of vehicular information through Vehicle-to-Vehicle (V2V) communication, contributing to Cooperative Intelligent Transportation Systems (C-ITS). The transmitted messages among vehicles are vulnerable to various security threats executed by malicious insider nodes. The dynamic VANET necessitates context-aware solutions for detecting various security attacks. Existing learning and deterministic mechanisms showed high detection accuracy for attacks on which they were trained explicitly for large datasets. Therefore, we propose an intelligent framework utilizing Deep Reinforcement Learning (DRL) for attack detection in evolving scenarios and mitigate the need for extensive training datasets. Our approach employs a Deep Q Network (DQN) trained on a compact dataset encompassing multiple attacks. The trained model is then applied to an unknown and extensive dataset, detecting various attacks with high accuracy. Notably, the model autonomously updates itself upon observing changes in the network context. This framework represents a promising security solution that is effective and adaptable for V2V communication in VANET.

A hybrid CNN-GRU-based intrusion detection system for secure communication in vehicular adhoc network

ABSTRACT Vehicular Ad hoc Network (VANET) is a component of the Intelligent Transportation System (ITS) which furnishes communication among vehicles. It delivers comfort and safety information to passengers and vehicle drivers. Security plays a vital role during the transmission of data as the various distinct security attacks directly influence the safety of the passengers on the road. Several security attacks will disrupt normal functions like the transmission of data. Some security breaches inject false information which affects the safety of drivers. However, there are several demurrers in detecting abnormal activities efficiently as the traditional system faces imbalanced data problems. This paper presents an Intrusion Detection System (IDS) for detecting anomalies and protecting communication systems from several distinct attacks. The proposed IDS utilizes two deep learning (DL) mechanisms such as Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) for detecting anomalies in the vehicular network. The hybrid CNN-GRU mechanism helps in solving the overfitting and low-velocity problems. It also detects several attacks and protects the ad hoc network from those attacks to safeguard the vehicle users. The results show that the propounded model outperforms the other traditional detection mechanisms by 10.79% in terms of performance compared to other IDS.

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.

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.