Vehicular Ad Hoc Networks Scenarios Research Articles

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.

Evaluating End-to-End Delay in Road-Based Routing Protocols for VANETs with Snake Optimization

Vehicular Ad hoc Networks (VANETs) require efficient routing protocols due to their dynamic network topologies and high mobility. This paper proposes a Road-Based Vehicular Traffic (RBVT), which combines reactive (RBVT-R) and proactive (RBVT-P) routing protocols to improved the performance. We evaluate the impact of RBVT on Average End-to-End Delay (AEED) by comparing it with existing protocols such as AODV, DSR, GPSR, OLSR, DSDV, and WRP. Furthermore, we integrate Snake Optimization Algorithm (SOA) with RBVT to optimize routing and reduce AEED. Simulations demonstrate that SOA-RBVT significantly reduces AEED compared to baseline protocols, especially in dense VANET scenarios.

Enhancement of VANETS Security against Gray Hole Attack with ANFIS-GWO and LBK

Vehicular Ad Hoc Networks (VANETs) have emerged as a critical component of intelligent transportation systems, facilitating real time communication among vehicles and infrastructure. However, the open and dynamic nature of VANETs makes them vulnerable to various security threats, among which gray hole attacks pose significant challenges. In gray hole attacks, malicious nodes manipulate or selectively drop data packets, disrupting communication and compromising network integrity. In response to these challenges, this paper proposes an innovative approach that integrates the Adaptive Neuro Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO). The main objective of this research is to develop a robust ANFIS GWO framework capable of identifying and mitigating gray hole attacks in real time VANET scenarios. Through extensive simulations and experiments, the performance of proposed technique in terms of average energy consumed, throughput, Packet Delivery Ratio (PDR) and packet drop is examined. The results demonstrate that the ANFIS GWO approach not only improves the resilience of VANETs against gray hole attacks but also guarantees reliable and secure communication among vehicles and infrastructure.

Shared group session key-based conditional privacy-preserving authentication protocol for VANETs

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Due to the open nature of VANETs, conditional privacy-preserving authentication protocol should be offered against potential attacks. Efficient and secure authentication among vehicles in VANETs are important requirements, but there are various limitations in the existing conditional privacy-preserving authentication protocols for securing VANETs. To cope with the inherent issues, we propose a conditional privacy-preserving authentication protocol based share group session key (SGSK) by integrating the self-healing key distribution technique, blockchain, and MTI/C0 protocol. In our protocol, we use SGSK instead of the time-consuming Certificate Revocation List (CRL) checking, and we revoke malicious vehicles by updating SGSK. It is shared among unrevoked vehicles within a domain and across-domain. As a result, when a malicious revoked vehicle enters a new domain, it is difficult for it to access the system and send false messages. Furthermore, our protocol can not only achieve computation efficiency by reducing the number of computing operations of bilinear pairing but also resist various attacks while keeping conditional privacy protection. We implement our protocol in the Hyperledger Fabric platform. The experimental results show that our protocol is available to revoke 180 malicious vehicles in across-domain scenarios within one second, and it can meet the requirement of verifying 600 messages per second easily. Moreover, our comprehensive performance evaluations demonstrates that our protocol outperforms other approaches in terms of vehicle revocation checking cost, computation overhead, and communication overhead. In addition, to show the feasibility and validity of our protocol, we use SUMO and NS2 to simulate the actual VANET scenario and validate the efficiency and performance of our protocol. Simulation results prove the practicability of our protocol for VANETs.

Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs

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.

Urban Advanced Mobility Dependability: A Model-Based Quantification on Vehicular Ad Hoc Networks with Virtual Machine Migration

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts.

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes’ behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.

A Security-Enhanced Pairing-Free Certificateless Aggregate Signature for Vehicular Ad-Hoc Networks

As self-organizing, highly heterogeneous and multi-interactive networks, vehicular ad-hoc networks (VANETs) are rapidly improving the efficiency of modern traffic. This promising technology is significant for supporting current intelligent transportation by cooperative data sharing and processing. Considering requirements for secure interactions and the limitation of resources in VANETs, protecting data authenticity while enhancing computational efficiency has become the main challenge. A certificateless aggregate signature (CLAS) can not only ensure data security, but also reduce communication costs, which is suitable for a resource-constrained environment. However, most existing CLAS schemes either own serious security vulnerabilities or result in tremendous computational overhead. In this article, we propose an efficient, privacy-preserving, and pairing-free CLAS scheme with enhanced security. We prove that it is existentially unforgeable against adaptive chosen message attacks in the random oracle model. Performance evaluation illustrates that our proposed scheme is more appropriate to the VANETs scenario.

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.

HCSC: A Hierarchical Certificate Service Chain Based on Reputation for VANETs

In vehicular ad hoc networks (VANETs), there are many resource constrained communication nodes, distributed geographical locations, and low delay requirements. The authentication system is required to be distributed. However, the information interaction between traditional distributed authentication entities (AEs) is not transparent, and the use of blockchain is only to save certificate status records on the chain. There is a lack of trusted security management mechanisms between AEs, so there is a risk of collusion attacks and behavior record forgery and tamper attacks between AEs. Therefore, this paper mainly addresses the management problems of opaque interaction and insufficient security verification of distributed authentication entities in the VANETs scenario. In this paper, based on the security features of blockchain technology, such as public audit and tamper proof block structure, and distributed trust consensus, we propose a hierarchical and trust certificate service chain based on reputation value called HCSC. We have a set of safe and reliable management mechanisms between distributed authentication entities such as the Bitcoin system. First, we integrate the traditional hierarchical authentication system and the blockchain network into a new hierarchical certificate service chain. Then, we propose a reputation evaluation model based on the logistic regression model by quantifying the behavior records of the AEs. In addition, a new hierarchical reputation consensus based on delegated proof of stake (DPoS) and proof of work (PoW) is given. Then, a fast verification based on block height and security verification for the efficiency and security of certificate verification is proposed. Finally, the security analysis is given, and a prototype implementation of HCSC is developed based on Fabric. The experimental simulation and security analysis reveal that the HCSC is very efficient and suitable for the above issues.

Smart optimization in 802.11p media access control protocol for vehicular ad hoc network

<span lang="EN-US">The innovative idea presented in this research is that advancements in automotive networks and embedded devices can be used to assess the impact of congestion control on throughput and packet delivery ratio (PDR), or so-called multimedia content delivery. Vehicle networking and the distribution of multimedia content have become essential factors in getting packets to their intended recipients due to the availability of bandwidth. Vehicle-to-infrastructure (V2I) communication systems are crucial in vehicular ad hoc networks (VANETs), which permit vehicles to connect by distributing and delivering traffic data and transmission packet schemes. High levels of mobility and changing network topology necessitate dispersed monitoring and execution for congestion control. The amount of traffic congestion for packet transfers could be reduced by enhancing congestion management in terms of throughput and PDR percentages. In a highway setting, the Taguchi approach has been used to optimize the parameters for congestion control. Based on throughput and PDR performance measures, this technique minimizes superfluous traffic information and lowers the likelihood of network congestion. The simulation results have shown that the proposed approach performs better since it increases network performance while effectively utilizing bandwidth. The effectiveness of the suggested technique is evaluated using a typical VANETs scenario for V2I communication while driving on a highway.</span>

Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization

The Optimized Link State Routing Protocol (OLSR) is envisioned as one of the key components to the operation of future vehicular ad hoc networks (VANETs). Its performance is however fairly constrained in the vehicular communication environment due to the limited coverage of wireless transmission and high-speed movement of nodes. In this brief, we propose a multi-objective particle swarm optimization (MOPSO) framework to enhance the performance of OLSR in VANETs. Specifically, we formulate a multi-objective optimization problem (MOP) considering not only the quality of service (QoS), including throughput, delay and packet loss rate, but also the cost of service, i.e., routing load. We solve this MOP with MOPSO and obtain the Pareto front corresponding to the optimal equilibrium of performance and cost. In the experiment, our optimization framework is used in a general setting of VANETs to obtain the optimal parameters of OLSR, i.e., Hello and TC intervals, and these parameters are further validated in realistic VANET scenarios. Our optimization framework can be naturally extend to other dynamic routing protocols in networks.

End-to-end network slicing in vehicular clouds using the MobFogSim simulator

Fog computing and network slicing are drawing attention as promising technologies in modern networks, such as Vehicular Ad-Hoc Networks (VANETs). On the one hand, fog computing lets end-user devices offload part of their computation and data to micro data centres (i.e., fog nodes) that are pervasively deployed in their proximity. On the other hand, network slicing creates different logical networks over a common physical infrastructure, wherein each logical network, called a slice, aims at meeting the requirements and characteristics of a specific class of applications. In this context, simulators allow the evaluation of new solutions in a cost-efficient and repeatable way. MobFogSim is specifically targeted at allowing the evaluation of resource management solutions in modern networks, including the support of device mobility, fog service migration, and network slicing. In this work, we further extend MobFogSim by presenting new functionalities, namely: (i) the support of VANETs; (ii) the modelling of end-to-end (E2E) slices, which include storage and processing resources of fog nodes besides the network resources; and (iii) improvements to the scalability of our simulator. We validate this work by conducting experiments over MobFogSim to assess its improved scalability as well as to test network slicing and fog computing solutions in three different VANET scenarios: (i) fog_only, wherein fog nodes are deployed only at the edge of the fixed infrastructure; (ii) vehicular_only, where fog nodes are provided only by nearby vehicles; and (iii) hybrid, which combines the previous two. Results show that the vehicular_only approach can reduce the latency provided to end-user devices by around 50% with respect to the fog_only approach. Besides, the improved version of MobFogSim reduces the simulation time by around 65% if compared to the previous version.

Methodology and Performance Assessment of Three-Dimensional Vehicular Ad-Hoc Network Simulation

Packet-based simulation is a key tool for the research and development of Vehicular Ad-hoc Networks (VANETs). Over the last decade, many models throughout the communication stack have been presented, which have increased the degree of realism that can be achieved with popular simulation frameworks. Nevertheless, the three-dimensional aspects of many real-world traffic situations barely find consideration. In this paper, we present a holistic approach to simulate large-scale three-dimensional VANET scenarios. We briefly summarize our previously presented models covering different aspects of communication in 3D scenarios, including an environmental diffraction model, an n-ray ground interference model, and the consideration of multi-floor communication. We then describe the principle of a model selector, which applies the appropriate models depending on the environment of the currently transmitted packet. Subsequently, we use the outlined methodology implemented in our Veins 3D framework to simulate a large urban reference scenario. The results differ significantly from comparable 2D simulations, demonstrating the necessity of three-dimensional considerations. However, they also show strongly increased execution times. Therefore, we further suggest different approaches to improve the simulation performance. Based on these optimizations, simulation durations in the same order of magnitude as a comparable 2D simulation can be achieved.

A VANET Collision Warning System with Cloud Aided Pliable Q-Learning and Safety Message Dissemination

Ease of self-driving technological developments revives Vehicular Adhoc Networks (VANETs) and motivates the Intelligent Transportation System (ITS) to develop novel intelligent solutions to amplify the VANET safety and efficiency. Collision warning system plays a significant role in VANET due to the avoidance of fatalities in vehicle crashes. Different kinds of collision warning systems have been designed for diverse VANET scenarios. Among them, reinforcement-based machine learning algorithms receive much attention due to the dispensable of explicit modeling about the environment. However, it is a censorious task to retrieve the Q-learning parameters from the dynamic VANET environment effectively. To handle such issue and safer the VANET driving environment, this paper proposes a cloud aided pliable Q-Learning based Collision Warning Prediction and Safety message Dissemination (QCP-SD). The proposed QCP-SD integrates two mechanisms that are pliable Q-learning based collision prediction and Safety alert Message Dissemination. Firstly, the designing of pliable Q-learning parameters based on dynamic VANET factors with Q-learning enhances collision prediction accuracy. Further, it estimates the novel metric named as Collision Risk Factor (CRF) and minimizes the driving risks due to vehicle crashes. The execution of pliable Q-learning only at RSUs minimizes the vehicle burden and reduces the design complexity. Secondly, the QCP-SD sends alerts to the vehicles in the risky region through highly efficient next-hop disseminators selected based on a multi-attribute cost value. Moreover, the performance of QCP-SD is evaluated through Network Simulator (NS-2). The efficiency is analyzed using the performance metrics that are duplicate packet, latency, packet loss, packet delivery ratio, secondary collision, throughput, and overhead.

A forward-secure and efficient authentication protocol through lattice-based group signature in VANETs scenarios

Message authentication has been a research hotspot in current vehicular ad hoc networks (VANETs). Many researchers adopt group signatures based on number-theoretic assumptions to authenticate the vehicular users’ identities. Nevertheless, the classical group signature is vulnerable to quantum computing attacks and without considering the negative consequences of secret key disclosure. In this paper, to address these problems, we propose a novel group signature protocol for authentication in VANETs, which is based on lattice cryptography to achieve quantum-resistance and Bonsai-tree signature architecture to achieve forward security. Our scheme is proven secure in terms of traceability, anonymity, and forward-security under the Short Integer Solution (SIS) and Learning With Errors (LWE) hardness. Through comprehensive performance evaluation, we demonstrate that the storage overhead of our scheme is relatively diminutive and the computation cost of the sign and verify algorithms are efficient and practical compared with other existing schemes.

A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs

Routing security attacks in Vehicular Ad hoc Networks (VANETs) represent a challenging issue that may dramatically decrease the network performances and even cause hazardous damage in both lives and equipment. This study proposes a new approach named Multivariate Statistical Detection Scheme (MVSDS), capable of detecting routing security attacks in VANETs based on statistical techniques, namely the multivariate normality tests (MVN). Our detection approach consists of four main stages: first, we construct the input data by monitoring the network traffic in real time based on multiple metrics such as throughput, dropped packets ratio, and overhead traffic ratio. Secondly, we normalize the collected data by applying three different rescaling techniques, namely the Z-Score Normalization (ZSN), the Min-Max Normalization (MMN), and the Normalization by Decimal Scaling (NDS). The resulting data are modeled by a multivariate dataset sampled at different times used as an input by the detection step. The next step allows separating legitimate behavior from malicious one by continuously verifying the conformity of the dataset to the multivariate normality assumption by applying the Rao–Ali test combined with the Ryan–Joiner test. At the end of this step, the Ryan–Joiner correlation coefficient (R–J) is computed at various time windows. The measurement of this coefficient will allow identifying an attacker’s presence whenever this coefficient falls below a threshold corresponding to the normal critical values. Realistic VANET scenarios are simulated using SUMO (Simulation of Urban Mobility) and NS-3 (network simulator). Our approach implemented in the Matlab environment offers a real time detection scheme that can identify anomalous behavior relying on multivariate data. The proposed scheme is validated in different scenarios under routing attacks, mainly the black hole attack. As far as we know, our proposed approach unprecedentedly employed multivariate normality tests to attack detection in VANETs. It can further be applied to any VANET routing protocol without making any additional changes in the routing algorithm.

Comprehensive Review on Misbehavior Detection for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) can increase road safety and comfort. It needs strong demand for security because the data sent in VANETs influence vehicles’ behavior. Existing studies have summarized VANET security, challenge, and attacks. This study aims to present a comprehensive overview of misbehavior detection in VANETs. First, VANET characteristics, security issues, and attacks are discussed. Then, the precise definition of misbehavior, detection mode, and detection objects are presented. Generic misbehavior detection is classified as data-centric and node-centric. In this study, to adapt to the VANETs scenario, we proposed a novel taxonomy of misbehavior detection, which considers the interaction between vehicles and which is refined by emphasizing the detection modes and participants. Finally, the remaining concerns, open issues, and prospective future research directions are discussed.

An Evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs

Many precious lives are lost world-wide due to road accidents. To counter this issue, the ultimate solution is Vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and varying network topology in VANETs, efficient communication among the vehicular nodes is of extreme importance. To enhance communication proficiency in VANETs, clustering is a renowned procedure. Therefore, a clustering algorithm based on Moth-Flame Optimization (MFO), titled AMONET, is projected which can effectively work in high mobility nodes scenario of VANETs. AMONET is based on bio inspired procedure and creates optimized clusters for reliable and efficient communication. Our algorithm is assessed experimentally with well-known procedures such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi Objective Particle Swarm Optimization (MOPSO). Several experiments are conducted to measure the comparative efficiency of these procedures. The average cumulative results for all the grid sizes are 27.1% for AMONET while 36.3% for ACO, 54.9% for CLPSO and 58.7% for MOPSO. The results signify that AMONET produces near ideal results, covers the entire network and generates least number of clusters. It is an efficient technique to accomplish vehicular clustering with the purpose of improving the network’s overall performance and consequently reducing the routing cost of the vehicular network.