Vehicular Ad Research Articles

An approach for offloading with multi-hop considerations in an RSU signal overlay setting

In recent years, significant advancements in vehicle technology have spurred growing interest in Vehicular Ad hoc Networks (VANETs). This interest is driven by concerns for road safety and the need to alleviate network congestion, leading to the emergence of Intelligent Transport Systems (ITS). ITS focuses on improving road traffic management and safety through the utilization of wireless and mobile network communication technologies. VANETs play a pivotal role within the realm of ITS, facilitating tasks such as enhancing road safety, traffic monitoring, and ensuring passenger comfort by mitigating accidents and congestion. These objectives rely on the timely and accurate delivery of data to vehicle agents and relevant authorities, facilitated by reliable VANETs and Road Signal Units (RSUs). Achieving this necessitates identifying optimal routes with minimal distance, high radio access, and quality-awareness levels. To address these objectives, this study proposes the utilization of the Congestion Network with Predicted K-means multi-hop RSU algorithm (CN-MHMR) to enhance vehicular networking and communication. This algorithm facilitates efficient node transfer from base nodes to destination nodes via the shortest and energy-efficient paths, thereby enabling viable and reliable vehicular communications. The performance of the proposed model was evaluated based on various metrics, including energy consumption, throughput, delay, packet delivery ratio, accuracy, precision, and recall values.

A NEW Technique for Disseminating Knowledge Concerning VANET Traffic Using Crocodile Hunting Search

VANETs or Vehicular Ad hoc Networks represent the most rapidly emerging application of Ad hoc Networks. It shares traffic condition information and is one of the attempts to improve road safety and traffic management. This paper develops a new technique in the name of "Crocodile Hunting Search" (CHS) to assist information dissemination in Vehicular hoc networks (VANETs). It is named after hunting a crocodile, methodically moving to catch prey, which CHS bases many of its principles upon. The CHS solution that we use sends important traffic data in a decentralized, flexible way within the network. From the point of view of VANET, we consider each vehicle as a mobile node that carries the packets with the information contained on the traffic conditions. These packets are solicited and forwarded to the various vehicles in proportion to closeness and usefulness. The point here is that the search perimeter would have to be adapted to the variation in the quantity of vehicles and their actual traffic. If times are off-peak and there is less traffic, the cars would therefore widen their searching area, which could not be the case for other solutions during that time of less traffic. On the other side, in localities with narrow narrowing information-seeking to find only what is essential and related becomes increasingly important. We show through large-scale simulations and real-world experimentation that our method can work in sending information over Vehicular Ad Hoc Networks (VANETs). This perspective, the current research, clearly demonstrates that knowledge propagation is way more effective in spreading information than traditional methods. It causes low packet loss, and the communication overhead is also low. The Crocodile Hunting Search is another practicable method of solving the problems that arise while trying to send information effectively in VANETs.

A Lightweight, Efficient, and Physically Secure Key Agreement Authentication Protocol for Vehicular Networks

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

Exploring VANET Security within the VEINS Framework: A Review

Vehicular Ad Hoc Systems (VANETs) are urgent components of future shrewd transportation frameworks, encouraging communication between vehicles and framework to move forward security, effectiveness, and comfort through vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) intuitive. In any case, the broad selection of VANETs postures critical security challenges, counting potential malevolent assaults and security breaches, which debilitate the unwavering quality and reliability of vehicular communication systems. In reaction to these challenges, the VEINS Vehicles in Organize Recreation system has risen as an unmistakable apparatus for re enacting VANETs and assessing their security instruments. This systematic literature audit points to supplying a comprehensive investigation of investigative endeavours centring on the security angles of VANETs inside the VEINS framework. By synthesizing discoveries from existing considerations, this audit recognizes key security dangers, vulnerabilities, assault models, and countermeasures significant to VANETs re enacted utilizing VEINS. It sheds light on the current state of VANET security inside re enacted situations, highlighting crevices in information and giving experiences into future investigation directions. The discoveries of this audit contribute to a more profound understanding of VANET security challenges and offer profitable direction for analysts, professionals, and policymakers included in planning, creating, and sending secure VANET frameworks. Eventually, the point is to upgrade the security pose of VANETs and guarantee the astuteness and dependability of vehicular communication in future transportation systems.

Challenges and Outcomes of Combining Machine Learning with Software-Defined Networking for Network Security and management Purpose: A Review

Current research in data dissemination in Vehicular Ad Hoc Networks (VANETs) has examined different approaches and frameworks to enhance the effectiveness and dependability of information sharing between vehicles on the road. The integration of Machine Learning (ML) with Software-Defined Networking (SDN) has fundamentally transformed the field of network administration and security. This paper specifically addresses the challenges faced by traditional network architectures in effectively handling the increasing amount of data and complex applications. Software-Defined Networking (SDN), a cutting-edge framework, separates the control of network operations from the actual forwarding of data, offering a versatile and cost-effective solution. The combination of Software-Defined Networking (SDN) and Machine Learning (ML) allows for the extraction of valuable information from network data, leading to enhanced network management and the facilitation of predictive analytics. The aim of this study is to examine the feasibility and challenges of incorporating machine learning into software-defined networking (SDN), focusing particularly on practical applications. Integrating Machine Learning (ML) into Software-Defined Networking (SDN) presents challenges, including the requirement for robust algorithms to detect patterns and ensure security. It is crucial to carry out the tasks of developing and implementing machine learning models for real-time predictions and ensuring the robustness of the system. Research is essential to strike a balance between the transformative abilities of ML-SDN and the practical network environments. This helps to improve the resilience, security, and adaptability of network infrastructures in the digital age.

DDoS Attack Detection in SDN-Based Instrumented Ad Hoc Networks

The physical characteristics of today's urban intersection structures and the traffic flows caused by unplanned road intersections lead to many negative effects such as time/cash loss, stress, increased fuel consumption, and more. For this reason, many studies are being conducted on traffic management systems, an application of smart cities, in both academic and commercial circles. In recent years, it has been observed that the VANET (Vehicular Ad Hoc Networks) architecture, which easily enables communication between vehicles or with devices on the side of the field, thus transporting relevant traffic data to the center, is frequently used in these studies. When Software Defined Networking emerged as a new technology, it brought many advantages such as high availability, scalability, and performance, but also introduced new security vulnerabilities targeted by attackers. This research primarily focuses on a resource-based detection approach by combining the powers of Software Defined Networking and s-Flow-RT technology against Distributed Denial of Service Attacks. In the simulation study conducted within the scope of this work, an SDN (Software Defined Networking)-based DDoS (Distributed Denial of Service) attack was carried out, and changes in data before and after the attack were examined. Traffic for the DDoS attack was generated with the Hping3 application. The RYU controller (a component-based software-defined networking framework) was selected as the software-defined network controller to create software-defined networks, and the Mininet emulator was used. A traditional computer network's Ubuntu virtual machine was used to carry out the attack in the scope of the work.

Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks.

Vehicular clouds represent an appealing approach, leveraging vehicles' resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance.

A hybrid fennec fox and sand cat optimization algorithm for clustering scheme in VANETs

The popularity of intelligent vehicles with cutting-edge vehicular applications has fueled the rapid expansion of Vehicular Ad hoc Networks (VANETs) in recent years. VANETs are a network of vehicles designed to exchange and explore real-time data using a well-developed and effectively organized data transport technology. However, the major issue of dynamic topology and cluster stability always has an impact on choosing an optimal path between the cars. At this point, an intelligent clustering technique in VANETs that handles dynamic topology and cluster stability is critical for efficient route selection between vehicular nodes. This is an NP-hard issue that can be effectively solved using an intelligent nature-inspired algorithm that can discover near-optimal solutions in the search space. An Intelligent Hybrid Fennec Fox and Sand Cat Optimization Algorithm (HFFSCOA) -Based Clustering Scheme is proposed in this paper as a novel route clustering optimization strategy that takes grid size, orientation, velocity node density, and communication range into account while achieving its goal. This HFFSCOA contributed to the route clustering process, which determines dependable and optimal routes between vehicular nodes for the purpose of building and evaluating ideal Cluster Heads (CHs) in the network. HFFSCOA's findings clearly demonstrated its usefulness and efficacy in terms of the number of vehicles, network size, changeable communication ranges, and number of clusters built in the network. The statistical results of HFFSCOA also confirmed an enhanced cluster Optimization rate of 56.21% and an increased cluster stability of 92.34.

Blockchain-Based Trust Management Model for Vehicular Ad Hoc Networks

Blockchain-Based Trust Management Model for Vehicular Ad Hoc Networks

EAPRAD: A MAC protocol for Enhancing Access Probability and Reducing Access Delay in VANETs

Vehicular Ad Hoc Networks (VANETs) present challenges in terms of access collision and merge collision, leading to increased access delay in end-to-end vehicle communication. How to address these issues remains an unsolved problem. In this paper, we propose EAPRAD (Enhancing Access Probability and Reducing Access Delay), a novel MAC (Media Access Control) protocol based on a Markov model. Our protocol defines the number of vehicle accessing time slot failures, with different failure states forming a partial Markov chain. The highest failure status vehicle is prioritized to access the time slot, effectively resolving the problem of indefinite time slot reservations during the back-off phase. Furthermore, after acquiring the time slot, vehicles may still experience package collision, particularly when vehicles traveling in opposite directions share the same frequency band. To address this, our protocol introduces a new MAC frame structure that partitions available frequency band resources based on distinct driving directions, eliminating merge collision in opposite directions. For vehicles traveling in the same direction, intermediate nodes are utilized to proactively anticipate and prevent package collision. To model the state transition of access collision probability and merge, we develop a Continuous Time Markov Chain (CTMC) analytical model. Simulation results demonstrate that EAPRAD reduces end-to-end delay and beacon lost rate (BLR), thereby improving overall network performance in VANETs.

Seagull Optimization Algorithm with Share Creation with an Image Encryption Scheme for Secure Vehicular Ad Hoc Networks

Α Vehicular Ad hoc Network (VANET) allows transmission, amid moving or stationary vehicles via wireless technology. Amongst several problems, safe transmission is the most important one in smart VANETs in 5G networks. Smart vehicles require integration with advanced road systems encompassing smart payment and traffic control systems. Numerous security mechanisms are used in VANETs to ensure safe communication. One such mechanism is cryptographic digital signatures based on encryption. This study introduces the new seagull optimization algorithm involving share creation with an image encryption scheme (SGOA-SCIES) for secure VANET transmissions. The goal of the SGOA-SCIES technique is to create a considerable number of shares and encrypt them to accomplish security. In the SGOA-SCIES technique, a Multiple Share Creation (MSC) scheme is employed to generate numerous share sets. For the share encryption process, the SGOA-SCIES technique engages the Fractional-Order Chaotic System (FOCS) approach to encrypt the generated shares. The optimal keys of the FOCS method can be chosen by the SGOA usage, which ameliorates the security level. The performance evaluation of the SGOA-SCIES method is examined on benchmark data. The simulations demonstrate the enhanced SGOA-SCIES methodology outcome and compare it with the ones of other existing systems and under the implementation of various measures.

An Enhancement of Identity Based Conditional Privacy-preserving Authentication Process in Vehicular Ad Hoc Networks

In general, Vehicular Ad hoc Networks (VANETs) are permitting the communication between one vehicle with neighboring vehicles, infrastructure, and Road-Side Unit (RSU). In this, vehicle platoon is commonly known as the vehicle driving pattern it categorizes the batching of the vehicle in the on the trot fashion. It has been reviewed as an effective resolution to mitigate the reduction in traffic blockage and to widen the opulence of the travel. However, the malicious activities of any unauthorized person in VANET are increased the damage to authorized vehicles. In this manuscript, the Identity based Conditional Privacy-Preserving Authentication (ID-CPPA) signature scheme is proposed to detect the malignant command vehicle very efficiently by the consumer vehicle. In this, the proposed ID-CPPA method uses one-way hash functions for improving the efficiency of Road-Side Unit (RSU) signing and verification of a messages. In order to provide better concealment to the vehicle, Phase Truncated Fourier Transform based asymmetric encryption algorithm (PTFT-AE) is proposed. Thus, the proposed ID-CPPA-PTFT-AE approach has achieved 28.96%, 37.58%, 31.36% higher security rate and 25.8%, 37.9%, 42.6% lower delay than the existing MPDC-LPNS, PPSR-GS, and WCAA-TST methods respectively.

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.

Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning

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

An ontological approach to the detection of anomalies in vehicular ad hoc networks

In a vehicular environment, by becoming connected, vehicles are subject to more threats in comparison to traditional information systems, with the difference that, as a cyber-physical system, anomalies and intrusions could have repercussions in the physical world. In this work, we have developed an ontological anomaly-detection approach (OADA). The anomalies studied in this work mainly concern: network scans, DNS tunnel attacks, and telemetry data anomalies. Our contribution relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic, such as flow or latency. They are subject to the process of detecting anomalies and intrusions, carried out thanks to the algorithm with HTM. For each entry, the algorithm produces a score that allows us to decide if a window is abnormal and to lift an alert if that is the case. We evaluated our system using a communications and anomalies emulation tool. We use the corpus of data produced thanks to Autobot. We seek to determine from among the best scores of Matthews correlation coefficient (MCC) and Detection efficiency score (DES) which were the parameters for which HTM detects all anomalies with the greatest possible coverage. The obtained results prove that HTM can detect all anomalies for each window duration.

Cryptanalysis of Two Conditional Privacy Preserving Authentication Schemes for Vehicular Ad Hoc Networks

Conditional Privacy Preserving Authentication (CPPA) schemes are an effective way of securing communications in vehicular ad hoc networks (VANETs), as well as ensuring user privacy and accountability. Cryptanalysis plays a crucial role in pointing out the vulnerabilities in existing schemes to enable the development of more resilient ones. In 2019, Zhang proposed a CPPA scheme for VANET security (PA-CRT), based on identity batch verification (IBV) and Chinese Remainder Theorem (CRT). In this paper, we cryptanalyze Zhang’s scheme and point out its vulnerability to impersonation and repudiation attacks. In 2023, Zhang’s scheme was cryptanalyzed by Tao; however, we point out flaws in Tao’s cryptanalysis due to invalid assumptions; hence, we propose countermeasures to Tao’s attacks. Furthermore, in 2021, Xiong proposed a Certificateless Aggregate Signature (CLAS) scheme which is also cryptanalyzed in this paper. Finally, we analyze the causes and countermeasures by pointing out the vulnerabilities in each scheme that enabled us to launch successful attacks and proposing changes that would fortify these schemes against similar attacks in the future.

Fog Computing in Vehicular Ad Hoc Network Applications: A Survey of Challenges and Scope

Abstract: The use of computationally intensive applications requiring substantial storage resources in vehicular ad hoc networks is on the rise due to the technology shift from smart, intelligent vehicles to autonomous vehicles. Cloud computing addresses this problem to some extent by providing computational and storage facilities for the significant amount of data generated by vehicular nodes. However, it still cannot meet the stringent requirements of highly dynamic vehicular nodes in a real-time environment. The delay-sensitive applications of vehicular ad hoc networks instill the need for computing facilities in the closest possible proximity to the vehicular nodes. Therefore, fog computing has found potential in vehicular ad hoc network applications. Fog computing brings storage and computing resources closer to vehicles. Fog-based vehicular ad hoc networks can help improve the issues of first-generation vehicular ad hoc networks, such as latency, location perception, and concurrent response. Based on existing research, using fog computing decreased the delay by 50-60% and 60-70% in vehicular ad hoc network safety and entertainment and commercial applications, respectively, compared to cloud computing. An extensive study of the amalgamation of fog computing and vehicular ad hoc networks should be conducted to understand the underlying challenges of fog-based vehicular ad hoc networks. Existing surveys only include specific applications of this technology. However, we present an extensive survey focusing on the architecture of these two technologies, their integration, use cases of fog vehicular ad hoc networks, simulation tools, important performance metrics, the challenges in multiple contexts, and the gaps that need to be addressed, as well as suggest prominent journals in this field. This paper will not only impart knowledge to researchers in this area but also provide them with insights to identify the challenges and pursue their work further in fog-based vehicular ad hoc networks.

An Enhanced Location-Aided Ant Colony Routing for Secure Communication in Vehicular Ad Hoc Networks

The dynamic characteristics of vehicular ad hoc networks (VANETs) demand reliable and secure communication over wireless media. However, there are significant contradictions in autonomous vehicular systems related to security and privacy. Furthermore, VANETs require seamless data transmission to exchange time-critical data about accidents and traffic. Therefore, VANETs require secure seamless data transmission mechanisms to provide secure reliable communication without communication interruption to exchange time-critical data between vehicles. This paper proposes an Enhanced Location-Aided Ant colony Routing (ELAACR) for VANETs to achieve seamless secure data transmission. The proposed ELAACR is a two-fold scheme in which Phase 1 implements Location-Aided Key Management (LAKM) and Phase 2 implements Ant Colony Routing (ACR). An ACR combined with an LAKM pattern for offering secure and reliable communication. The LAKM scheme works based on the group signature concept to construct a secure network, and an ACR is used to find an efficient secure shortest path for seamless data transmission. The proposed work is simulated in Network Simulator (NS 2.35). By using an efficient and secure shortest path with secure seamless data transmission, the proposed ELAACR achieves an average throughput (T) of 640 Kbps, packet loss rate (PLR) of 0.018%, packet delivery ratio (PDR) of 98.5%, overhead (OH) of 0.157%, and 0.16 s end-to-end delay (EED) compared to Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) and Fuzzy Based Ant Colony Optimization (F-ANT). The simulation results show that the proposed ELAACR registers better performance in all aspects compared to the existing EHACORP and F-ANT.

Retracted: Enhancing Vehicular Ad Hoc Networks’ Dynamic Behavior by Integrating Game Theory and Machine Learning Techniques for Reliable and Stable Routing

Retracted: Enhancing Vehicular Ad Hoc Networks’ Dynamic Behavior by Integrating Game Theory and Machine Learning Techniques for Reliable and Stable Routing

Retracted: An Anonymous Signature-Based Authentication and Key Agreement Scheme for Vehicular Ad Hoc Networks

Retracted: An Anonymous Signature-Based Authentication and Key Agreement Scheme for Vehicular Ad Hoc Networks