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Software-defined networking (SDN) is envisioned to be a main pillar in the management of emerging networks such as satellite networks (SatNets), vehicular ad hoc networks (VANET), and the Internet of Things (IoT). The concept of SDN decouples data and control planes. This allows for flexible management and configuration of network resources, enabling more efficient and dynamic network operations. The location of the controller(s) is a significant design concern for the SDN control plane, impacting several network performance parameters including latency, resiliency, energy efficiency, load balancing, etc. Several solutions have been proposed to address the controller placement problem (CPP). In this paper, we explain the CPP in SDN and discuss its importance before presenting a complete review of the CPP in software-defined emerging networks (SDEN). The problem was addressed in different ways in different emerging networks. This is due to the differences in characteristics and environment among the various types of emerging networks. However, there are some similar characteristics among VANETs, SatNet, and IoT. This allows some CPP solutions to be adopted from other emerging networks. For instance, both VANETs and SatNets are dynamic networks, thus some CPP solutions in VANETs can be adopted and adapted to work in SatNets. Throughout this review, we discuss the influencing factors and environmental requirements which should be considered while placing controllers in SatNets, VANETs, and IoT. Also, we discussed the limitations of existing solutions. Finally, the review concludes with a discussion on critical issues in CPP and potential future research directions. [ABSTRACT FROM AUTHOR]

The Vehicular Ad-hoc Networks includes high-density vehicles, communication access points, data collection and traffic load management. The nodes of these networks are relatively fast, thus accurate data transfer is a major challenge over time. As a result, routing in such networks can be taken as serious issue. To overcome this, an Optimal Geographic Cluster-based Scheduled Routing (OGCSR) protocol for VANET is proposed. It is divided into three stages, first, a multi-hop cluster formation is done using the Improved Chaotic Ant Swarm optimization method. Second, Cluster Head selection by measuring the optimal cluster member and third, searching for the nearby neighbours with Adaptive Neighbour Search algorithm and initiate data scheduling process to reduce the extra time of transmission of control packets. Finally, the performance of OGCSR is evaluated and compared with Cluster Based Life Time Routing, AODV for Conectivity in Vanet and Cluster Based Directional Routing Protocol protocols. As per the results analysis, with a constant speed of 40 kmph, the proposed routing protocol OGCSR gives 13.1% increase in packet delivery rate, 3.5% less delay and better throughput in a high density network. [ABSTRACT FROM AUTHOR]

Authentication of vehicles and users, integrity of exchanged messages, and privacy preservation are essential features in VANETs. VANETs are used to collect information on road conditions, vehicle location and speed, and traffic congestion data. The open exchange of information within VANETs poses serious security threats. Furthermore, existing schemes have higher communication and computational costs, making them incompatible with resource-constrained VANET applications. This study proposes a multifactor authentication and privacy-preserving security scheme for VANETs based on blockchain and fog computing to meet all these requirements. The proposed scheme uses fingerprints and Quick Response (QR) codes as a multifactor to authenticate vehicle users and fog-cloud computing techniques to reduce the computational burden on RSUs and improve service quality and resilience. Additionally, the scheme synchronizes a consistent ledger across all RSUs using blockchain technology to store and distribute vehicle authentication statuses. Through a thorough comparison with relevant current protocols, the scheme shows a much-reduced computing expense and communication burden in situations with high vehicle density within a timeframe of 6.3846 ms and 544 bytes for communication costs. In addition, the proposed scheme demonstrates a successful balance between efficacy and complexity, protecting confidentiality, anonymous authentication, and ensuring integrity and conditional tracking. Formal and informal security analysis showed that the proposed scheme is more reliable, practical, and secure against many hostile attacks, such as modification attacks, 51% attacks, Sybil attacks, and MITM attacks. [ABSTRACT FROM AUTHOR]

Traffic congestion imposes burdens on society and individuals. In 2022, the average congestion cost per auto commuter in the USA was USD1259. New possibilities to increase traffic efficiency are now available as vehicles can interact using Vehicular Ad Hoc Network (VANET) systems, a subset of the Internet of Vehicles (IoV). The traffic assignment problem deals with road network traffic optimization. It is a complex and challenging problem. A few solutions incorporating VANET technology have been presented; most are centralized or depend on infrastructure. In previous work, we introduced Road-ACO, an ant colony optimization (ACO), single-hop, decentralized, infrastructure-less, VANET solution. In this paper, we propose a new multi-hop-enabled, decentralized, ant-colony-inspired algorithm for dynamic highway traffic assignment. The algorithm works for large road networks and requires no infrastructure. We develop Veins framework-based simulations to evaluate the algorithm's performance. The results indicate that the proposed algorithm consistently outperforms Road-ACO and performs optimally on road segments up to 4000 m long, with improvements of up to 40% on average travel time. [ABSTRACT FROM AUTHOR]

Privacy and security issues in VANET have always been a major concern. Existing authentication schemes in VANET mostly rely on computationally intensive operations such as elliptic curve scalar multiplication or bilinear pairings, which are time-consuming and computationally expensive. On the other hand, some lightweight authentication schemes often fail to guarantee sufficient security. In this paper, we propose an efficient batch authentication and key establishment scheme that is resistant to quantum attacks. The proposed scheme is designed to address the challenges posed by existing schemes. It leverages the NTRU algorithm to achieve efficient batch authentication and efficient pseudonymous identity management. Due to the high volume of authentication required in VANET, the proposed scheme is better suited to adapt to the network environment of VANET. Additionally, the scheme incorporates the Chinese Remainder Theorem to enable group key distribution and fast updates following batch authentication. Through formal security proofs, BAN logic analysis, Proverif protocol analysis, and heuristic security analysis, the proposed scheme is shown to resist various common attacks. Compared to other schemes, our proposed solution offers higher efficiency and comprehensive security measures. [ABSTRACT FROM AUTHOR]

Summary: Advanced development in technology raises a huge number of data sources that generate real time data for processing. To process such huge and complex real time data, there is a need for high processing units and more computing resources available at the nearest end, which causes high deployment cost and needs distributed data processing units at different locations. To concern this issue, we proposed a 6G‐based Resource Utilization Framework using SDN in the hybrid vehicular network (HVN). The key objective of this framework is to provide the computing vehicles as resources to complete the task efficiently. Three different technologies are combined into vehicular networks such as Vehicular Fog Computing, Vehicular Edge Computing, and Vehicular Cyber Physical System into integrated HVN. A layered architecture is used to define the data flow and control with the support of SDN and also the fast delivery of data by using 6G cellular communication in the proposed HVN. This scheme uses various algorithms to provide resources through the fast communication between vehicles in Hybrid Vehicular network. An extensive simulation is performed on the proposed scheme using the NS3 simulator. The simulation results outperform the performance of the proposed scheme in terms of various key computing parameters. [ABSTRACT FROM AUTHOR]

The Vehicular Ad-hoc Network (VANET) is emerging as a new networking/communication model that offers remarkable approaches for controlling and managing vehicular traffic. In this scenario, Virtual traffic light (VTL) techniques, advanced navigation support, and fleet management facilities attempt to handle traffic management and control concerns by leveraging vehicular network communication paradigms. The most important communication models in vehicular networks are characterized as vehicle-to-everything (V2X) communications technology. In V2X communications, security issues, and related countermeasures are crucial, as they are in other wireless technologies. Our survey provides a systematic overview of various research directions by highlighting and investigating the most critical V2X security issues and related countermeasures. First, we discuss the points mentioned in the previous papers, including the architecture, main characteristics, and communication models of the V2X networks. Furthermore, some of the most significant applications of these mission-critical networks reported in the published papers and the main security challenges and requirements will be highlighted. We also arrange corresponding (existing) resources, to investigate different solutions for securing V2X communications. Finally, a few suggestions for future research related to V2X security challenges are presented. In general, this article reviews and summarizes the studies of the last ten years. This survey will help make it easier for scholars studying V2X communication security and related fields to keep track of the academic frontier, by offering a sufficiently clear landscape and a roadmap.

The vehicular ad hoc network (VANET) enables vehicles to disseminate messages to improve traffic safety and efficiency. However, the task of enabling consensus through synchronous communication among them remains a challenging problem due to the high mobility of vehicles. This prompts the need for a consensus model suitable for VANETs. Thus, A hierarchical reputation consensus model for VANET has been proposed. The model dynamically partitions consensus groups based on vehicles’ global reputation, which restricts malicious nodes from participating in consensus to improve consensus efficiency. Secondly, an improved event reliability assessment mechanism is introduced into the reputation model to detect the authenticity of the events and mitigate the fabrication of false events. Finally, a hierarchical directed gossip consensus model based on directed acyclic graph (DAG) called as HDGgraph has been proposed. It is aimed at addressing the challenge of achieving consensus when vehicle communication synchronization cannot be guaranteed. In HDGgraph, vehicles can construct a DAG locally and implement virtual voting to achieve local consensus, which can reduce system communication overhead and significantly improve consensus efficiency. Experimental results demonstrate that our proposed approach outperforms some state-of-the-art methods in terms of performance.

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM-based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities.

In this paper, we propose a Proof-of-Location (PoL)-based location verification scheme for mitigating Sybil attacks in vehicular ad hoc networks (VANETs). For this purpose, we employ smart contracts for storing the location information of the vehicles. This smart contract is maintained by Road Side Units (RSUs) and acts as a ground truth for verifying the position information of the neighboring vehicles. To avoid the storage of fake location information inside the smart contract, vehicles need to solve unique computational puzzles generated by the neighboring RSUs in a limited time frame whenever they need to report their location information. Assuming a vehicle has a single Central Processing Unit (CPU) and parallel processing is not allowed, it can solve a single computational puzzle in a given time period. With this approach, the vehicles with multiple fake identities are prevented from solving multiple puzzles at a time. In this way, we can mitigate a Sybil attack and avoid the storage of fake location information in a smart contract table. Furthermore, the RSUs maintain a dedicated blockchain for storing the location information of neighboring vehicles. They take part in mining for the purpose of storing the smart contract table in the blockchain. This scheme guarantees the privacy of the vehicles, which is achieved with the help of a PoL privacy preservation mechanism. The verifier can verify the locations of the vehicles without revealing their privacy. Experimental results show that the proposed mechanism is effective in mitigating Sybil attacks in VANET. According to the experiment results, our proposed scheme provides a lower fake location registration probability, i.e., lower than 10%, compared to other existing approaches. [ABSTRACT FROM AUTHOR]

Vehicle ad hoc network is used on the road where vehicles serve as mobile nodes. VANET's primary uses include intelligent transportation and active security. The framework for intelligent transportation systems relies heavily on VANETs. Intelligent transportation networks are sometimes used to refer to VANETs. In VANETs, however, authentication and user privacy remain crucial issues. This study proposes a novel method for privacy-based authentication algorithms that improve the accuracy of data transmission for smart VANET transportation. Here, a lightweight hybrid authentication-based privacy-preserving algorithm is used to improve privacy. Then, an effective congestion-based clustering algorithm is used to send the data. Throughput, QoS, latency, computational cost, and data transmission rate are the focus of the experimental analysis. Throughput, QoS, latency, computational cost, and data transmission rate were all achieved using the proposed method. The proposed method makes use of lightweight, efficient pseudonyms that can be used for both message authentication and as a trapdoor to provide conditional anonymity. The proposed method's ability to withstand a variety of security and privacy threats is demonstrated by the various attack scenarios and the proposed technique attained throughput 93%, QoS 55%, latency 62%, computational cost 63%, and data transmission rate 69%. [ABSTRACT FROM AUTHOR]

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

Modern smart cities rely on Vehicular Ad-hoc Networks (VANETs) for real-time traffic safety warnings and collision avoidance. However, high mobility, variable driving patterns, and urban contexts in VANET require optimal clustering and stable Cluster Head (CH) selection for reliable communication. Problem statement: Due to dynamic mobility in VANET, maintaining the CH stability and reliable communication is still a challenge. In high-density urban environments, the risk of communication disruptions increases due to the presence of a large number of vehicles competing for limited communication resources. Traditional graph-based suboptimal clusters lead to suboptimal communication reliability and disruptions compared to hypergraph based VANET. Such a modelling leads to reduced overhead and increased packet delay. Proposed Methodology: To overcome these issues of varying mobility in VANET and unstable CH selection, a new CH selection framework with an Adaptive Neighboring Behavior-Incentivization (ANB-I) process for stable and reliable CH selection is introduced. The selection of the CH based on Multi-criteria Multi-decision (MCMD) making of three metrics: Adaptive Neighborhood Degree, Adaptive Link Lifetime (Adaptive -LLT), and Adaptive Relative Average Speed. Results: The suggested approach prioritizes CHs with normal behaviours and longer link lifetimes to balance stability and reliability. The proposed model is tested on MATLAB software and SUMO simulations of 100 (sparse) and 1000 (dense) vehicle densities. The proposed model achieves a 75% CH stability with three optimal numbers of clusters, outperforming previous works by 4.17% improvement. Conclusion: A key aspect of the proposed CH selection is the incorporation of dynamic behaviour of driver's estimation based on the concept of incentive process. By prioritizing CHs with longer link lifetimes and stable driving patterns, the proposed model seeks to mitigate disruptions and enhance the throughput (overhead) of the VANET network. [ABSTRACT FROM AUTHOR]

Software-Defined Vehicular Networks (SDVN) have been established to facilitate secure and adaptable vehicle communication within the dynamic environment of Vehicular Ad-hoc Networks (VANETs). To enhance efficiency, various optimization techniques are employed in cluster-based routing, focusing on reducing energy consumption, improving cluster stability, enhancing throughput, minimizing network overhead, increasing packet delivery ratio, and reducing latency. This work proposes enhancements to dynamic adaptive cluster-based routing to mitigate suboptimal decisions in VANETs. A centralized controller maintains Energy and Distance-Based Clustering and Dynamic Adaptive Cluster-Based Routing (EDBC-DACBR) to optimize VANET clustering and routing. EDBC utilizes energy and distance metrics between vehicles and cluster centres, or Roadside Units (RSUs), for cluster formation. A fitness model identifies Cluster Heads (CH) based on nodes with the highest fitness values, while a Location-Based Fuzzy C-Means (LBFCM) algorithm ensures optimal cluster formation. The resultant CH, chosen for their energy efficiency, stability, and dynamism, are derived by combining the LBFCM with the fitness model. Additionally, DACBR adapts to network variations, such as energy levels, communication distances, and vehicular congestion, to define the shortest path. Simulation-based evaluations demonstrate the effectiveness of the proposed approach, outperforming existing methods such as Learning-Based Cluster-Based Routing (ANFC-QGSOR), Fuzzy-Based Cluster-Based Routing (FCBR), Energy-Efficient-Based Cluster-Based Routing (EEOR), and Hierarchy-Based Cluster-Based Routing (EHCP) in terms of throughput, overhead, packet loss, latency, stability, and network lifetime. Specifically, EDACR achieves a 15% improvement in throughput, reduces network overhead by 20%, increases the packet delivery ratio by 25%, and decreases latency by 30% compared to existing approaches. Furthermore, EDACR enhances network stability, with a 10% reduction in packet loss and a 20% increase in network lifetime. These results highlight the efficacy of EDACR in enhancing the efficiency and reliability of SDVN deployments in dynamic vehicular environments. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined Networking (SDN) can help address these challenges by centralizing network control. The integration of SDN with VANETs, forming Software Defined-based VANETs (SD-VANETs), shows promise for intelligent transportation, particularly with autonomous vehicles. Nevertheless, SD-VANETs are susceptible to cyberattacks, especially Distributed Denial of Service (DDoS) attacks, making cybersecurity a crucial consideration for their future development. This study proposes a security system that incorporates a hybrid artificial intelligence model to detect DDoS attacks targeting the SDN controller in SD-VANET architecture. The proposed system is designed to operate as a module within the SDN controller, enabling the detection of DDoS attacks. The proposed attack detection methodology involves the collection of network traffic data, data processing, and the classification of these data. This methodology is based on a hybrid artificial intelligence model that combines a one-dimensional Convolutional Neural Network (1D-CNN) and Decision Tree models. According to experimental results, the proposed attack detection system identified that approximately 90% of the traffic in the SD-VANET network under DDoS attack consisted of malicious DDoS traffic flows. These results demonstrate that the proposed security system provides a promising solution for detecting DDoS attacks targeting the SD-VANET architecture. [ABSTRACT FROM AUTHOR]

The convergence of pioneering innovations like 5G and edge computing, alongside the rise of vehicle communication requirements, has introduced challenges in vehicular Ad-hoc networks (VANET) research. These challenges have driven the development of novel protocols, devices, and utilization scenarios to enhance vehicle communication via wireless Ad-hoc networks. Due to the complexities of real-world testing, several simulators have been created to evaluate these advancements. This research investigates cooperative vehicular communication in urban scenarios using a MATLAB-based simulation framework. The simulation utilizes two core functions such as UrbanCitySimu and VANET, to model vehicle mobility, communication, and collaborative routing strategies. The Ad-hoc protocol, Ad-hoc On- Demand Vector along with the clustering technique is implemented to measure key metrics such as distance travelled, energy usage, network lifespan, throughput, and bit error rate. These metrics are presented graphically to provide a comprehensive analysis of network behaviour under various road and weather conditions. The graphical user interface further aids in visualizing the simulated network, enhancing the understanding of cooperative communication dynamics for VANET. The findings reveal critical insights into the effectiveness of cooperative strategies, contributing to the advancement of intelligent vehicular communication networks. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANET) are revolutionizing intelligent transportation systems (ITS), and as a result, research on their security is becoming increasingly important. As the primary security concern for VANET, authentication security is still quite difficult to achieve. Consequently, the prior knowledge of VANET is covered in this survey before outlining the primary security concerns. To set itself apart from previous surveys, this study suggests security properties and challenges among VANET. Next, the essential and significant features of a secure VANET system, such as confidentiality and integrity of data, and the availability of network systems have been reported, the authenticity of nodes and messages, and the refusal to deny data once it has been transmitted is detailed. Later, it outlined the requirement of the ITS which makes the survey unique. More importantly, the report on the most recent developments in VANET concentrates on the authentication schemes that have been proposed recently. The security features and authentication resistance against attacks, along with the overhead and efficiency of these schemes, are thoroughly examined and contrasted. A detailed analysis of V2V, V2I, and V2X authentication is been reported. Various cryptographic schemes have been discussed along with some advanced techniques such as Blockchain and hybrid schemes. An overview of the integration of 5G/6G networks is documented. Applications of VANET have been discussed in detail along with some open challenges in VANET. In summary, this work reviews a few lessons learned and explores different possibilities for future research. [ABSTRACT FROM AUTHOR]

ABSTRACT In vehicular ad hoc networks (VANETs), the challenge of broadcast storms during data transmission arises due to an exponential increase in message rebroadcasts. This problem is exacerbated by high‐speed node movements, frequent topology changes, and repetitive discontinuities within these networks, hindering the development of efficient broadcasting protocols. Addressing this gap, our study introduces a pioneering approach utilizing a novel fuzzy method based on multicriteria decision‐making (MCDM) to prioritize vehicles in selecting optimal neighbors for data broadcast. The aim of this work is to propose for VANETs a fuzzy MCDM‐based re‐broadcasting scheme (FMRBS). This method seeks to eliminate broadcast storms and raise data distribution efficiency. We choose the best vehicles for data transportation by using fuzzy logic. The FMRBS system excelled in many respects over UMB and 802.11‐Distance. It decreased end‐to‐end latency and overhead while increasing packet delivery ratio (PDR) and network performance. By efficiently optimizing data distribution inside VANETs, FMRBS lowers broadcasting traffic and network congestion. [ABSTRACT FROM AUTHOR]

The vehicular ad-hoc network (VANET) is a new subcategory of mobile ad-hoc networks that has excellent potential application in intelligent transportation systems. The cyberattack on vehicles can lead to privacy leaks and financial losses and could even become a national public safety issue. In this paper, a multi-level Intrusion Detection System based on Machine Learning (ML) algorithms is developed in order to detect multiple types of attacks on VANET. The proposed model combines two feature selection techniques: random forest (RF) and fast correlation-based filter (FCBF) and four tree-based algorithms: extreme gradient boosting decision tree, RF, decision tree, and ExtraTrees, for classification performance. Moreover, the model is evaluated using CICIDS2017 dataset and Python ML libraries in Jupyter Notebook such as scikit-learn and Pandas. Experiment results show that the proposed model using the stacking method achieves 99.86% attack detection accuracy, 99.85% precision with hypo-parameter (HPO), and 99.83% attack detection accuracy without using HPO. The experimental findings on the vehicular network demonstrate the feasibility of the proposed solution and the ability to implement it in real-time environments. [ABSTRACT FROM AUTHOR]

Intelligent transportation system integration is becoming increasingly dependent on robust and energy-efficient protocols for communication between vehicles and infrastructure (V2V and V2I) networks. This study offers an energy-efficient cluster-based routing protocol for interaction between V2V and V2I, with a focus on traffic forecasting. To improve communication efficiency in dynamic vehicle contexts, the protocol makes use of the capabilities of vehicle ad-hoc networks (VANETs). A novel approach is provided to optimize the routing process and meet the issues posed by variable traffic situations. The suggested method combines two advanced hybrid optimization algorithms: The Adaptive Binary Bird Swarm Optimization Algorithm (ABBSOA) and the Adaptive Golden Eagle Optimization Algorithm (AGEOA). The goal of hybridizing these algorithms is to capitalize on their complementary capabilities, resulting in a synergistic impact that improves the protocol's adaptability and efficiency. Through its feeding, guarding, and flying imitation behaviors, ABBSOA, inspired by the collective behaviors of birds, focuses on global optimization, outperforming local optima. The golden eagle hunting patterns served as inspiration for AGEOA, which introduces indirect and direct effects for improved convergence and solution quality. For efficient communication management, the protocol adopts a cluster-based design, with cluster heads (CHs) dynamically determined using the hybrid optimization algorithm. The ABBSOA-AGEOA-based strategy improves the CH selection process by optimizing job distribution among less burdened CHs in changing traffic circumstances. The simulation results indicate that the proposed cluster-based energy-efficient. The routing protocol performs better than current protocols with regard to traffic forecasting accuracy, packet delivery rate (PDR), packet loss rate (PLR), throughput, network lifetime (NLT), cluster lifetime, cluster build time, and energy consumption end-to-end latency. Comparative simulations demonstrate the suggested method's advantage over current VANET routing techniques like SFSR, ICMFO, and FA-OLSR, as shown by metrics using the proposed systems ABBSOA and AGEOA, which work well together to get through the complicated networks of vehicles. This makes the protocol a new way for V2V and V2I to communicate in smart transportation systems that will last for a long time. [ABSTRACT FROM AUTHOR]

Clustering in vehicular ad-hoc networks (VANETs) plays a pivotal role in enhancing the reliability and efficiency of transmission among vehicles. VANET is a dynamic and highly mobile network where vehicles form clusters to enable effective data exchange, resource allocation, and cooperative actions. Clustering algorithm, helps vehicles self-organize into clusters based on connectivity and proximity, thus improving scalability and reducing transmission overhead. This cluster enables critical applications such as traffic management, collision avoidance, and data dissemination in VANET, which contribute to more efficient and safer transportation systems. Effective clustering strategy remains an active area of research to address the unique challenges posed by the diverse and rapidly changing environments of VANET. Therefore, this article presents a golden jackal optimization-based energy aware clustering scheme (GJO-EACS) approach for VANET. The presented GJO-EACS technique uses a dynamic clustering approach which adapts to the varying network topologies and traffic conditions, intending to extend the network lifetime and improve energy utilization. The results highlight the potential of the GJO-EACS technique to contribute to the sustainable operation of VANETs, making it a valuable contribution to the field of vehicular networking and smart transportation systems. [ABSTRACT FROM AUTHOR]

Vehicular networks (VANETs) are a subset of wireless ad hoc networks that enable communication between moving items and/or with the underlying road infrastructure. More comprehensive and individualized information may be exchanged thanks to wireless connectivity. Every problem relating to wireless communication and ongoing analysis among vehicles is handled by VANET. A new VANET routing model with a link breakdown handling scenario is presented in this study. The first step in the routing procedure is the selection of the best CH, for which the Hybrid Shuffled Shepherd NBO (HSS-NBO) algorithm is introduced and distance, time, energy, and trust are taken into account. Following the optimal CHS, the proposed link dynamic behavior (LDB) measure, link throughput (LT), distance, congestion, and routing are all taken into account. In order to prevent performance degradation, it is also necessary to estimate the connection breakage during routing. Effective management is required if the link breaks. This effort ensures better connection breakage handling by taking link reliability into account, calculating a new distance degree, and evaluating a new node's level of trust based on previous data transfer. In comparison to existing approaches like NBO, SSOA, HHO, LOA, and RHSO, the trust for vehicles counts 100 offers a higher trust of 89.21%. [ABSTRACT FROM AUTHOR]

The paper introduces the VANET Mobility Regression Forecasting Network (VMRF-Net), a new model for effectively predicting vehicular mobility trends in VANETs (Vehicular adhoc Networks). VMRF-Net uses LSTM (Long Short Term Memory) layers with a 20% dropout rate to avoid overfitting and the 'adam' optimization method to improve learning efficiency. The suggested model produces remarkable results, including a 100% packet delivery ratio at lower node densities (20 and 40 nodes), as well as good performance at higher densities, with a minimum end-to-end delay of 0.29 seconds and a throughput of 1 at a node density of 20. Furthermore, the model shows a strong positive relationship between simulation duration and network sustainability, resulting in constant stability periods and longer operational lifetimes across different simulation lengths. These findings demonstrate the VMRF-Net's efficacy in improving VANET performance and resource utilization. [ABSTRACT FROM AUTHOR]

VANETs are useful for V2V (vehicle-to-vehicle) communications in such a special network. Vehicles can also communicate with roadside infrastructure (V2I). These are two important, logical, and useful features of VANET. V2I enables the mobile node to connect to the Internet and enables global communication on the go. The detailed V2V function allows the exchange of data, for example, accidents and environmental conditions [5]. The idea of utilizing radio communications in vehicles to enhance safety has been around for a long time, even before the appearance of digital communications. In recent years, vehicle safety has been focused on communication between vehicles. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Network (VANET), provides considerable real-time traffic information services that enhance safety and traffic effectiveness. However, as most of the VANET systems are centralized in nature prone to single-point failure, and vulnerable to attacks there will be reasonable delay in communication. In this paper, a lightweight privacy-preserving authentication scheme for peer-to-peer communication using blockchain (DLPA) while considering the resource-constrained nature of VANET is proposed. We have designed and deployed smart contracts using public blockchain to resist the vehicle impersonation attack, to identify illegal vehicle's identity and thereby non-repudiation will be achieved. Vehicle-to-Vehicle (V2V) authentication and peer-to-peer communication are attained without the involvement of a Trusted Authority (TA) and to eliminate the trusted third party who is responsible for generating the key. Furthermore, DLPA has achieved handover authentication of vehicles so that vehicles need not be re-authenticated when they enter into a new Road Side Unit (RSU) limit. The proposed scheme is implemented in different Ethereum-powered test networks using Remix IDE to demonstrate the feasibility and to analyze the performance of the smart contract in terms of transaction cost and execution cost. In addition to that, security proof and analysis are performed to unveil that our proposed scheme preserves the privacy of the communicating parties, semantic security of the session key, and resistance against various known threats and attacks. Finally, the performance analysis of the scheme is done by calculating the communication and computation costs. The computation cost for our DLPA scheme for V2V authentication is 0.27461 ms and for V2RSU authentication is 0.15622 ms. The communication cost for both V2V and V2RSU authentication is 640 bits. While analyzing the result, the proposed protocol has a minimal cost when compared with other blockchain-based authentication schemes in VANET. [ABSTRACT FROM AUTHOR]

The Intelligent Transport System (ITS) is very prominent due to its connection with the Internet of Things (IoT), which enhances passenger security and comfort. The Vehicular Ad-hoc Network (VANET) is a component of ITS. It manages the techniques used for routing and privacy in autonomous cars. The increasing number of autonomous cars has exceeded the capacity of current wireless networks for transmission. It is expected that the proposed 6G wireless network can meet VANET criteria. Very little research has investigated the privacy concerns of VANETs in 6G networking connections. This work presents a method for dealing with authentic and privacy concerns for automobiles in VANETs. Our solution strengthens the vehicle's connectivity system by detecting malicious attacks like replay attacks, DoS attacks, and impersonification attacks. The proposed system uses batch authentication to reduce traffic and workload on the network. The proposed system employs both ID-based authentication and deep learning methods. Where the role of ID-based authentication is to check for access in the network, deep learning takes on the role of identifying all the malicious packets in the system. Our result also shows that the proposed system can identify malicious packets with an accuracy of 98.25% and works successfully in 6G networking communication. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) are crucial for infrastructure-less vehicular communication. The dynamic and complex nature of VANETs necessitates efficient routing strategies. This paper introduces an optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR) for VANETs. By utilizing the improved bald eagle optimization (IBEO) algorithm, we achieve optimal clustering that enhances energy efficiency. Cluster heads (CHs) are elected based on trust, calculated from metrics like energy consumption and signal strength. A Poplar optimization algorithm (POA) further refines these metrics. Subsequently, an optimal min–max recurrent neural network (OMM-RNN) determines the next forwarder nodes, facilitating data transfer in VANETs. Implemented using the NS-2 tool and SUMO traffic generator, OSC-GOR showcases optimized clustering and superior network efficiency. Comparative simulations reveal OSC-GOR’s advantages in energy use, latency, delivery ratio, goodput, and routing overhead, outperforming existing methods. Notably, OSC-GOR effectively counters VANET attacks such as GPS spoofing and Sybil threats. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) are becoming important with the advancement of connected technologies, yet they grapple with the pivotal challenge of ensuring quality of service (QoS) and quality of experience (QoE). This stems from the inherently erratic nature and huge data volumes. While reliability and efficiency remain paramount, the need to address QoS and QoE emerges as a critical motivation. Most current algorithms that address streaming data fall short in terms of QoS performance metrics. Thus this work strives to improve upon QoS metrics and further improve QoE. Our proposed method uses deep learning to address these problems in a realistic software-defined vehicular network (SDVN) based on the QoS and QoE. Our research aims to combine SDVN with a recurrent neural network (RNN) in Ryu SDVN controller. The RNN model encompasses a sophisticated architecture comprising multiple layers of recurrent units designed to capture temporal dependencies in data. Through a meticulously crafted training methodology utilizing techniques such as backpropagation through time, it learns to predict future network states based on historical data. Fine-tuning hyperparameters such as the number of epochs and the batch size enables optimal model convergence. We examine this method in a realistic simulation and compare its effectiveness with conventional approaches. The results show significant gains, i.e., marginal to 28% better than the nearest rival and far better than VANET. We also evaluate the network's resilience by varying transmission rate and packet size. Our method functions well in high-density situations, suggesting that real-world deployments can benefit from it. [ABSTRACT FROM AUTHOR]

Nowadays, VANETs are becoming a very interesting research topic for researchers as the benefits are very high in terms of ensuring driver comfort, enhancing road effectiveness and minimizing the risk of accidents. VANET is a wireless network directly linked to the Internet that links multiple vehicles through the use of OBUs (onboard units) to contact and communicate with the other units and RSUs (roadside units). This can be both an advantage and a risk for VANETs as the number of communications offered by this type of network including both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) forms of communication continues to grow, making VANETs increasingly susceptible to many types of cyber security attacks, such as denial of service attacks DOS, false and alternative messages, drive-by downloads, and false alarms. By identifying misbehaving vehicles, intrusion detection systems (IDS) significantly contribute to the protection of vehicle networks. In this research, we present an ensemble learning method, AdaBoost, as the basis for an IDS. To address the attack class imbalance problem, we employed the synthetic minority oversampling approach, or SMOTE, and for feature selection, we used the Chi-Squared technique. By creating new synthetic examples close to the other objects, the SMOTE technique helps to improve the minority classes while preventing overfitting, and Chi squared aids in the solution of the feature selection issue by examining the relationship between features. The NSL-KDD and UNSW-NB15 datasets, two of the most popular datasets these days, will be utilized to test our model. The following metrics were employed to assess our proposed model: It accomplishes this by creating new synthetic examples in feature space that are near to the other points (that is, members of the minority class). Chi squared then assists us in selecting features by examining the relationship between features that utilize the three new features. The tree datasets NSL-KDD, UNSW-NB15, and TON-IOTthree of the most popular datasets these days—are utilized to test our model. The following metrics have been applied to assess our suggested model. We used 10 cross-validation, f1-score, accuracy, precision, and recall to our model. Our model approach outperforms the current IDSs in terms of accuracy, recall, and precision, scoring nearly 100% on all metrics (accuracy, precession, recall, and f1-score). [ABSTRACT FROM AUTHOR]

An efficient data packet delivery in a vehicular ad hoc network (VANET) is still a challenging task because of the rapid changes in the network topology and the instability of link quality, especially for nonsafety applications. Several routing schemes are proposed to improve the reliability of data delivery between vehicles. These protocols still suffer from the growth of the data packet delivery delay, the lost data packets, and the control packets. For this reason, we propose a novel cross‐layer routing protocol, which is the first that uses virtual multibackbones and combines them with the quality of the link to choose the next forwarder vehicle. The next candidate forwarders, among neighbors, are those that keep a minimum of link quality during the data packet delivery delay. Then, among these candidates, we select that is closest to the destination vehicle to receive and forward the data. In addition, our proposal gives priority to those that travel toward the destination vehicle avoiding the forwarding of data several times by the same vehicle. The substantial simulations show that our protocol outperforms certain of the well‐known existing schemes in the VANET area by varying the vehicle density on the highway. It exhibits an improvement of 6.72% in the average end‐to‐end delay of data delivery, 14.44% in the packet delivery ratio, 11.2% in the throughput, and 6.4% in the control overhead as compared to other schemes. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) make it easy to transfer information between vehicles, and this feature is utilized to enable collaborative decision-making between vehicles to enhance the safety, economy, and entertainment of vehicle operation. The high mobility of vehicles leads to a time-varying topology between vehicles, which makes inter-vehicle information transfer challenging in terms of delay control and ensuring the stability of collaborative decision-making among vehicles. The clustering algorithm is a method aimed at improving the efficiency of VANET communication. Currently, most of the research based on this method focuses on maintaining the stability of vehicle clustering, and few methods focus on the information interaction and collaborative decision-making of vehicles in the region. In this context, this paper proposes a networking method for intra-regional vehicle information interaction, through an efficient information transmission mechanism, vehicles can quickly obtain the required information and make more accurate decisions. Firstly, this networking method utilizes DBSCAN and the proposed vehicle scoring model to form clusters, ensuring the stability and adaptability of clusters; secondly, in the process of interacting with the information, the cosine similarity is utilized to check the similarity of the information to eliminate the highly similar information, effectively reducing redundant information; and lastly, in the case of a consensus reached by the cluster, the frequency of broadcasting of information between vehicles is reduced as a way to minimize the waste of communication resources. The proposed method is simulated based on Python and Sumo platforms, and several metrics such as cluster clustering situation, information volume, and state change rate are analyzed. The results show that the method maintains better cluster stability with a 60% and 92% reduction in information overhead compared to the FVC and HCAR algorithms, respectively. [ABSTRACT FROM AUTHOR]