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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]

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]

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]

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]

Summary: The vehicular ad hoc network (VANET) provides a variety of applications and is gaining popularity due to the reuse of network resources. Exact location identification with optimum delay is the demand of all vehicle users. Currently, individuals are utilizing the Global Positioning System (GPS) to ascertain the precise geographical coordinates of a given location. The drawback of GPS technology is its inability to accurately determine location and use GPS in some remote areas. Searching for the location of a particular house randomly incurs a loss of fuel as well as increases delay. This motivates us to propose one automated method through VANET using location service‐based routing for the location identification of a house. The proposed work involves searching for the location of a house using the open‐source MongoDB database, and the operations on the database are performed using the tool Node‐Red. By simulation using SUMO and Network Simulator 2.35, the proposed work is evaluated and compared with existing location service‐based routing like geographic location service (GLS) and hierarchical location service (HLS). The proposed work performs better in terms of routing efficiency (end‐to‐end latency, packet delivery rate), location efficiency (request sends, query success rate, and request travel time), and routing and location overhead (MAC bandwidth consumption). Also, the performance of the proposed work is presented as stable by increasing the number of vehicles. The statistical analysis of the packet delivery ratio and CBR end‐to‐end latency is carried out using T score. [ABSTRACT FROM AUTHOR]

To address the issue of diminished LBS privacy protection capabilities in sparse user environments within vehicular networks, this paper proposed a hybrid privacy protection scheme based on homomorphic encryption. The scheme exploited the properties of homomorphic encryption to enable the on-demand generation and indistinguishable authentication of vehicle users' virtual identities. By obfuscating the virtual addresses with the vehicles' real locations, it further provided hybrid privacy protection based on vehicle identity and location. Security analysis demonstrates that, in addition to effectively preserving service privacy, the scheme is traceable against malicious vehicle identities, and it effectively defends against denial-of-service attacks and pseudonym forgery attacks. Performance analysis results indicate that, compared to traditional pseudonym generation methods and k-anonymity techniques, as the privacy factor k increases to 40, the scheme reduces computational overhead by 25% in dynamic pseudonym generation and achieves over 91% privacy protection, this compensates for the deficiencies of k-anonymity and pseudonym techniques in specific environments, making it more suitable for dynamic, low-latency vehicular network communication environments. [ABSTRACT FROM AUTHOR]

Unmanned mine detection vehicles (UMDVs) have been used for military missions to detect and deactivate mines and reduce military and civilian casualties. UMDVs must cover an area of several kilometers to detect mines. Hence, a central coordination system is required for these UMDVs, and it has been achieved via the development of a vehicular ad hoc network (VANET). UMDVs can communicate with each other by broadcasting mine detection messages (MDMs) to incoming vehicles in VANET without infrastructure installations. Therefore, a novel mine detection vehicle routing (MDVR) protocol has been developed to create an ad hoc communication network among UMDVs. The protocol performs cluster-based multicast communication in real time. It adapts to dynamic scenarios by proposing a priority-based cluster head election scheme (PBCHE) and introducing cluster adaptability level schemes. Network simulator results show that the MDVR protocol can reduce the overhead and delay in MDM dissemination. In addition, the MDVR protocol shows promising results in terms of throughput, packet delivery ratio, and cluster stability. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the Internet of Things and the surge in the volume of data exchanged in it, cloud computing became more significant. To face the challenges of the cloud, the idea of fog computing was formed. The heterogeneity of nodes, distribution, and limitation of their resources in fog computing in turn led to the formation of the service placement problem. In service placement, we are looking for the mapping of the requested services to the available nodes so that a set of Quality-of-Service objectives are satisfied. Since the problem is NP-hard, various methods have been proposed to solve it, each of which has its advantages and shortcomings. In this survey paper, while reviewing the most prominent state-of-the-art service placement methods by presenting a taxonomy based on their optimization strategy, the advantages, disadvantages, and applications of each category of methods are discussed. Consequently, recommendations for future works are presented. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) is one of the challenging research areas in recent times with applications to intelligent traffic systems. In VANET, the link between vehicles going in opposite directions only lasts for a very short time when they are within range of each other, otherwise lost, hindering the complete exchange of meaningful information. Most valuable applications on VANET are related to safety-related applications that require real-time response. If the safety and the real-time requirements are not met, serious consequences may result in the form of traffic accidents or failed rescue operations. Flooding attacks on route request packet (RREQ) disrupt the communication between parties, reduce the successful packet delivery rate, and introduce excessive packet transmission delays. This type of attacks, when executed over VANET, may result in disastrous consequences for critical applications such as collision warning or autonomous vehicle assistance. Many security solutions have been proposed; however, they all have several limitations: either they fail to recognise malicious nodes when they attack with low frequency rates, or they suffer performance degradation because of the burden of add-on security measures by the solutions even when malicious nodes are not present in the environment. This paper proposes a Median Filter based flooding attacks detection algorithm (MFFDA) that enables efficient and reliable operation on VANET. The MFFDA solution is novel in three aspects: (1) it uses a route discovery frequency vector (V) to capture a node's behavioral history; (2) it uses a nonlinear mapping to transform the representational space V into a new space VMF through the use of a Median Filter; (3) it utilises a robust statistic-the median value (mv)—of the data sample V and a suitable separating hyperplane to detect malicious nodes. The paper also proposes the Flooding Attacks Prevention and Detection Routing Protocol (FAPDRP), that incorporates the proposed MFFDA for routing protection. Using NS2, the paper simulated the FAPDRP and related protocols in both attacked and normal network scenarios. The results show that the proposed solution has an accuracy over 98.5% under the minimal flooding attacks 10 pkt/s, outperforms those of previous studies. In addition, the performance of FAPDRP approaches that of the AODV in both attacked and normal network scenarios. This confirms that the proposed MFFDA satisfies the delay, efficiency, and reliability constraints for VANET environment. [ABSTRACT FROM AUTHOR]

VANET technology is an essential component of Intelligent Transportation Systems, which makes c communication between moving cars and stationary Road Side Units more accessible. It allows vehicle nodes to share crucial data among communication devices. VANET has significant potential to enhance traffic efficiency and road safety. This is accomplished by decreasing the chances of collisions between vehicles and reducing the number of accidents. Man-in-the-middle (MITM) attacks are a crucial issue in VANET which needs significant consideration from researchers. To solve the problem of man-in-the-middle attacks, this article presents a dynamic and optimized routing approach for VANET conversation in smart cities by utilizing a chaotic secure multi-verse optimization algorithm. The strategy that has been proposed seeks to achieve the goal of ensuring safe and effective interaction between vehicles participating in VANETs by dynamically determining the optimal path for the exchange of data. A chaotic protect multi-verse optimization approach is used to generate several random sequences from which the most secure route may be selected. This is done to enhance the security of the VANET transmission network during transmission. The results of the trials indicate that the suggested technique is more successful in avoiding MITM and improving the functioning of VANET connections in settings that are characterized by intelligent cities. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) play a critical role in ensuring safety and welfare applications for drivers and passengers amidst the escalating vehicular population in urban environments. The efficient functioning of VANETs hinges on addressing the challenge of load balancing among Road Side Units (RSUs). This paper introduces a groundbreaking approach aimed at enhancing real-time data transmission services within VANETs. The key contribution lies in the development of a multicast routing algorithm utilizing a geo-targeting protocol, facilitating simultaneous delivery of source data packets to multiple destinations. This innovative strategy aims to alleviate RSU congestion, thereby significantly enhancing the quality of real-time data transmission services. Moreover, this study presents advancements in the Statistical Match and Queuing algorithm, refining it over time to substantially mitigate network congestion and redundancy. Additionally, a Multi-Protocol Label Switching based algorithm is implemented to elevate service quality parameters, including end-to-end latency, packet loss, and overall network efficiency within in-vehicle networks. Importantly, this approach remains adaptable across various Layer two technologies, ensuring compatibility and scalability. Simulation results validate the efficacy of the proposed methodology, showcasing its superiority over existing methods. The findings underscore the innovative algorithms' prowess in addressing load balancing challenges across diverse scenarios, affirming their potential to significantly enhance VANET service quality. [ABSTRACT FROM AUTHOR]

Vehicle ad hoc networks (VANETs) have garnered considerable attention for their potential to enhance road safety and facilitate advanced driver assistance systems. A fundamental aspect of VANET is the formation of stable clusters and cluster heads (CH) for improved network performance. Due to the dynamic nature of VANET and the different mobility of the vehicles, maintaining CH stability is significant. To address this issue, this study presents the network as connecting hypergraphs. The proposed approach uses an improved tensor-trace maximization-based spectral clustering algorithm (iTTM) and eigen heuristics to generate an optimal. The suggested clustering approach is followed by the CH selection based upon the four vehicle attributes: modularized link lifetime, connectivity level, relative speed, and consensus trust score. The multi-decision CRITIC approach will decide the CH in each cluster using those four attributes. These metrics have improved throughput and reduced packet delay to improve network performance. Simulation results, conducted in Delhi's Connaught Place using SUMO, demonstrate the proposed method's superiority in CH stability, throughput, and packet delay compared to existing algorithms. The state-of-the-art comparison is done on the criteria of CH stability, which comes out to be 84% in the proposed case and 82% in the previous work. The evaluation is done with recently published research on the CH rate of change and to validate the lower switching frequency in the network. The network is evaluated with different average speeds of the vehicles, and superior performance is noted in the proposed approach with the increase in lesser switching frequency with higher average speed than state-of-the-art. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANET) facilitate vehicle to everything (V2X) communication between vehicles and road side units (RSU) to exchange safety and alert messages required for the successful implementation of VANET applications. However, VANET is exposed to Sybil attacks, where malicious vehicles generate multiple false identities to seize control of network resources or create non-existent traffic situations. While several Sybil attack detection mechanisms based on deterministic and learning methods exist, their performance is restricted to particular scenarios. This work proposes a deep learning-based Sybil Attack Detection mechanism. It identifies the Sybil nodes by detecting the associativity between senders at a time instant using common vehicle characteristics, including Euclidean distance, speed, flow, and computed similarity between senders by using the dynamic time warping (DTW) method. Among the five applied deep learning models, the proposed solution achieves improved detection performance through convolutional neural network (CNN) and a combination of CNN with long short-term memory (LSTM) models in varying network scenarios. [ABSTRACT FROM AUTHOR]

VANET routing has become a very popular area of research as it lays the foundation for the intelligent transportation system (ITS). In VANET, the mobility of the vehicle is high hence the network is dynamic. Therefore, the connectivity between the two vehicles and the roadside unit (RSU) keeps changing, increasing the links dynamics and affecting the network quality of Service (QoS). To improve the VANETs’ QoS in dynamic scenarios, an efficient routing protocol is designed and implemented. Here, VANET-generated packets are routed to the destination through the Onboard unit (OBU) of vehicles and roadside units (RSU). The simulation results using MATLAB 2022a demonstrate that end-to-end delay is 0.106s and packet lost ratio is 5.91 for the proposed algorithm indicating our proposed algorithm performs better than the protocols like EERM, MAODV, and SDIoV. Simulation results show that the proposed protocol outperforms over previous algorithms in terms of QoS metrics e.g., packet delivery ratio (PDR), normalized routing load (NRL), energy usage (EU), and throughput. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) make it possible for applications such as autonomous driving, active safety systems and smart cities. As the infrastructure of ITSs, vehicular ad-hoc network (VANET) plays a key role in ensuring traffic safety while improving driving experience and comfort. However, VANET faces many challenges as the network is exposed to the public and involves sensitive information such as vehicle control commands and driving records. Although blockchain technology can provide such features as decentralization, immutability, non-reliance on trust and traceability, most of the existing blockchain systems impose high storage and computing capacity requirements for participating nodes while terminals nodes in VANET, such as road-side units (RSUs), on-board units (OBUs) and sensors, usually have very limited storage and computing capacity. To solve this problem, this paper proposes a blockchain system that provides scalable storage capacity for VANETs. The proposed scheme uses network sharding and multi-consensus strategy to improve the topology and consensus process of the blockchain. Aimed at lowering the storage requirement on the RSU nodes that participate in the blockchain, a collaborative storage mechanism and a dynamic copy number strategy for the blockchain ledger is designed. Experimental results show that compared to existing schemes, the proposed scalable blockchain storage scheme can significantly lower the storage requirement for the blockchain nodes, thus making it possible for the nodes with limited storage capacity in VANET to participate in the maintenance of the blockchain to prevent the blockchain from drifting towards centralization, and support maximal sharing of road traffic information while ensuring the security, privacy and trustworthiness of information. [ABSTRACT FROM AUTHOR]

The increasing development and implementation of Intelligent Transportation System have led to a growing focus on traffic flow prediction. To make these predictions, a large amount of historical and real-time traffic data is collected from various sources such as sensors, cameras, GPS and social media. Predicting traffic flow helps reduce congestion and promotes safer and more cost-effective travel. However, traditional models that use basic networks are not effective in today's scenarios due to the exponential increase in the number of vehicles. Federated Learning provides an opportunity to use learning techniques while addressing privacy concerns when data is spread across different devices or entities. This research explores the use of Federated Learning combined with deep learning methods to analyze and predict traffic flow based on available historical data. Different Federated aggregation techniques and deep learning models are also compared in this work. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Making (MCDM) approaches have been developed to support a wider range of application areas. For example, channel allocation, among the vehicles in infrastructure-based/hybrid Vehicular Ad hoc NETworks (VANETs), is done by exploiting MCDMs to minimize the waiting time and maximize the request service rate by the Road Side Unit (RSU). The Analytic Hierarchy Process (AHP) developed by Saaty, however, has gained enormous popularity and been used all over the world since its invention in 1977. AHP is capable of prior weight calculation of individual criteria using Criteria Preference Matrix (CPM) and rank computation of alternatives as well. Another MCDM technique, termed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was created by Hwang and Yoon in 1981. It is a ranking technique that requires inputs of the weights of the criteria. This paper aims to analyze the impact on ranks of various alternatives for different CPMs using the AHP-AHP method and the AHP-TOPSIS method. For both cases weights of criteria have been calculated using AHP before rank computation. In addition to that, this paper deals with the performance evaluation of infrastructure-based VANETs for priority-based channel allocation using the AHP-AHP and the AHP-TOPSIS method in terms of Request Service Rate and Average Waiting Time for various CPMs. It is observed that AHP-TOPSIS performs better in terms of Request Service Rate with the increasing alternatives. Furthermore, the AHP-AHP method shows frequent rank change while increasing the number of alternatives for various CPMs. [ABSTRACT FROM AUTHOR]

The Vehicular Ad Hoc Network (VANET) is a novel and innovative technology which is part of the Intelligent Transportation Systems (ITS). VANET is a network composed of a collection of vehicles and other roadside components that are interconnected wirelessly. The intention underlying the development of this technology is the improvement of the vehicle environment and the enhancement of vehicle and driver safety. Nevertheless, since VANETs operate wirelessly and under complicated conditions, they are susceptible to a variety of attacks by malicious actors. Traditional techniques such as encryption are no longer effective, so new techniques using intrusion detection systems IDS have attracted the attention of a large number of researchers. The IDS scans the entire network and identifies all the possible harmful nodes present in the network. The present paper covers the problem of the identification of attacks in VANET by using XGBoost. The effectiveness analysis of the proposed models has been realized on the NSL-KDD and 5RoutingMetrics datasets combined with various feature selection techniques Boruta and Adaptive Synthetic Sampling Approach (ADASYN). Furthermore, the acquired results are being compared to two of the last most used ensemble methods CatBoost and convolutional neural networks CNN.In comparison with the other IDSs, our model approach achieves high performance in accuracy, recall and precision. [ABSTRACT FROM AUTHOR]

Recent advances in smart connected vehicles and intelligent transportation systems are based on the collection and processing of massive amounts of sensor data. There are various internal sensors integrated into modern vehicles that are useful to monitor multiple mechanical and electrical systems, and the shift to semi-autonomous vehicles adds outward-facing sensors such as cameras, lidar, and radar. Low-cost, dependable sensing, connectivity, computational capacity, and powerful analytics are ushering in a new era of vehicle context sensing and vehicular network (VANET). However, due to latency, bandwidth, cost, security, and privacy issues, as well as the growing capabilities of edge computing devices, it is necessary to examine both edge and cloud computing in order to make informed judgments based on their contexts and performances. So, in this paper, we have proposed an architecture that focuses on an intelligent transportation system based on both Cloud and Edge computing in order to monitor and analyze vehicle sensor data and its environment; known as monitoring and analyzing as a service. Our proposed architecture is able to react in real-time to any vehicle context changes in a dynamic, adaptive, and autonomous way. It generates many plans adapted to the vehicle context that will be useful for future research to achieve a self-learning and self-adaptive system. This suggested architecture helped us improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. Also, we have reduced latency and increased bandwidth. Finally, we have approved the performance of our architecture based on different evaluation metrics. [ABSTRACT FROM AUTHOR]

The vehicular ad-hoc network is a technology that enables vehicles to interact with each other and the surrounding infrastructure, aiming to enhance road safety and driver comfort. However, it is susceptible to various security attacks. Among these attacks, the position falsification attack is regarded as one of the most serious, in which the malicious nodes tamper with their transmitted location. Thus, developing effective misbehavior detection schemes capable of detecting such attacks is crucial. Many of these schemes employ machine learning techniques to detect misbehavior based on the features of the exchanged messages. However, the studies that identify the impact of feature engineering on schemes' performance and highlight the most efficient features and algorithms are limited. This paper conducts a comprehensive literature survey to identify the key features and algorithms used in the literature that lead to the best-performing models. Then, a comparative study using the VeReMi dataset, which is publicly available, is performed to assess six models implemented using three different machine learning algorithms and two feature sets: one comprising selected and derived features and the other including all message features. The findings show that two of the suggested models that employ feature engineering perform almost equally to existing studies in identifying two types of position falsification attacks while exhibiting performance improvements in detecting other types. Furthermore, the results of evaluating the proposed models using another simulation exhibit a substantial improvement achieved by employing feature engineering techniques, where the average accuracy of the models is increased by 6.31–47%, depending on the algorithm used. [ABSTRACT FROM AUTHOR]

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network's backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays. [ABSTRACT FROM AUTHOR]

In urban Vehicular Ad-hoc Networks (VANETs), disseminating traffic data efficiently is challenging due to the dynamic and complex nature of the network. Multi-hop-based broadcasting approaches are commonly used to address this issue. However, selecting the optimal relay nodes poses a challenge and directly impacts network performance. Existing relay selection strategies, such as beacon-based methods, have limitations in scaling under different traffic conditions. This paper proposes a multi-attribute relay selection strategy for urban multi-hop VANETs to overcome these challenges. The strategy evaluates the rebroadcast capability of each receiving node based on its real-time status. It utilizes a fuzzy-BCM-based weight estimation strategy to determine the contribution of each attribute to the node's capability. The node with the highest broadcasting capability is given priority to access the channel and broadcast the data packet. The proposed scheme is evaluated through simulation tests in VANET simulation environments, considering various traffic flow and speed variations. Performance comparison with four benchmark methods is conducted. The results show that the proposed scheme improves the overall dissemination efficiency by 51.1% compared to the benchmarked methods. [ABSTRACT FROM AUTHOR]

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In terms of applications and research, VANET (Vehicular Ad-hoc Networks) communication is becoming more popular. Existing VANET communication protocols try to improve network performance but fail to consider security issues. Attackers exploit the vulnerabilities of VANET communication protocols. Providing security to the VANET is still a challenging task because of the vehicles' mobility and their short communication range. Based on our study, we found that wormhole attacks are the most common type of attack on VANET communication. The existing security solutions are inadequate to prevent or detect external wormhole attacks on VANET communication because these solutions do not consider important parameters such as vehicle mobility, neighboring ratio, and node mobility to detect the wormhole attack. To address external wormhole attacks in VANETs, we propose a two-level fast and efficient distance based external wormhole detection and prevention system in this paper. In our proposed solution, we consider the vehicles' mobility, geographical location, and distance parameters to identify and isolate external wormhole attacker nodes. For effective monitoring of wormhole attacks, we use a dynamic threshold value for suspecting external wormhole attack links and then use the hop count metric to detect and prevent it. We used SUMO with NS2 simulators to compare our proposed system with existing works, and our simulations show that our proposed security solution outperforms existing works in terms of throughput, PDR, and jitter in a wormhole attack VANET environment. [ABSTRACT FROM AUTHOR]

The reliability of the communication link is quite common and challenging to handle as the topology changes frequently in vehicular ad hoc networks (VANETs). Another problem with VANETs is that the vehicles are from different manufacturers. Hence, the heterogeneity of hardware is obvious. These heterogeneity and reliability problems affect the message dissemination in VANETs. This paper aims to address these challenges by proposing a robust routing protocol capable of ensuring reliable, scalable, and heterogeneity-tolerant message dissemination in VANETs. We first introduced a hybrid hierarchical architecture based on software-defined networking (SDN) principles for VANETs, leveraging SDN's inherent scalability and adaptability to heterogeneity. Further, a road-side unit (RSU)-assisted cloud-based location-aware hybrid routing for software-defined VANETs (SD-VANETs) that we call RC-LAHR was proposed. RC-LAHR was rigorously tested and analyzed for its performance in terms of packet delivery ratio (PDR) and end-to-end delay (EED), along with a comprehensive assessment of network traffic and load impacts on cloud infrastructure and RSUs. The routing protocol is compared with state-of-the-art protocols, Greedy Perimeter Stateless Routing (GPSR) and Opportunistic and Position-Based Routing (OPBR). The proposed routing protocol performs well as compared to GPSR and OPBR. The result shows that the EED is reduced to 20% and the PDR is increased to 30%. The network reliability is also increased up to 5% as compared to the OPBR and GPSR. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) establish the connection between the vehicles through wireless communication. This link stability is significant for achieving traffic safety and traffic efficiencies. But sharing security messages in the mobile environment is very complex because of the excessive speed of the vehicles, unsteady communication links, and geographically constrained topologies. A Roadside Unit is vital for vehicle connection with the transportation center. We proposed Tri-Parametric FUZZY logic (TP-FUZZY) for establishing the stable connection in VANET. The proposed Tri-Parametric FUZZY Logic effectively addresses the complexity of the vehicles' excessive speed, etc. We have considered the combination of normal-speed vehicles containing a high data rate and the shortest distance for forming the clusters. The cluster head (CH) concept is implemented to monitor and control the entire mechanism. The FUZZY Logic executes the automatic selection of the next CH if the current CH triggers exit from the cluster. It ensures the overall cluster mechanisms stability. The proposed simulation results prove that Tri-Parametric FUZZY logic performance in the aspect of Packet delivery ratio, Dynamic cluster head changes, Routing overhead, End to end delay and Energy consumption is more effective than the other existing mechanisms. Thus, the proposed TP-FUZZY is a prominent solution for establishing reliable link connectivity in the VANET. [ABSTRACT FROM AUTHOR]

Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution's simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol's significance with enhanced quality of service performance. [ABSTRACT FROM AUTHOR]

Connected vehicles (CVs) are an emerging technology in intelligent transportation systems. Currently, many data-driven intelligent transportation systems (D2ITS) use CV data. Unfortunately, these D2ITS still need serious improvement before they meet higher-level visualization needs. Thus, we aim to develop a new, intelligent data-driven transportation system framework. We focus on visualizing real-time CV data using a big data analytic system in urban areas. In response, we first propose an effective real-time data distribution approach within the Vehicular Ad-hoc NETwork. Second, we develop novel strategies for aggregating, extracting and ingesting data. We provide scalable and fault-tolerant delivery methods without interruption or delay. Finally, we proposed a novel visualization REpresentational State Transfer (REST) web service. We used Simulation of Urban MObility, OMNET++ and Veins to simulate a traffic incident dataset. Then, we tested the Basic Safety Messages in an experimental big data cluster. We used NIFI, Kafka and Cassandra for ingestion, distribution, delivery and storage. The results show accurate performance for packet loss, packet delivery and communication delay. Also, it indicates high throughput and low latency for distributed data delivery systems. Additionally, we obtained the smallest response time for the RESTFUL visualization web service. [ABSTRACT FROM AUTHOR]

Summary: In recent days, VANET is considered as the main hopeful equipment in the system of transportation since traffic congestion arises regularly and thus occurred road accidents very easily. Besides, the network traffic is increasing due to the huge quantity of information generated in region of urban. Therefore, one of the primary challenges faced by Intelligent Transportation System (ITS) is ensuring the accurate transmission of information in both vehicle‐to‐vehicle (V2V) and vehicle‐to‐road (V2R) sensing unit communications. So, here, an adaptive routing controller (ARC) is utilized to improve the data transmission between vehicle and road sensing unit (RSU) and to minimize the traffic density in VANET, and the self‐adaptive search optimization is developed in VANET clustering to prioritize vehicles in the lane, in which the multi‐objective function is intended depending on the energy of the node, acceleration, jitter, priority, velocity, and trust factors. The traffic light control is done to facilitate the effective communication in the network. Thus, the proposed technique is calculated in terms of throughput, jitter, quadratic mean of acceleration (QMA), and spatially distributed travel time (SDTT), which acquired the values of 0.53 s, 36.07 kmph, 3.472 s, and 43.572%, respectively, while using 50 vehicles at 50 s. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) plays a vital role in emerging Intelligent Transportation System (ITS). VANET's deal with the group of network that are designed in ad-hoc model in which various moving devices and other stationary linking objects that are connected over a wireless manner which interchange effective data to each other. Since, the communication between the VANET and other communication devices take place in open channel, there exist many threats caused by the intruder. Moreover, most of the existing authentication schemes suffer from computational and communicational overhead. In order to provide efficient lightweight authentication with enhanced privacy. In this paper, a novel lightweight authentication protocol named as Lightweight Authentication Protocol with Enhanced Privacy (LAPEP) has been proposed to provide efficient end to end security in VANET. The proposed protocol employs Elliptic Curve Cryptography (ECC) along with Bilinear pairing for efficient generation of keys and provides secured multiparty key exchange. Moreover, the security analysis of the proposed scheme is carried out by Burrows Abadi Needham (BAN) logic. The advantages of the proposed system are reduction of computation overhead and provide resistance to various security attacks during data transmission. Moreover, the proposed system enhances the Quality of Service (QoS) namely latency, overhead, delivery ratio, loss ratio and delay rate. The implementation of the proposed protocol is carried out in NS3 simulator. The simulation result proves that the proposed protocol improves the computational cost by 42.85%, communication cost by 35.82%, message end to end delay by 30.65%, signing time by 25.45% and verification time by 32.45%. [ABSTRACT FROM AUTHOR]

In recent years, the popularity of vehicle adhoc networks (VANET) in wireless intelligent transportation systems has significantly increased. It is difficult to determine the quickest path between the source and the target in a VANET traffic system. Longer routes feature increased network overhead, more expensive connections, more path failures, and worse routing efficiency. Identifying the shortest route in optimization, often known as the Travelling Salesman Problem (TSP) and traffic congestion, is a well-known combinatorial optimization challenge with various practical applications. To improve routing efficiency, the HACOSMO (Hybrid-Ant Colony Optimization with Spider Monkey Optimization) system's recommended meta-heuristic approach finds the shortest path using distance and traffic based principles. The simulation (NS-2) outcomes focused on the efficiency of the proposed method beats the other existing methods like ACO, GRACO, OACO, and IDBACOR in terms of overhead, throughput, latency, packet failure, and message transmission ratio. According to HACOSMO, routing overhead is 8% to 11% under IDBACOR, 10% to 14% under GRACO,19% to 31% under OACO, and 24% to 37% under ACO for a variety of vehicle counts and speed ranges. When compared to IDBACOR, the proposed method enhances throughput by 3% to 7%, 6% to 8% when compared to GRACO, 11% to 15% when compared to OACO, and 16% to 25% when compared to ACO. [ABSTRACT FROM AUTHOR]

Effective message dissemination in vehicular ad hoc networks requires proper management of broadcast storms for effective bandwidth usage, fast and reliable delivery of messages. Studies suggest clustering of vehicles as a means of solving broadcast storm problems due to reduction of nodes that rebroadcast emergency messages. However, clustering in an urban environment faces challenges in terms of bandwidth utilization and high End-to-End (E2E) delay due to high density of vehicles, and the number of intersections and buildings. Several cluster-based dissemination schemes have been proposed however, their model evaluations lack typical urban features, like intersections and buildings, which can adversely affect their actual performances. Thus, it is still not clear which scheme is truly the best for real-world urban scenarios. To address this challenge, a fair and thorough performance evaluation of the state-of-the-art schemes in a detailed urban setup, specifically at crossroads with buildings nearby, is urgently needed. Results deduce that, in terms of E2E delay, the Time-Barrier Emergency Message Dissemination Scheme (TBEMDS) outperforms Effective Emergency Message Dissemination Scheme (EEMDS) and Position-based Emergency Message Dissemination Scheme (PBEMDS) by 75% and 50% respectively at low traffic density. However, it is outperformed by PBEMDS by around 9% in high density. For Packet Delivery Ratio (PDR), from low to high density, EEMDS has the least percentage decrease in PDR with a 10% and 40% higher than TBEMDS and PBEMDS respectively, making it the most robust scheme in maintaining PDR as traffic density. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) were created more than eighteen years ago with the aim of reducing accidents on public roads and saving lives. Achieving this goal depends on VANET mobiles exchanging Road State Information (RSI) with their surroundings and acting on the received RSI. Therefore, it is essential to ensure that transmitted messages are accurately received. This requires controlling the quality of the sharing medium or link while considering Channel State Information (CSI), which provides information on channel quality and Signal-to-Noise Ratio (SNR). The process of adjusting the payload based on the CSI is known as Link Adaptation (LA). While several LA works have been published in VANETs, few have considered the effect of relative node mobility. This work presents a link adaptation strategy for VANETs that uses a Neural Network (NN) and the Levenberg-Marquardt Algorithm (LMA). While accounting for the Doppler Shift effect induced by the relative velocity, the simulation results demonstrate that the NN approach outperforms its peers by 77%, 115% and 853% in terms of transmitted errors, model efficiency, and throughput respectively, compared to the Cte, ARF, and AMC algorithms. [ABSTRACT FROM AUTHOR]

The development of 5G communication, big data technology and intelligent transportation system has driven the rapid development of vehicle network (VANET). However, with the rapid development of vehicle networks, it will be invaded by various security problems, such as privacy disclosure, session key security, forward security, etc. To eliminate the security threats faced by the vehicle network and ensure privacy and secure communication between vehicles and roadside units(RSU), it is necessary to introduce a secure, reliable, and efficient authentication key agreement scheme. Therefore, based on the principle of challenge authentication handshake protocol, we propose a novel security authentication and key agreement scheme for the vehicle networks. The scheme meets mutual authentication, and is the session key of communication protocol between vehicle and roadside unit to resist common attacks. In addition, compared with other schemes, our scheme has the advantage that we can flexibly set time keys to constrain vehicle behaviour and achieve dynamic vehicle management. We also give a formal security proof of the scheme under the random oracle model (ROM). Finally, the efficiency of the protocol is evaluated, and the computing and communication overhead of our scheme is about 35% lower than that of the existing scheme. So, our scheme is more practical. [ABSTRACT FROM AUTHOR]

With the growth in Vehicular Ad Hoc Network (VANET) technology, many vehicular devices are communicating with each other and with the edge nodes, generating a massive amount of data. One of the biggest challenges is to preserve users' privacy as the data hold personal and sensitive information, which upon leakage could have disastrous consequences. Privacy preservation has gained remarkable consideration by companies as a notable number of users have started being conscious about privacy protection of their data. Most privacy preserving solutions that have been developed in such a distributed scenario need a third party for data anonymization. In a system of public data sharing, one of the most popular and useful anonymization techniques is Local Differential Privacy (LDP). LDP allows users to anonymize their data locally and individually and does not need a third party for data anonymization, resulting in stronger privacy guarantees. In this work, firstly, considering the security and privacy threats posed by untrusted third parties, namely edge nodes or roadside units (RSUs), we provide a privacy preservation solution for VANETs using LDP, eliminating the need for a third party to anonymize sensitive vehicular data. Secondly, to provide a tier 2 privacy and security, we introduce a model that uses IOTA ledger on top of the LDP perturbation technique. Consequently, not only does our model achieve privacy, but tier 2 privacy preservation method based on IOTA ledger provides immutability, scalability, and quantum secrecy over a largely complex and distributed network of vehicles. [ABSTRACT FROM AUTHOR]

One of the network performance challenges of Vehicular Ad Hoc Networks (VANET) is the Black Hole attack. This destructive attack severely damages the network performance if succeeded. By replying to a route request with the minimum routing cost and the maximum sequence number (SN) value, the malicious vehicle can fool the source vehicle that it has the "freshest" and best cost-effective route to the destination vehicle. As a result, all data packets intended to a legitimate destination are caught and destroyed by the malicious vehicle. This may also cause a serious risk for autonomous vehicle control system or traffic warning applications. Previous published malicious detection algorithms suffer high error rates when the malicious node uses a "close-to-legitimate" SN value to attack or actively change the information in the route reply packet. This paper proposes a Black Hole Attack Detection Algorithm (BADA) based on a machine learning approach. In VANETs, it is a challenge to differentiate the behavior of malicious vehicles as they try to avoid detection by imitating that of the normal vehicles. The BADA classification algorithm outperforms existing solutions because it relies on the history of each vehicle's route request and response behavior, and it deploys the k-Nearest Neighbors machine learning algorithm to identify malicious vehicles. The paper also proposes a Black hole Attack Detection Routing Protocol which utilizes the proposed BADA solution to realize a more secured and improved AODV-based protocol. Using the NS2 simulation system, the paper evaluates the performance of the proposed protocol and compares it with related solutions on the traffic network model in Ho Chi Minh city, Vietnam under black hole attacks using minimum SN values. Simulation results have shown that the proposed algorithm can correctly detect the malicious node higher than 99.0%, outperforming previously published algorithms. [ABSTRACT FROM AUTHOR]

Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model's performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Network (VANET) Technology is advancing due to the convergence of VANET and cloud computing technologies, Vehicular Ad-Hoc Network (VANET) entities can benefit from the cloud service provider’s favourable storage and computing capabilities. Cloud computing, the processing and storage capabilities provided by various cloud service providers, would be available to all VANET enterprises. Digital Twin helps in creating a digital view of the Vehicle. It focuses on the physical behaviour of the Vehicle as well as the software it alerts when it finds issues with the performance. The representation of the Vehicle is created using intelligent sensors, which are in OBU of VANET that help collect info from the product. The author introduces the Cloud-based three-layer key management for VANET in this study. Because VANET connections can abruptly change, critical negotiation verification must be completed quickly and with minimal bandwidth. When the Vehicles are in movement, we confront the difficulty in timely methods, network stability, and routing concerns like reliability and scalability. We must additionally address issues such as fair network access, inappropriate behaviour identification, cancellation, the authentication process, confidentiality, and vehicle trustworthiness verification. The proposed All-Wheel Control (AWC) method in this study may improve the safety and efficiency of VANETs. This technology would also benefit future intelligent transportation systems. The Rivest–Shamir–Adleman (RSA) algorithm and Chinese Remainder Theorem algorithms generate keys at the group, subgroup, and node levels. The proposed method produces better results than the previous methods. [ABSTRACT FROM AUTHOR]

In this research work, a novel Intrusion Detection and Mitigation System (IDMS) is modeled based on the optimization assisted deep learning technique. The two major phases are: feature extraction, attack detection and mitigation. In the feature extraction phases, the features related to the traffic flow and the vehicle position gets extracted. Then, these extracted features are subjected to attack detection phase, where the weight optimized Deep Neural Network detects the presence/absence of attack in the network. For the tuning purpose, a new Improved Particle Swarm Optimization (IPSO) algorithm is introduced in this work. The presented method overcomes the traditional PSO's drawbacks, such as easily falling into local optima, and improves its performance. Once the attack behavior is identified, it is very important to mitigate the attacker from the network. For this, BAIT based mitigation process is used in this work. Finally, the performance of the proposed IDMS is evaluated over the extant techniques in terms of certain performance measures. The proposed work's UR is 13% better than the current SVM, 9% better than PSO + DNN, 6.6% better than NB, 8.6% better than I-GHSOM, and 16.6% better than SLnO + DNN. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) have gained significant attention in the past decade for their potential to support intelligent transportation systems and entertainment facilities in smart cities. However, VANETs face significant challenges, such as intermittent connectivity and poor signal strength caused by abstraction from buildings and poles. The excessive packet drop rate results in re-transmissions which negatively affect the end-to-end delay. Diverse ranges of vehicle mobility increase the routing overhead and make the VANET an unscalable network. The severity of these challenges becomes even more intensified when deployed in an urban area and limits the widespread adoption of VANETs. The core aim of this study is to investigate the potential of integrating micro-UAVs with VANETs that can be used for bandwidth hungry as well as delay sensitive applications of intelligent transportation systems. The proposed model leverages the line-of-sight communication capabilities of UAVs to facilitate communication between vehicles and infrastructure. We demonstrated the effectiveness of our approach in high-density urban areas, where traditional VANET struggles to perform. Experiment results show that the proposed model achieves the probability of line-of-sight approaches to 1. This helps to provide a reliable link and has better receive signal strength which results in higher throughput for entertainment applications and short message delay for safety and interactive applications. [ABSTRACT FROM AUTHOR]

The Vehicular Self-Organizing Network (VANET) is a burgeoning research topic within Intelligent Transportation Systems, holding promise in enhancing safety and convenience for drivers. In general, VANETs require large amounts of data to be shared among vehicles within the network. But then two challenges arise. First, data security, privacy, and reliability need to be ensured. Second, data management and security solutions must be very scalable, because current and future transportation systems are very dense. However, existing Vehicle-to-Vehicle solutions fall short of guaranteeing the veracity of crucial traffic and vehicle safety data and identifying and excluding malicious vehicles. The introduction of blockchain technology in VANETs seeks to address these issues. But blockchain-enabled solutions, such as the Starling system, are too computationally heavy to be scalable enough. Our proposed NeoStarling system focuses on proving a scalable and efficient secure and reliable obstacle mapping using blockchain. An opportunistic mutual authentication protocol, based on hash functions, is only triggered when vehicles travel a certain distance. Lightweight cryptography and an optimized message exchange enable an improved scalability. The evaluation results show that our collaborative approach reduces the frequency of authentications and increases system efficiency by 35%. In addition, scalability is improved by 50% compared to previous mechanisms. [ABSTRACT FROM AUTHOR]

Aim: The research aims at developing a traffic prediction and signal controlling model based on deep learning technique in order to provide congestion-free transportation in Intelligent Transport System (ITS). Need for the Research: Recent technical advancements in the ITS, industrialization, and urbanization increase traffic congestion, which leads to high fuel consumption and health issues. This signifies the need for a dynamic traffic management system to handle the traffic congestion issues that negatively affect the transportation service. Methods: For promoting congestion-free transportation in the ITS, this research aims to devise a traffic prediction and control system based on deep learning techniques that effectively controls the traffic during peak hours. The proposed mode-search optimization effectively clusters the vehicles based on the necessity. In addition, the mode-search optimization tunes the optimal hyperparameters of the deep Long Short Term Memory classifier, which minimizes the training loss. Further, the traffic signal control system is developed through the mode-search-based deep LSTM classifier for predicting the path of the vehicles by analyzing the attributes, such as velocity, acceleration, jitter, and priority of the vehicles. Result: The experimental results evaluate the efficacy of the traffic prediction model in terms of quadratic mean of acceleration (QMA), jitter, standard deviation of travel time (SDTT), and throughput, for which the values are found to be 37.43, 0.23, 8.75, and 100 respectively. Achievements: The proposed method attains the performance improvement of 5% to 42% when compared with the conventional methods. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc NETwork (VANET) safety applications allow vehicles to exchange messages with surrounding vehicles periodically to improve the contextual awareness of the drivers about the driving environment which significantly enhances traffic safety. However, these messages usually contain sensitive information such as the Spatio-temporal information of each vehicle which might be exploited by malicious entities for various purposes (e.g. monitoring the vehicle for a long period and breaching the driver's privacy). Researchers have proposed different schemes to enhance the privacy level of drivers and their vehicles alike. However, most of the existing schemes have a negative impact on safety applications; they stop broadcasting messages for a period which increases the chance of accidents. In this paper, we propose a Safety-related Privacy Scheme (SRPS) that enhances both the privacy and safety of VANET safety applications by reducing silent periods without degrading the privacy level. While the vehicle continues monitoring neighbour vehicles if an accident is expected, it exits the silent period and starts sharing its location with its neighbour vehicles. The SRPS consists of two algorithms based on the status of the vehicle (i.e. silent vs. active). These algorithms use a multi-target tracking algorithm to search for an effective context to change pseudonyms and avoid potential accidents. Four simulators are used to implement SRPS. The latter has been compared with five pseudonym-changing schemes (PPC, RSP, CSP, SLOW, and CAPS). The simulation results indicate that SRPS achieves an efficient balance between security, privacy, and safety when compared to the other schemes. [ABSTRACT FROM AUTHOR]