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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 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]

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]

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]

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]

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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]

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]

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]

In this study, we propose a novel approach to enhance the security and efficiency of Vehicular Ad-Hoc Networks (VANETs) by integrating user authentication and cluster-based routing. The proposed method is divided into two segments. The first segment focuses on user authentication using a curve fitting technique, implemented via MATLAB simulation. Nodes are randomly deployed with geostationary coordinates and node keys. These nodes are clustered based on their geographical locations, and their legitimacy is verified using curve fitting. This ensures that only authenticated nodes participate in the network, thereby enhancing security and reliability. The second segment employs a modified Ad hoc On-Demand Distance Vector (AODV) protocol for routing, adapted to the clustered network structure. Route Requests (RREQs) are sent to Zone Heads (ZH) for validation and then forwarded to Cluster Heads (CH), where idle and execution costs are calculated based on buffer states and execution capacities. The proposed method also incorporates fog computing to enable localized data processing, reducing latency and improving scalability. The performance of the proposed method was evaluated through extensive simulations, measuring key metrics such as throughput, Packet Delivery Ratio (PDR), and latency. Results show that the proposed method achieves a throughput of 8367.141811 packets per second, a PDR of 0.83336875, and a latency of 6.606503751 seconds, outperforming state-of-the-art algorithms by significant margins. Specifically, the proposed method demonstrates a 7.5% improvement in throughput over Khudhair et al. and a 12.9% improvement over Ahmad et al. In terms of PDR, it shows an 8.8% increase over Khudhair et al. and a 7.1% increase over Ahmad et al. The latency reduction compared to these algorithms is 9.1% and 10.5%, respectively. These enhancements are attributed to the efficient clustering and authentication mechanisms, along with the integration of fog computing. The proposed method thus provides a comprehensive solution for secure and efficient VANET communication, paving the way for advanced intelligent transportation systems. [ABSTRACT FROM AUTHOR]

A Vehicular Ad Hoc Network (VANET) is an emergent wireless technology that enables high-speed communication. It became attractive to automobile manufacturers because of the secure information transmission without fatal accidents. It has several unique features, such as data dissemination, frequently disconnected networks during transmission, dynamic network density, and dynamic topology, that differentiate VANETs. Data dissemination is vital because it ensures the safety and performance of the vehicle. Conversely, VANET has limitations in providing effective communication between the vehicles due to delays and frequent disruptions. Thus, data dissemination needs an effective routing and scheduling process to avoid collisions of vehicles. Based on this fact, we proposed a novel Authenticate Relay Selection and Scheduling Framework (ARSSF) for secure and quality data transmission in VANET. ARSSF is a novel VANET communication method that prioritizes trustworthy relay nodes and dynamic scheduling to increase network performance and safety as data dissemination needs rise. It used the SNR, utility, and relay selection schemes for choosing the relay nodes that can cover the capacity of the network, thus minimizing the data dissemination delay. Additionally, an authentication method was utilized during the relay transmission phase to ensure both data security and authorization. The proposed framework performance was assessed by NS2 simulation. The results demonstrate that ARSSF is beneficial. ARSSF's minimum delay of 47 ms, throughput of 98.17%, and security measures of 98.38% show its value in VANETs. It enhances VANET safety, efficiency, and data distribution and could serve as a high-end vehicular network research platform. [ABSTRACT FROM AUTHOR]

Routing in vehicular ad hoc networks (VANETs) enables vehicles to communicate for safety and non-safety applications. However, there are limitations in wireless communication that can degrade VANET performance, so it is crucial to optimize the operation of routing protocols to address this. Various routing protocols employed the expected transmission count (ETX) in their operation as one way to achieve the required efficiency and robustness. ETX is used to estimate link quality for improved route selection. While some studies have evaluated the utilization of ETX in specific protocols, they lack a comprehensive analysis across protocols under varied network conditions. This research provides a comprehensive comparative evaluation of ETX-based routing protocols for VANETs using the nomadic community mobility model. It covers a foundational routing protocol, ad hoc on-demand distance vector (AODV), as well as newer variants that utilize ETX, lightweight ETX (LETX), and power-based light reverse ETX (PLR-ETX), which are referred to herein as AODV-ETX, AODV-LETX, and AODV-PLR, respectively. The protocols are thoroughly analyzed via ns-3 simulations under different traffic and mobility scenarios. Our evaluation model considers five performance parameters including throughput, routing overhead, end-to-end delay, packet loss, and underutilization ratio. The analysis provides insight into designing robust and adaptive ETX routing for VANET to better serve emerging intelligent transportation system applications through a better understanding of protocol performance under different network conditions. The key findings show that ETX-optimized routing can provide significant performance enhancements in terms of end-to-end delay, throughput, routing overhead, packet loss and underutilization ratio. The extensive simulations demonstrated that AODV-PLR outperforms its counterparts AODV-ETX and AODV-LETX and the foundational AODV routing protocol across the performance metrics. [ABSTRACT FROM AUTHOR]

In the emerging world the Vehicular Ad-hoc Network (VANET) plays a vital role in the Intelligent Transportation Sector (ITS). Basically VANET are exposed rapid topology changes, dynamic network environment, intermittent connectivity and fluctuating communication range which leads to security and privacy concerns. To address these issues, in this work, an enhanced pairing free certificate less aggregate authentication scheme is proposed. The proposed method employs Elliptic Curve Cryptography (ECC) instead of bilinear pairing algorithm to reduce the overhead and to provide efficient security in the network. Further, during the communication and authentication phases the message transmission takes place in an aggregate manner which in turn reduces the time and storage in the network. The novelty of the proposed work is that the use of aggregation scheme which reduces the delay during the communication in the network. The formal security analysis is provided to the proposed protocol by using the Random Oracle Model (ROM) to prove the level of security and privacy in the proposed system. The proposed system is compared with various existing system and has improved its performance metrics in terms of computational, communication cost, signing delay, verification delay and message end to end delay. The implementation of the proposed protocol is carried out in NS3 simulator. The simulator results also proves that the proposed protocol improves the transmission overhead by 42.28%, computational cost by 39.45%, communication cost by 49.75%, signing delay by 45.23% and verification delay by 44.35% when it is compared with other existing systems. [ABSTRACT FROM AUTHOR]

Nowadays, several service providers in urban areas significantly consider vehicular ad hoc networks (VANET). VANETs can enhance road safety, prevent accidents, and grant passengers entertainment. Though in VANET, efficient routing has remained an open problem. VANET is dynamic; the frequent update in the situation originates through several aspects, such as traffic conditions and updates in the road topology, which demand a suitably adaptive routing. The existence of blocking obstacles degrades routing approaches and increases the failure of paths. These issues build an excessive amount of resource utilization and increase network delay. To solve these issues, obstacle detection to minimize delay and Q-learning to improve routing efficiency (ODQI) in VANET is proposed. This mechanism uses the spanning tree algorithm detects the obstacle. Clustering can be used to manage the topology in VANETs. The dingo algorithm selects the best cluster head (CH) based on vehicle bandwidth, speed, and link lifespan. Furthermore, the sender forwards the traffic information from the sender to the receiver by applying a Q-learning algorithm. This learning algorithm computes the award function to choose the forwarder, improving the routing efficiency. Simulation results demonstrate that the ODQI mechanism increases the CH lifetime and minimizes the network delay. [ABSTRACT FROM AUTHOR]

With increased vehicular traffic on roadways, traffic jams and accidents are common, leading to substantial social and financial losses. Early detection of roadway accidents is beneficial as help can be directed to the accident site quickly, saving the lives of the injured. With this goal, an intelligent accident detection system is proposed in this paper using a Vehicular Adhoc Network model. Each vehicle has an on-board unit with multiple sensors in the proposed work. Outputs of these sensors, which monitor the condition of the driver and the vehicle, are input to a fuzzy logic controller for quick detection of an accident. The proposed system is validated by designing and implementing a hardware prototype. When an accident is detected, the proposed hardware uses a low-range radio (LoRa) transceiver network to send messages to a central server. After receiving the message from a vehicle, the server can inform a nearby ambulance. It can also identify the shortest path to a nearby hospital using Dijkstra's algorithm and inform the ambulance. This would facilitate timely treatment of the accident victim(s). [ABSTRACT FROM AUTHOR]

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.

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]

Recent developments in the fields of communications, smart transportation systems and computer systems have significantly expanded the potential for intelligent solutions in the domains of traffic safety, convenience and efficiency. The utilization of Artificial Intelligence (AI) is presently prevalent across diverse sectors of application due to its significant capacity to augment conventional data-driven methodologies. In the domain of Vehicular Ad hoc NETworks (VANETs), data is regularly gathered from several sources. The collected data serves multiple goals, such as facilitating efficient routing, enhancing driver awareness, forecasting mobility patterns to prevent potential risks, and ultimately enhancing passenger comfort, safety and overall road experience. Internet of thing (IoT) can be a good solution fro many issues that of VANETs. In order to make a complete system of VANETs communications are very essential like 5G and 6G. This study provides a detailed examination of AI, IoT and 5G/6G methodologies currently being investigated by multiple research endeavors in the field of VANETs. The merits and demerits of the AI-based methodologies suggested for the VANET domain have been analyzed with the using of IoT and 5G/6G. In conclusion, forthcoming research prospects in the field of VANETs are ascertained.

Mobile ad hoc networks (MANETs), which include vehicular ad hoc networks (VANETs), are a promising method for intelligent transportation systems. The major challenges of VANET protocols are symmetric links and changing topology of the networks and hence a proper routing method is necessary for VANET. A crucial issue with VANETs is the transmission of data from a starting point to the base station. To discover the least path with the lowest cost and fewest hops between the source and the destination, existing work uses a novel priority-based direction-aware collision avoidance (P-DVCA). To direct the ants to develop a shorter path with fewer hops for data transmission, the suggested method uses the prioritising and scheduling enhanced congestion control-based source ant colony optimization (ECC-ACO) for VANETs. Based on the message type and network situation, this technique provides precedence for privacy messages communications. Privacy messages can also be divided into beacon messages and occurrence messages. At that point, each message is routed into the appropriate queue predicated on its primary consideration, and each message in each queue is planned. When compared to renowned VANET communication architecture, simulation and analytical findings show that proposed ECC-ACO offers a respectable reduction in collisions.

This paper addresses the vulnerability of vehicular ad hoc networks (VANETs) to malicious attacks, specifically focusing on position falsification attacks. Detecting misbehaving vehicles in VANETs is challenging due to the dynamic nature of the network topology and vehicle mobility. The paper considers five types (constant attack, constant offset attack, random attack, random offset attack, and eventually stop attack) of position falsification attacks with varying traffic and attack densities, considered the most severe attacks in VANETs. To improve the detection of these attacks, a novel travel distance feature and an enhanced two-stage detection approach are proposed for classifying position falsification attacks in VANETs. The approach involves deploying the misbehavior detection system within roadside units (RSUs) by offloading computational work from vehicles (onboard units, or OBUs) to RSUs. The performance of the proposed approach was evaluated against different classifiers, including a wide range of paradigms (KNN, Decision Tree, and Random Forest), using the VeReMi dataset. Experimental results indicate that the proposed method based on Random Forest achieved an accuracy of 99.9% and an F1-Score of 99.9%, which are better not only than those achieved by KNN and Decision Tree but also than the most recent approaches in the literature survey.

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]

Mobile ad hoc networks (MANETs), which include vehicular ad hoc networks (VANETs), are a promising method for intelligent transportation systems. The major challenges of VANET protocols are symmetric links and changing topology of the networks and hence a proper routing method is necessary for VANET. A crucial issue with VANETs is the transmission of data from a starting point to the base station. To discover the least path with the lowest cost and fewest hops between the source and the destination, existing work uses a novel priority-based directionaware collision avoidance (P-DVCA). To direct the ants to develop a shorter path with fewer hops for data transmission, the suggested method uses the prioritising and scheduling enhanced congestion control-based source ant colony optimization (ECC-ACO) for VANETs. Based on the message type and network situation, this technique provides precedence for privacy messages communications. Privacy messages can also be divided into beacon messages and occurrence messages. At that point, each message is routed into the appropriate queue predicated on its primary consideration, and each message in each queue is planned. When compared to renowned VANET communication architecture, simulation and analytical findings show that proposed ECC-ACO offers a respectable reduction in collisions. [ABSTRACT FROM AUTHOR]

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency. Revolutionary advanced technology, such as Intelligent Transportation Systems (ITS), enables improved traffic management, helps eliminate congestion, and supports a safer environment. ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users. However, ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks (VANETs). This is because each vehicle plays the role of a router in this network, which leads to a complex vehicle communication network, causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation. This may lead to loss or delay in packet transmissions; this weakness can be exploited in routing attacks, such as black-hole and gray-hole attacks, that threaten the availability of ITS services. In this paper, a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms, enhancing the trustworthiness between vehicles. Self-Classification Blockchain-Based Contracts (SCBC) and Voting-Classification Blockchain-Based Contracts (VCBC) are utilised in the proposed protocol. The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET's network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach's performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures. [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]

The proposed systems can improve cyber security in VANET applications by enabling efficient detection of complex attacks on the RSU component. The subsequent sections discuss the systems that are applied and support the suggestions for improving the VANET trustworthiness. VANETs and show that the utilization of Cross-Layer Adaptive GNNs can improve cyber security and LEARNING in VANET-based RSUs. As a result, the suggested system can provide robust ways for detecting cyber-attacks by: modeling the network architecture using graphs while combining information regarding several protocol layers to detect complicated interactions between the network entities and find the abnormal activities. the nature of the GNN enables it to update in real-time by adapting to the evolving attack patterns and the shifting network conditions, making them sturdy and flexible defense ways for cyber security. The proposed network e systems can efficiently detect multiple cyber threats and focus on reducing the number of false positives while maintaining low computation costs. Therefore, chances are that incorporating the Cross-layer adaptive GNNs into the RSUs can improve cyber security in VANETs, enhancing the robustness and reliability of prospective smart transportation systems. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) have received extensive consideration from the industry and the research community because of their expanding emphasis on constructing Intelligent Transportation Systems (ITS) to enhance road safety. ITS is a collection of technologies and applications that aim to improve transportation safety and mobility while lowering the number of accidents. In VANET, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in VANET and inconsistent network coverage in different areas make routing a challenging task. As a result, ensuring that the VANET routing protocol has the maximum packet delivery ratio (PDR) and low latency is of utmost necessity. Due to the high dynamicity of the VANET environment, position-based routing protocols are paramount for VANET communication. VANET is subjected to frequent network disconnection due to the varied speeds of moving vehicles. Managing and controlling network connections among V2V and V2I is the most critical issue in VANET communication. Therefore, reliable routing protocols that can adapt to frequent network failures and select alternative paths are still an area to be explored further. Majorly, VANET routing protocols follow the greedy approach; once the local maximum is reached, the packets start dropping, resulting in a lower packet delivery ratio. Therefore, lower PDR is still an issue to be resolved in VANET's routing protocols. This paper investigates recent position-based routing protocols proposed for VANET communication in urban and highway scenarios. It also elaborates on topology-based routing, which was initially used in VANET, and its research gaps, which are the major reason for the advent of position-based routing techniques proposed for VANET communication by various researchers. It provides an in-depth comparison of different routing protocols based on their performance metrics and communication strategies. The paper highlights various application areas of the VANET, research challenges encountered, and possible solutions. Further, a summary and discussion on topology-based and position-based routing protocols mark the strengths, limitations, application areas, and future enhancements in this domain. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Vehicles (IoV) and low-orbit satellite Internet not only increases the efficiency of traffic management but also contributes to the emergence of new cyberattacks. Spoofing interference occupies a special place among them. To prevent a rogue satellite from imposing unauthorized content on vehicle owners, a zero-knowledge authentication protocol (ZKAP) based on a modular polyalphabetic polynomial code (MPPC) was developed. The use of MPPC allowed for increasing the authentication speed of the satellite performing the role of RSU. As a result, a reduction in the time needed to guess the prover's signal also reduces the probability of granting a rogue satellite the communication session and increases the imitation resistance of the satellite IoV. At the same time, the MPPC allows for improving the noise resistance of the ZKAP. An algorithm for calculating the control residuals for a noise-resistant MPPC was developed for this purpose, as well as an algorithm for correcting errors arising in the communication channel due to interference. Thus, the developed authentication protocol based on a noise-resistant modular code allows for simultaneously reducing the probabilities of the first-order and second-order errors, which leads to the increased cybersecurity of satellite IoV. [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 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 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]

Vehicular Ad hoc Networks (VANETs) have become prominent in the past few years for the transportation sector. Vehicular mobility poses a significant challenge for establishing private communications in VANETs. The classical Ad hoc On-Demand Distance Vector (AODV) routing protocol used in VANET assumes that all nodes are non-malicious. To address this matter, this paper proposes making AODV routing protocols more secure by using a privacy scheme in AODV for vehicle-to-vehicle communication. The AODV privacy scheme tries to keep the automobile network connected reliably and stably during communication with the secured transmission of messages and minimize the risk of unauthorized access to sensitive information from eavesdropper attacks. The proposed privacy secure AODV routing named PSAODV used pseudonym changes in vehicle communication to hide the target vehicle's location. A VANET simulator based on OMNET++ and SUMO are used for evaluating the PSAODV routing protocol. A simulation study was conducted that compared the PSAODV with SE-AOMDV, ECC-AODV, and AODV in fundamentals of efficiency and confidentiality. The analysis results showed that PSAODV routing demonstrates routing efficiency with privacy by diminishing the effect of eavesdropping of vehicles information based on various scenarios in urban cities. [ABSTRACT FROM AUTHOR]

A charge scheduling strategy is a robust approach to schedule the charging strategies in electric vehicles (EVs) from a broad perspective with the aim of evading the overloading of charging stations and enhancing energy efficiency. However, devising an effective charging scheduling schemefor attaining optimal energy consumption still prevails as a complicated problem, particularly while considering the synchronized behavior of both charging stations as well as EVs. Here, a robust QoS-based charge scheduling approach was developed, which exploits the vehicular Adhoc networks (VANETs) with the improved functionalities for enabling communication between the vehicle-traffic server, road-side units (RSUs), and various EVs on roads. An optimal routing is performed by the Fractional-social sky driver (Fractional SSD), which is devised by the incorporation of the Fractional calculus (FC) and social sky driver (SSD) optimization. Here, the multi-objectives, namely, distance, battery power, and predicted traffic density are considered where the traffic density is effectively predicted using deep recurrent neural network (Deep RNN). Then, the charge scheduling process is executed by the utilization of the developed optimization technique called Fractional-social water cycle algorithm (Fractional SWCA)-based scheduling algorithm by taking into account the QoS-based fitness objective, likepriority, response time, and latency. Moreover, the proposed Fractional SWCA is developed by the integration of fractional SSD and water cycle algorithm (WCA). The performance of the devisedscheme is evaluated withmeasures, like metrics, delay, traffic density, fitness, total trip time, percentage of successful allocation, and power with the values of 8.429 min, 4.8 per lane, 24.571, 49.421 min, 94.494%, and 14,135.72 J. [ABSTRACT FROM AUTHOR]

A vehicle network is a complicated, dense network with a range of network and node-level complexity. The communication issues in a vehicular network are made more difficult by the high mobility, heterogeneity, energy restriction, high density, and scenario circumstances. Additionally, scenarios including traffic jams, accidents, and high communication loads make communication more challenging. Such difficult circumstances are beyond the capabilities of the current routing methods. A zone-based, load- and position-aware routing strategy for vehicular networks is presented in this paper. The infrastructure devices are set up statically and given zonal control in this protocol. In order to determine the load, energy, and fault-safe intermediate nodes, zone-based greedy weighted parameters are analyzed. The neighbor count, load, energy, and distance metrics are among the weighted parameters. The current GPSR protocol incorporates the greedy rule-based optimum neighbor identification method and weighted evaluation. The V2V and V2I communication is optimized using the proposed Load, Fault, Energy adaptive GPSR (LFE_GPSR) protocol. This LFE_GPSR protocol is simulated in a densely populated, heterogeneous environment. The SUMO setup NS2 environment is used for the simulation. The ZRP, GPSR, AMGRP, GSR, E-GyTAR, TFOR, EE-FMDRP, FBAODV, AFMDR, RMRPTS, FLAR-C, and D-CALAR protocols are the subjects of the comparative analysis. To evaluate the effectiveness of long- and short-distance communication, the analytical observations are carried out in a variety of scenarios. Multiple scenarios are generated with different vehicle densities, vehicle speed, and RSUs. In various circumstances, the suggested protocol's performance and efficacy are measured against the transmission delay, PDR ratio, and LFR characteristics. The outcomes show that, in comparison to existing protocols, the suggested protocol significantly reduced communication failure and latency. [ABSTRACT FROM AUTHOR]