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As vehicular technology advances, intelligent vehicles generate numerous computation‐intensive tasks, challenging the computational resources of both the vehicles and the Internet of Vehicles (IoV). Traditional IoV struggles with fixed network structures and limited scalability, unable to meet the growing computational demands and next‐generation mobile communication technologies. In congested areas, near‐end Mobile Edge Computing (MEC) resources are often overtaxed, while far‐end MEC servers are underused, resulting in poor service quality. A novel network framework utilizing sixth‐generation mobile communication (6G) and digital twin technologies, combined with task migration, promises to alleviate these inefficiencies. To address these challenges, a task migration and re‐offloading model based on task attribute classification is introduced, employing a hybrid deep reinforcement learning (DRL) algorithm—Dueling Double Q Network DDPG (QDPG). This algorithm merges the strengths of the Deep Deterministic Policy Gradient (DDPG) and the Dueling Double Deep Q‐Network (D3QN), effectively handling continuous and discrete action domains to optimize task migration and re‐offloading in IoV. The inclusion of the Mini Batch K‐Means algorithm enhances learning efficiency and optimization in the DRL algorithm. Experimental results show that QDPG significantly boosts task efficiency and computational performance, providing a robust solution for resource allocation in IoV. [ABSTRACT FROM AUTHOR]

In recent years, the Integrated Satellite Aerial Terrestrial (I‐SAT) network has garnered significant attention as an innovative and integrated communication system. However, it still encounters interference in the face of the complex external environment. In this context, reconfigurable intelligent surface (RIS) provides a key way of solving this problem and effectively improves the performance and stability of the I‐SAT network. This article considers the combination of unmanned aerial vehicle (UAV) and RIS and proposes a novel architecture for sub‐connected active RIS (ARIS) under the energy consumption constraints of UAV and ARIS. The authors first provide a UAV‐ARIS based position prediction strategy for the vehicle. Then, a joint RIS phase shift, amplification and UAV trail optimization algorithm is proposed to pursue a high achievable rate. The interference between each link and the total energy consumption are all taken into consideration. In addition, a deep deterministic policy gradient (DDPG) algorithm is utilized for the optimization problem, and achieves convergence in continuous action space. Finally, the simulation results affirm the precision of the proposed method in significantly enhancing performance compared to other schemes. [ABSTRACT FROM AUTHOR]

Recently, due to increasing the number of rechargeable battery-based electric vehicles around the world, a new style of vehicular networks called electrical vehicular ad hoc networks (E-VANET) has emerged. However, In addition to the vehicle's battery limitation; E-VANET suffers from several challenges. First, forwarding the data via electric vehicles with a low level of residual energy affects the lifetime and stability of vehicles and routes. Second, selecting a route with a large number of hops consumes a lot of vehicle energy. Third, due to the mobility of electric vehicles; the built routes are destroyed frequently and must be corrected with a high amount of consumed energy. One of the efficient solutions for these issues is selecting a set of the shortest routes that have intermediate vehicles with maximum residual energy. This paper formulates the routing problem as a Pareto optimization problem to do a trade-off between maximizing the residual energy and minimizing the number of hops in the selected paths. Moreover, a new residual energy and hop count-based multipath routing protocol for E-VANET called EHMRE is proposed. EHMRE depends on the Pareto optimization model to select a set of paths with a minimum number of hops and contain vehicles with maximum residual energy. It aims to reduce the number and effect of frequent route correction processes and enhance energy consumption and path stability. The simulation showed that first, in terms of PDR, EHMRE results are about 13%, 6%, and 10% improvement with various numbers of vehicles and about 12%, 5%, and 7% improvement with different velocities compared with DMRV, ACOPSO, and RMRUV, respectively. Second, EC of EHMRE with various numbers of vehicles is approximately 29%, 33%, and 36%, while with different velocities, it is about 23%, 27%, and 30% less than DMRV, ACOPSO, and RMRUV, respectively. Third, in terms of dead vehicles, EHMRE produces about 73%, 77%, and 80% reduction with various numbers of vehicles and presents about 71%, 75%, and 77% reduction with different velocities compared with DMRV, ACOPSO, and RMRUV, respectively. [ABSTRACT FROM AUTHOR]

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project's overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load. [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]

In contemporary times, as accident rates continue to rise, there is a growing interest in vehicular ad hoc networks (VANETs) as a means to reduce accidents and vehicle damage, as well as enhance public safety. VANETs facilitate the transmission of messages within predefined areas to ensure system safety and efficiency. However, ensuring message authenticity in such a dynamic environment presents a significant challenge, with previous research focusing less on security aspects. Some previous protocols send plain message, which leads to the compromise of the message confidentiality. Although few algorithms work on security, they involve the communication to the trusted authority, which lead to increased latency. Thus, in this paper we propose a robust cryptographic scheme for reliable data communication in VANET which minimizes the involvement of trusted authority and uses communication ID of vehicle for communication in place of real entities. Security analysis of the proposed scheme is showing the sturdiness of the proposed protocol again various types of safety spasms. The evaluation of its performance includes assessing communication cost and time, revealing the proposed algorithm's efficiency compared to others. [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]

The identity authentication scheme in vehicular Ad hoc network is one of the effective ways to solve many security and privacy problems in the Internet of Vehicles. However, most of the existing authentication schemes do not focus on single points of failure or information leakage. In order to solve the above problems, this paper proposed a threshold-based edgeassisted identity authentication scheme in VANET. On the premise of protecting the real identity information of the vehicle, the solution cooperated with multiple edge nodes to authenticate the vehicle, thereby avoiding the issue of a single point of failure. At the same time, in the initial cooperative authentication stage, the scheme generated a token for rapid switching of identity authentication in the authentication stage, and achieved rapid switching based on identity authentication without interacting with multiple edge nodes, which effectively reduced the authentication delay. If there was an abnormality in the authentication process, the cloud computing center could also track the malicious vehicle, restore its real identity and revoke it. The security analysis and simulation experiments show that the proposed scheme is secure and effective. Finally, it prospected the proposed scheme summarized and the future work. [ABSTRACT FROM AUTHOR]

Access control refers to the mechanisms and policies determining which vehicles or entities can access certain network resources, services, or information. Nevertheless, the dynamic nature of a VANET, the necessity to support a wide range of services, and the importance of protecting users' personal information make access control a challenging issue in vehicle contexts. This study presents an efficient access control scheme based on a blockchain to enhance efficiency and security in heterogeneous signcryption (AC-HSC). Additionally, the AC-HSC protocol incorporates blockchain technology for revocation transparency, allowing roadside units to efficiently verify revoked pseudo-identities without relying on a central unit. The AC-HSC effectively fulfills various security criteria, including integrity, privacy preservation, authentication, traceability, unlinkability, non-repudiation, and resistance against replay attacks. Moreover, the formal analysis carried out in a random oracle model (ROM) proves that the proposed protocol is secure against indistinguishability under chosen-ciphertext attack (IND-CCA2) under the Computational Diffie-Hellman (CDH) assumption and existential unforgeability under chosen message attack (EUF-CMA) under the Discret-Logarithm (DL) assumption. In addition, the proposed protocol effectively addresses the issue of key escrow and successfully eliminates the challenges associated with certification management. Finally, the AC-HSC protocol implemented with a free pairing demonstrates reduced computational costs by 3.57%, 50%, 53.53%, 72.22%, 32.66%, 50.90%, 3.57%, and 57.14%, respectively, and the total energy consumption of the VANET nodes in our protocol reduced by about 4.16%, 45.03%, 50.04%, 68.37%, 31.52%, 49.67%, 4.16%, and 53.68%, respectively, as compared with the existing signcryption schemes. [ABSTRACT FROM AUTHOR]

While driving, stress and frustration can affect safe driving and pose the risk of causing traffic accidents. Therefore, it is important to control the driver's anxiety level in order to improve the driving experience. In this paper, we propose and implement an intelligent system based on fuzzy logic (FL) for deciding the driver's anxiety level (DAL). In order to investigate the effects of the considered parameters and compare the evaluation results, we implement two models: DAL Model 1 (DALM1) and DAL Model 2 (DALM2). The input parameters of DALM1 include driving experience (DE), in-car environment conditions (IECs), and driver age (DA), while for DALM2, we add a new parameter called the accident anxiety state (AAS). For both models, the output parameter is DAL. We carried out many simulations and compared the results of DALM1 and DALM2. The evaluation results show that the DAL is very good for drivers' ages between 30 to 50 years old. However, when the driver's age is below 30 or above 50, DAL tends to decline. With an increase in DE and IECs, the DAL value is decreased. But when the AAS is increased, the DAL is increased. DALM2 is more complex because the rule base is larger than DALM1, but it makes a better decision of DAL value. [ABSTRACT FROM AUTHOR]

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

Vehicular ad hoc networks (VANETs) provide intelligent navigation and efficient route management, resulting in time savings and cost reductions in the transportation sector. However, the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy violations. To address these challenges, several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed recently. However, most of these schemes are limited by a long execution time and massive communication costs, which make them inefficient for on-board units (OBUs). Additionally, some of them are still susceptible to many attacks. As such, this study presents a novel protocol based on the fusion of elliptic curve cryptography (ECC) and bilinear pairing (BP) operations. The formal security analysis is accomplished using the Burrows–Abadi–Needham (BAN) logic, demonstrating that our scheme is verifiably secure. The proposed scheme’s informal security assessment also shows that it provides salient security features, such as non-repudiation, anonymity, and unlinkability. Moreover, the scheme is shown to be resilient against attacks, such as packet replays, forgeries, message falsifications, and impersonations. From the performance perspective, this protocol yields a 37.88% reduction in communication overheads and a 44.44% improvement in the supported security features. Therefore, the proposed scheme can be deployed in VANETs to provide robust security at low overheads. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) is an important topic nowadays. A lot of research deal and attracts consideration owing to potential for increasing traffic and travel efficiency, improving road safety for vehicles, providing convenience and comfort to both drivers and passengers. The need for a packet delivery ratio (PDR) and low delivery delay time in communication are the key elements in modern life especially when traveling in vehicles. To satisfy these demands; researchs in VANET systems aims to develop some new technologies. One of these technologies is using software-definednetwork (SDN) to enhance communication between vehicles on the road. Because of this, project evaluates using SDN protocol with two most viable VANET protocols which are ad hoc on demand distance vector (AODV) and optimized link state routing (OLSR) in LTE communication. Two performance metrics are used to evaluate the performances, the PDR and the delivery delay time. The simulation is performed in the varying density network and varying speed vehicles. The simulation results show that SDN displays better performance than AODV and OLSR in both PDR and delivery delay time. SDN uses global views of SDN controller to determine the shortest route with the highest vehicle density. Additionally, it solves the local maximum issue and adds dense connectivity. [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]

The routing protocol for Vehicular Ad Hoc Networks (VANETs) uses hop-count as a routing metric to determine the shortest route from source to destination. The presence of unstable links in the selected route may cause packet failures, resulting in an increase in packet retransmissions, causing network congestion, increasing total end-to-end delay, and reducing throughput. Thus, route selection based on hop count may not always be optimal in terms of route link quality. In Software-Defined Vehicular Networks (SDVN), the global view of link-state information for the vehicular networks is created at the controller; thus, a better route can be computed. Our study proposes an SDVN routing technique that considers link quality while determining the route. Our proposed framework examines the link quality using two metrics: Expected Transmission Count (ETX) and Expected Transmission Time (ETT), and this information is used by the controller to compute the flow rule. The routing performance of the proposed scheme is compared to traditional VANETs and other existing works with the help of network simulations using the NS-3 simulator. The obtained simulation results for our proposed scheme outperform in terms of performance metrics such as packet delivery ratio, average throughput, and end-to-end delay. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) are recent advancements that permit vehicles to communicate with infrastructure and other vehicles, improving road safety and traffic efficiency. One of the difficulties in constructing and maintaining VANETs deals with the consequences of blockage, it may occur when buildings, trees, or other obstructions block radio signals between vehicles. However, the presence of vehicles as obstacles can severely impact the performance of VANETs. In this paper, an efficient machine learning (ML) model is developed to identify the impact of vehicle obstacles in VANETs. The proposed optimizable tree ML model showed better results in comparison to the other existing models. The results of the proposed model are superior as compared with other existing models in terms of nine performance measures namely, recall, specificity, balanced accuracy, accuracy, error rate, precision, F1 score, FNR and FPR. The values of these nine performance matrices for the proposed optimizable tree ML model are 0.99, 0.99, 0.99, 0.99, 0.01, 0.99, 0.99, 0.01, and 0.01 respectively. [ABSTRACT FROM AUTHOR]

Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment. [ABSTRACT FROM AUTHOR]

In recent times, Vehicular Ad hoc Network (VANET) has been the focal point of the research community to devise efficient smart transportation systems. VANET provides the key advantage of providing cautionary measures and safety to passengers and drivers. With the evolution of fifth-generation (5G) network technology and rapid growth in vehicles, it becomes challenging for conventional VANET to manage large-scale dynamic heterogeneous networks due to their limited flexibility and scalability features. Moreover, the dynamic nature of VANET makes it vulnerable to malicious attacks. Software Defined Networking (SDN) is a technology that provides an integrated improvement over the conventional VANETs. SDN architecture is flexible, programmable, scalable, and provides globally the knowledge of the network. However, its centralized nature makes SDN based VANETs a prime target of attackers, which may adversely impact the VANETs causing life-threatening consequences. To address these issues, this paper presents two novel schemes. Firstly, this paper presents a trusted routing scheme named Jellyfish Chimp Optimization Algorithm (JChOA) for SDN based VANETs. JChOA is designed by amalgamation of the Jellyfish Search Optimization algorithm (JS) and Chimp Optimization algorithm (ChOA). Secondly, this paper presents an attack detection and mitigation scheme named JChOA_RideNN for SDN based VANETs. This attack detection scheme utilizes the Rider Optimization Algorithm based neural network (RideNN) architecture at the SDN controller, where the weighting parameters of RideNN tunned through the use of JChOA. The effectiveness of JChOA routing is evaluated based on the metrics energy and trust value where the performance of JChOA_RideNN is assessed using precision and recall. Moreover, the JChOA routing algorithm attained greater performance with a maximum of 0.947 J energy and 0.462 trust value and JChOA_RideNN attained with a maximum of 93.9% precision, and 93.1% recall than other traditional approaches. The results of the experiments clearly show the effectiveness of the proposed defensive schemes for SDN based VANETs. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs), which are the backbone of intelligent transportation systems (ITSs), facilitate critical data exchanges between vehicles. This necessitates secure transmission, which requires guarantees of message availability, integrity, source authenticity, and user privacy. Moreover, the traceability of network participants is essential as it deters malicious actors and allows lawful authorities to identify message senders for accountability. This introduces a challenge: balancing privacy with traceability. Conditional privacy-preserving authentication (CPPA) schemes are designed to mitigate this conflict. CPPA schemes utilize cryptographic protocols, including certificate-based schemes, group signatures, identity-based schemes, and certificateless schemes. Due to the critical time constraints in VANETs, efficient batch verification techniques are crucial. Combining certificateless schemes with batch verification leads to certificateless aggregate signature (CLAS) schemes. In this paper, cryptanalysis of Xiong's CLAS scheme revealed its vulnerabilities to partial key replacement and identity replacement attacks, alongside mathematical errors in the batch verification process. Our proposed CLAS scheme remedies these issues by incorporating an identity authentication module that leverages chameleon hashing within elliptic curve cryptography (CHAM-CLAS). The signature and verification modules are also redesigned to address the identified vulnerabilities in Xiong's scheme. Additionally, we implemented the small exponents test within the batch verification module to achieve Type III security. While this enhances security, it introduces a slight performance trade-off. Our scheme has been subjected to formal security and performance analyses to ensure robustness. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is a technology that enhances vehicular applications by offloading the task of the resource-restricted vehicle to the resourceful fog node (vehicle or static fog node). The mobility of vehicles leads to various challenges in task offloading, i.e., low successful offloading ratio, high response time, etc. Apart from this, the available idle computation resource at the fog node is different at the time of advertisement of computation capability and at the time of task allocation. This change results in less efficient resource utilization and unequal load balance at the fog node. Hence to overcome this, we have proposed an approach based on future mobility and imminent computation awareness (FMICA). The future mobility prediction allows us to offload the task to the fog node, which will be near during task retrieval. Additionally, imminent computation awareness of the available resources (at the imminent time τ ) allows us to efficiently utilize the idle resources through offloading higher deadline tasks at the τ time. FMICA optimizes resources according to various criteria such as offloading delay, load variance, within-range ratio, and successful offloading ratio using a Strength Pareto Evolutionary Algorithm 2 (SPEA2). An order preference by similarity to the ideal solution (TOPSIS) and multiple criteria decision-making (MCDM) technique is used to select the best solution. Through the experimental analysis, we have found that the FMICA has an improvement of 37.64%, 25.57%, 9.96%, and 12.62% in metrics average response time, successful offloading ratio, resource utilization, and average load variance against available approach, respectively. [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]

The aim of the intelligent transport system (ITS) is the improvement of road safety. This system is based on intelligent vehicles composing a network called Vehicular Ad hoc NETwork (VANET). This network suffers from a disconnection problem due to its dynamic topology. Therefore, a framework of collaboration between the Wireless Sensor Network (WSN) and VANET called Hybr1id Sensor and Vehicular Networks (HSVN) has been proposed. In HSVN, the WSN can play the role of a relay between disconnected vehicles. This paper aims to propose a strategy allowing the exchange of messages between the two networks. That reduces the number of accidents and improves the management of road traffic. We can summarize our proposition in three essential points. First, an algorithm is proposed to decompose the vehicular network into clusters. This algorithm takes into account the mobile aspect of vehicles and the road model. Second, a data encoding model and a message model have been proposed to improve the quality of messages. That reduces the response time of drivers to a critical situation. Finally, an exchange algorithm is proposed to ensure the transmission of road messages between vehicles and sensors. Its principle of work is based on several scenarios defined relative to the network condition. Obtained results show an improvement in the delivery delays of road messages and the number of exchanged road messages between the vehicles. [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]

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]

Internet of Vehicles applications are known to be critical and time-sensitive. The value proposition of edge computing comprises its lower latency, advantageous bandwidth consumption, privacy, management, efficiency of treatments, and mobility, which aim to improve vehicular and traffic services. Successful stories have been observed between IoV and edge computing to support smooth mobility and the use of local resources. However, vehicle travel, especially due to high-speed movement and intersections, can result in IoV devices losing connection and/or processing with high latency. This paper proposes a Cluster Collaboration Vehicular Edge Computing (CCVEC) framework that aims to guarantee and enhance the connectivity between vehicle sensors and the cloud by utilizing the edge computing paradigm in the middle. The objectives are achieved by utilizing the cluster management strategies deployed between cloud and edge computing servers. The framework is implemented in OpenStack cloud servers and evaluated by measuring the throughput, latency, and memory parameters in two different scenarios. The results obtained show promising indications in terms of latency (approximately 390 ms of the ideal status) and throughput (30 kB/s) values, and thus appears acceptable in terms of performance as well as memory. [ABSTRACT FROM AUTHOR]

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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]

This work proposes a new architectural approach to enhance the security, privacy, and scalability of VANETs through threshold key management and Privacy Enhancing Technologies (PETs), such as homomorphic encryption and secure multiparty computation, integrated with Decentralized Ledger Technologies (DLTs). These advanced mechanisms are employed to eliminate centralization and protect the privacy of transferred and processed information in VANETs, thereby addressing privacy concerns. We begin by discussing the weaknesses of existing VANET architectures concerning trust, privacy, and scalability and then introduce a new architectural framework that shifts from centralized to decentralized approaches. This transition applies a decentralized ledger mechanism to ensure correctness, reliability, accuracy, and security against various known attacks. The use of Layer 2 DLTs in our framework enhances key management, trust distribution, and data privacy, offering cost and speed advantages over Layer 1 DLTs, thereby enabling secure vehicle-to-everything (V2X) communication. The proposed framework is superior to other frameworks as it improves decentralized trust management, adopts more efficient PETs, and leverages Layer 2 DLT for scalability. The integration of multiparty threshold key management and homomorphic encryption also enhances data confidentiality and integrity, thus securing against various existing cryptographic attacks. Finally, we discuss potential future developments to improve the security and reliability of VANETs in the next generation of networks, including 5G networks. [ABSTRACT FROM AUTHOR]

The secret key is stored in an ideal tamper-proof device so that a vehicle can implement a secure authentication with the road-side units (RSUs) and other drivers. However, some adversaries can capture the secret key by physical attacks. To resist physical attacks, we propose a physical-preserving authentication based on a physical unclonable function for vehicular ad hoc networks. In the proposed scheme, a physical unclonable function is deployed on the vehicle and the RSU to provide a challenge–response mechanism. A secret key is only generated by the challenge–response mechanism when it is needed, which eliminates the need to store a long-term secret key. As a result, this prevents secret keys from being captured by adversaries, improving system security. In addition, route planning is introduced into the proposed scheme so that a vehicle can obtain the authentication key of RSUs on its route before vehicle-to-infrastructure authentication, which greatly speeds up the authentication when the vehicle enters the RSUs' coverage. Furthermore, a detailed analysis demonstrates that the proposed scheme achieves security objectives in vehicular ad hoc networks. Ultimately, when contrasted with similar schemes, the performance assessment demonstrates that our proposed scheme surpasses others in terms of computational overhead, communication overhead and packet loss rate. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) and the Internet of Vehicles (IoV) represent rapidly developing technologies. The majority of car manufacturing companies invest large amounts of money in the field of connected autonomous vehicles. Applications of connected and autonomous vehicles (CAVs) relate to smart transport services and offer benefits to both society and the environment. However, the development of autonomous vehicles may create vulnerabilities in security systems, through which attacks could harm both vehicles and their drivers. To this end, CAV development in vehicular ad hoc networks (VANETs) requires secure wireless communication. However, this kind of communication is vulnerable to a variety of cyber-attacks, such as spoofing. In essence, this paper presents an in-depth analysis of spoofing attack impacts under realistic road conditions, which may cause some traffic congestion. The novelty of this work has to do with simulation scenarios that take into consideration a set of cross-layer parameters, such as packet delivery ratio (PDR), acceleration, and speed. These parameters can determine the integrity of the exchanged wave short messages (WSMs) and are aggregated in a central trusted authority (CTA) for further analysis. Finally, a statistical metric, coefficient of variation (CoV), which measures the consequences of a cyber-attack in a future crash, is estimated, showing a significant increase (12.1%) in a spoofing attack scenario. [ABSTRACT FROM AUTHOR]

In Vehicular Ad Hoc Networks, reliable information transmission relies on an effective routing strategy. Most existing reinforcement learning-based routing methods are ineffective in dynamic network environments and cannot prevent inefficient network routing. Efficient network routing can be controlled by network traffic management, so this paper proposes an intelligent routing strategy based on Deep Reinforcement Learning to enhance routing performance. By integrating intersection forwarding and traffic awareness capabilities, this paper addresses the problem of local optimality and utilizes the Deep Q Network to make intersection forwarding decisions. The state space of this strategy consists of intersection nodes, road information between intersections, and forwarding packet information. When a vehicle node carrying a packet approaches an intersection based on the state space, the intersection node uses a neural network to select the optimal next-hop relay intersection from past learning experiences. It generates appropriate vehicle routing decisions based on information from the current and candidate relay intersections. Finally, we use the real taxi trajectory data of Beijing City to conduct extensive simulation experiments. Simulation results and analysis demonstrate that the proposed strategy outperforms related research regarding higher average packet delivery ratio, shorter average end-to-end delay, and lower average overhead ratio in dense and sparse traffic periods under real road environments. Consequently, this strategy provides efficient and reliable message transmission services for Vehicular Ad Hoc Networks. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) includes groups of stationary or moving vehicles linked by a wireless network. The significant usage of VANET is to offer comfort and safety to drivers in road environments. VANET provides a communication framework that aids in minimizing accidents. Also, sharing data in VANET is time-sensitive and necessitates vigorous and quick network link formation. However, appropriate routing is critical to avoid the streaming issues that occur in VANET applications. This research area is of great concern to the researchers, and this work intends to propose a new Cluster-based VANET routing model with steps like (a) optimal cluster head selection (CHS) and (b) appropriate gateway selection for data transmission. Here, the optimal CHS takes place considering the Packet delivery ratio (PDR), mean routing load, end-to-end (e2e) Delay, Throughput, and packet overhead. This paper introduced a new Customized hunger's foraging honey badger with a Dynamic multi-objective non-sorted genetic algorithm (CHFHB-DMNSGA). Also, the gateway selection is done under two objectives: Vehicular gateway mobility and receiver signal strength, respectively. Finally, the paper presented a comparative assessment to validate the effectiveness of the adopted algorithm CHFHB-DMNSGA over the other algorithms like R-NSGA-II, NSGA-III, NSGA-II, HBA, HGS, ABC, FF, CSO, ACO, LA, MHB-GA, GA-FF, and HFCHBO. [ABSTRACT FROM AUTHOR]

The increasing reliance of Vehicular Ad-hoc Networks (VANETs) on the BeiDou Navigation Satellite System (BDS) for precise positioning and timing information has raised significant concerns regarding their vulnerability to spoofing attacks. This research proposes a novel approach to mitigate BeiDou spoofing attacks in VANETs by leveraging a hybrid machine learning model that combines XGBoost and Random Forest with a Kalman Filter for real-time anomaly detection in BeiDou signals. It also introduces a geospatial message authentication mechanism to enhance vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication security. The research investigates low-cost and accessible countermeasures against spoofing attacks using COTS receivers and open-source SDRs. Spoofing attack scenarios are implemented in both software and hardware domains using an open-source BeiDou signal simulator to examine the effects of different spoofing attacks on victim receivers and identify detection methods for each type, focusing on pre-correlation techniques with power-related metrics and signal quality monitoring using correlator values. The emulation results demonstrate the effectiveness of the proposed approach in detecting and mitigating BeiDou spoofing attacks in VANETs, ensuring the integrity and reliability of safety-critical information. This research contributes to the development of robust security mechanisms for VANETs and has practical implications for enhancing the resilience of transportation systems against spoofing threats. Future research will focus on extending the proposed approach to other GNSS constellations and exploring the integration of additional security measures to further strengthen VANET security. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the Integrated Satellite Aerial Terrestrial (I‐SAT) network has garnered significant attention as an innovative and integrated communication system. However, it still encounters interference in the face of the complex external environment. In this context, reconfigurable intelligent surface (RIS) provides a key way of solving this problem and effectively improves the performance and stability of the I‐SAT network. This article considers the combination of unmanned aerial vehicle (UAV) and RIS and proposes a novel architecture for sub‐connected active RIS (ARIS) under the energy consumption constraints of UAV and ARIS. The authors first provide a UAV‐ARIS based position prediction strategy for the vehicle. Then, a joint RIS phase shift, amplification and UAV trail optimization algorithm is proposed to pursue a high achievable rate. The interference between each link and the total energy consumption are all taken into consideration. In addition, a deep deterministic policy gradient (DDPG) algorithm is utilized for the optimization problem, and achieves convergence in continuous action space. Finally, the simulation results affirm the precision of the proposed method in significantly enhancing performance compared to other schemes.

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]

Vehicular ad hoc network (VANET) is a promising technology for controlling traffic on roads. Nowadays, heavy traffic is a major issue, and the presence of attackers exacerbates the situation. The most important challenge in VANET is its security from malicious vehicles. In order to defend against distributed denial of service DDoS attacks, we propose a comprehensive verification header format bandwidth detection (CVHB) in VANET. The behavior of a DDoS attack is unknown for all the other normal nodes in network. The header format of packer contains all the information of nodes that are actively participating in routing. The attacker infection probability measured by and or (10> 0.9). If both the parameters are high means attacker presence confirm in network. The CVHB scheme checks the packet header format of the attacker node, and only the attacker is one of the nodes whose sequence number is frequently changing. So, CVHB blocks the flooding of unwanted packets that consume the limited bandwidth of a wireless link and identify packets that contain no useful information. To measure the performance of the network, the basic performance metrics that are used are dropping percentage, packet delivery ratio (PDR), throughput and delay. The result of CVHB is showing improvement as compared to multilayer distributed self-organizing maps (MSOM) in VANET. [ABSTRACT FROM AUTHOR]

Abstract The sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

This paper presents a comprehensive study of 3D point cloud Federated Few-Shot Learning (3DFFL), focusing on addressing challenges such as limited data availability and privacy concerns in point cloud classification for applications such as autonomous vehicles. We introduce a novel approach that integrates Federated Learning with Few-Shot Learning techniques, with a special emphasis on optimizing network architectures for 3D point cloud data. Our method capitalizes on the strengths of PointNet++ for feature extraction and ProtoNet for classification, all within a federated learning framework to ensure data privacy and collaborative learning. Significantly, the approach is augmented with the use of attention and SoftMax layers, enhancing the feature extraction and classification processes. Extensive experiments on the ModelNet40, ShapeNet, and ScanOnjectNN datasets validate our method's accuracy and adaptability in handling 3D point cloud classification, especially in privacy-sensitive and collaborative scenarios. This study not only demonstrates the potential of integrating attention mechanisms and SoftMax layers in 3DFFL but also lays a robust foundation for future advancements in this evolving field, particularly in technologies dependent on 3D data processing. [ABSTRACT FROM AUTHOR]

Vehicle Ad-hoc Networks (VANETs) have experienced significant development in recent years, playing a crucial role in enhancing the driving experience by enabling safer and more efficient inter-vehicle interactions through information exchange. Vehicle-to-Vehicle (V2V) communication is particularly vital as it not only helps to prevent collisions and improve traffic efficiency but also provides essential situational awareness to drivers or autonomous driving systems. Communication is typically supported by roadside units (RSUs); however, in practical applications, vehicles may exceed the communication range of RSUs, thus exposing them to various malicious attacks. Additionally, considering the limited computational resources of onboard units (OBUs) in vehicles, there is a high demand for designing lightweight security protocols that support V2V communication. To address this issue, this paper proposes an efficient anonymous V2V identity-authentication protocol tailored for scenarios that lack RSU support. The proposed protocol was formally assessed using the Scyther tool, demonstrating its capability to withstand major typical malicious attacks. Performance evaluations indicate that the proposed protocol is efficient in terms of communication and computational overhead, making it a viable solution for V2V communication. [ABSTRACT FROM AUTHOR]

In the current 5G vehicle network system, there are security issues such as wireless intrusion, privacy leakage, and remote control. To address these challenges, an improved lightweight anonymous authentication key negotiation scheme based on certificate-less aggregate signatures is proposed and its security and efficiency are analyzed. The result shows that the scheme can offer security attributes including anonymity, traceability, and revocability, as well as effective identity authentication, and it can resist forgery attacks, man-in-the-middle attacks, tampering attacks, and smart card loss attacks. Moreover, compared with similar schemes, it possesses superior security and more efficient computational efficiency and less communication overhead, thereby being more appropriate for high-speed, large-capacity, low-latency, and resource-constrained 5G vehicle network application scenarios. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks (VANETs) can significantly improve the level of urban traffic management. However, the sender unlinkability has become an intricate issue in the field of VANETs' encryption. As the sender signcrypts a message, the receiver has to use the sender's identity or public key to decrypt it. Consequently, the sender can be traced using the same identity or public key, which poses some security risks to the sender. To address this issue, we present an unlinkable and revocable signcryption scheme (URSCS), where an efficient and powerful signcryption mechanism is adopted for communication. The sender constructs a polynomial to generate a unique session key for each communication, which is then transmitted to a group of receivers, enabling the same secret message to be sent to multiple receivers. Each time a secret message is sent, a new key pair is generated, and an anonymization mechanism is introduced to conceal the true identity of the vehicle, thus preventing malicious attackers from tracing the sender through the public key or the real identity. With the introduction of the identification public key, this scheme supports either multiple receivers or a single receiver, where the receiver can be either road side units (RSUs) or vehicles. Additionally, a complete revocation mechanism is constructed with extremely low communication overhead, utilizing the Chinese remainder theorem (CRT). Formal and informal security analyses demonstrate that our URSCS scheme meets the expected security and privacy requirements of VANETs. The performance analysis shows that our URSCS scheme outperforms other represented schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments. [ABSTRACT FROM AUTHOR]

Introduction: As the main means of information exchange within vehicles, the safety of the controller area network bus directly affects the safe operation of the vehicle and the safety of passengers' lives and property. Methods: To enhance its secure communication function, this study utilizes advanced encryption standard algorithms and improves the S-box of the algorithm to solve problems such as extended processing time. A secure communication system for the local area network bus of the vehicle controller is designed based on optimized advanced encryption standard algorithms. Results and Discussion: The results showed that when the file size was 200MB, the encryption and decryption time spent by the research method was 469.8 s and 528.5 s, respectively, which are significantly lower than traditional methods. In the simulation results, under both non-encrypted and encrypted transmission, the information remained intact throughout the entire transmission process. This indicated that the optimization algorithm effectively reduced encryption processing time and system resource consumption while ensuring data confidentiality and integrity. The new system meets the security requirements of the local area network bus of vehicle-mounted controllers. Conclusion: This study not only enhances the security of in-vehicle networks but also promotes the application and development of related encryption technologies in the field of vehicle networking. It provides strong technical support for the further development of vehicle networking and the safe operation of intelligent vehicles. [ABSTRACT FROM AUTHOR]

The emergence of the intelligent city, in the late 2000s, was a worldwide success that has invited itself to the national political agendas of several countries in world association of the major metropolises as well as in the multinational strategy. The success of this global phenomenon is indisputable; however, the smart city remains enigmatic, without precise definition and vague terms. The smart cities projects are characterized by the ability to interconnect urban systems between themselves, including transport system, electricity system, waste system, water system, gas system, etc. and to make each system develop itself; For instance, the field of urban and interurban transport has experienced a spectacular boom over the last few decades mainly with the appearance of vehicular ad hoc networks (VANETs) having promoted the concept of smarts cities mentioned by the birth of intelligent transport systems (ITS). The notion of routing is important and necessary for ITS in order to establish an efficient transmission process in VANETs. A large number of routing protocols exist in VANETs that are classified by different methods and according to determined criteria. This paper proposes a new geographic routing solution based on an intersection entitled A Geographic Routing Based on Local Traffic Density and Multi-hop Intersections in VANETs (GRBLTD-MI) that is more adapted in the context of smart cities. This new approach will discuss the selection of the next intersection when taking into consideration the connectivity of the multi-hop intersection with the use of vehicle's local traffic density, speed, distance and direction. Simulation comparison of GRBLTD-MI against some strong routing protocols has shown the superiority of our approach in terms of packet delivery ratio, end-to-end delay and packet overhead. [ABSTRACT FROM AUTHOR]