Search

Vehicular Ad-Hoc Networks (VANETs) play an essential role in the intelligent transportation era, furnishing users with essential roadway data to facilitate optimal route selection and mitigate the risk of accidents. However, the network exposure makes VANETs susceptible to cyber threats, making authentication crucial for ensuring security and integrity. Therefore, joining entity verification is essential to ensure the integrity and security of communication in VANETs. However, to authenticate the entities, authentication time should be minimized to guarantee fast and secure authentication procedures. We propose an authentication system for VANETs using blockchain and Kerberos for storing authentication messages in a blockchain ledger accessible to Trusted Authentication Servers (TASs) and Roadside Units (RSUs). We evaluate the system in three diverse network scenarios: suburban, urban with 1 TAS, and urban with 2 TASs. The findings reveal that this proposal is applicable in diverse network scenarios to fulfill the network requirements, including authentication, handover, and end-to-end delay, considering an additional TAS for an increasing number of vehicles. The system is also practicable in storing the authentication message in blockchain considering the gas values and memory size for all scenarios. [ABSTRACT FROM AUTHOR]

Abstract In order to improve road safety and offer extra services to cars and their users, vehicular ad hoc networks, or VANETs, are essential parts of intelligent transportation systems (ITS). The primary aim of this research is to suggest and assess a new approach for mitigating network congestion in VANET technology through the implementation of dynamic grouping of vehicles for safety (DGVS). To do this, virtual regions are created around cars using the DBSCAN and K Mean method. This allows vehicles to communicate directly with others within the same DGVS, eliminating the need to broadcast messages across the entire network.The ultimate goal is to drastically decrease traffic while preserving vital data transfer for VANET traffic control and safety. The suggested technique determines the optimal transmission rate in accordance with the existing channel circumstances, yielding a balanced performance concerning both packet delivery and channel congestion. With this novel method, cars may only talk to other vehicles that are in the same DGVS, thus there’s no need to broadcast messages to the whole network. With the use of this technique, the efficacy of DGVS in reducing VANET congestion and enhancing network performance will be thoroughly assessed. The study’s conclusions demonstrate how well the suggested dynamic grouping of vehicles for safety (DGVS) technique works to reduce VANET congestion. It was shown through simulation-based studies that, in comparison to current approaches, DGVS dramatically decrease network congestion, resulting in notable gains in network performance and decreased communication delay. Additionally, the study found a number of possible uses for DGVS in the transportation industry, such as emergency response, traffic management, and accident prevention.

Abstract VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is a promising project related to intelligent transportation systems (ITS), which aims at connecting vehicles and providing a set of functionalities for the efficient management of the network. However, the high mobility of the network nodes is considered a significant challenge for implementing a reliable, secure, and efficient exchange system. Furthermore, VANET faces the issue of packet delivery due to the high mobility of the nodes and packet collisions complicate the process of sending and receiving packets. We propose to combine two technologies which are unmanned aerial vehicle (UAV) and information centric networks (ICN) and apply it in VANET architecture as supporting technology. The UAV are more reliable and less affected by channel fading. And can be used in areas where we cannot install network infrastructure. The UAV has many advantages that we have cited in this article and can solve many issues of VANET. Using ICN can solve some of the problems of VANET since ICN has many strategies to capture and retrieve data. This study proposes a new VANET model based on an UAV and ICN, to reduce the overload of the vehicles, which in most cases require more resources and have a limited time to process and act especially in case of an accident or emergency. [ABSTRACT FROM AUTHOR]

Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment. [ABSTRACT FROM AUTHOR]

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

In the vehicle communication system, location information leakage of vehicles can endanger the privacy and security of drivers, and pseudonym changes based on vehicles in the mixed zones are an effective way to achieve location privacy protection. However, some existing mixed zone schemes ignore the impact of vehicle density changes on location privacy protection. To solve this problem, this paper proposed a mix zone location privacy protection scheme that supported virtual vehicle-assisted pseudonym changes. The scheme aimed to dynamically adjust the generation of required virtual vehicles according to the density of surrounding cooperative vehicles and broadcast their tracks, so that attackers couldn't distinguish between virtual vehicles and real vehicles, thereby achieving location privacy protection of vehicles. The simulation results show that the scheme introduces virtual vehicle information to make it impossible for attackers to distinguish between virtual vehicles and real vehicles, effectively reducing the possibility of leakage of the real location or trajectory of vehicles, and enhancing the location privacy protection effect under low traffic density. [ABSTRACT FROM AUTHOR]

The exchange of perceptual information between autonomous vehicles could significantly improve driving safety. In general, obtaining more information means driving more safely. However, Frequent information sharing consumes a significant amount of channel bandwidth resources, which will reduce transmission effi- ciency and increase delay, especially in crowded cities. This paper presents a novel method of motion prediction compensation to solve this problem. Firstly, we propose a distributed fountain coding scheme to improve transmission efficiency and reduce vehicles' delay in acquiring peripheral information. Secondly, we design a mobile prediction model and information transmission control algorithm to reduce traffic while ensuring information reliability. The simulation results show that the prediction accuracy of this method is above 94 %, the information transmission is reduced by more than 50 %, and the vehicle perception rate is increased by 34 %. [ABSTRACT FROM AUTHOR]

The study introduces the learning automata-based AODV (LA-AODV) protocol to enhance vehicle-tovehicle (V2V) communication in dynamic vehicular Ad-hoc networks (VANETs). Existing routing protocols, such as Ad-hoc on-demand distance vector (AODV) protocols, face significant challenges, including low data transfer rates, higher delay times, lower throughput, and data congestion resulting from rapidly changing network topologies. LAAODV addresses these issues by optimizing the quality of service (QoS) through the real-time selection of relay nodes based on vehicle speed, distance, and actual position parameters. Simulations were conducted at the Gadjah Mada university (UGM) roundabout in Yogyakarta, Indonesia, using SUMO and NS3 simulators. LA-AODV outperforms AODV with Packet Delivery Ratios ranging from 95% to 99% and Average Throughputs between 36.90 Kbps and 56.50 Kbps. Although LA-AODV exhibits slightly higher End-to-End Delays, it effectively mitigates Packet Loss Ratios ranging from 1% to 4%. These enhancements optimize routing decisions, reduce communication overhead, and enhance network resource utilization. [ABSTRACT FROM AUTHOR]

Smart city project is today a domain of interest to community research which play well-known role in road traffic management. Data exchange became complicated in terms of capacity in the intelligent transport system (ITS), and without the raise of big data, the treatment is very difficult to manage. vehicular ad-hoc network (VANETs) faces many challenges mainly the voluminous data generated by different actors of VANET environment. We propose a real time anomalies detection system in an instantaneous way with parallel data treatment. The system method intends to compute precisely vehicle density at each section on each road, which help to handle the traffic and forward to vehicles information about the road and the best safe path to reach their destination. Also, we build anomalies prediction system based on machine learning framework, it is a good solution for avoiding traffic congestion and limiting the risk of accidents. The simulation results demonstrate that the proposed system method reduces congestion greatly by taking into account the load balancing and therefore avoids saturation and reduces accidents. It should also be noted that the results obtained show that the system is characterized by low latency and high accuracy. [ABSTRACT FROM AUTHOR]

Intelligent Transportation System (ITS) heavily relies on the unique mobile ad-hoc network (MANET) known as the vehicular ad-hoc network (VANET). The convenience that the Location Based Service (LBS), security issues arising from VANETs' great portability. Among the most widely used privacy-preserving techniques, distributed k-anonymity does not consider users' reliability, which results in hostile vehicle tracking. Therefore, Blockchain-enabled, Trust and Location dependent-Privacy Preserving (BTLB-PP) authentication system in VANET to overcome issues. Trust Management (TM) based on Dirichlet distribution, client and member will only collaborate with vehicles they confide by explicitly examining various prerequisites of claimed vehicle and collaborative vehicle during construction of unidentified obfuscation region and integrating attributes of these two functions. Using blockchain, a data structure has been proposed to promptly register trust of vehicles on publicly available blocks, allowing any vehicle to retrieve. In the trials, tests on various datasets have been run. Suggested system is resistant to multiple trust model threats, improving the security of vehicles' confidentiality and privacy. The performance evaluation metrics are precision, recall, f-measure and false positive rate (FPR) are used to evaluate the proposed system. Results from the simulation show that the proposed method is efficient and practical in reality. The suggested method was tested in a simulated traffic situation to verify its effectiveness.

Summary: Vehicular Ad‐hoc Network (VANET) is a vehicle‐to‐vehicle communication technology, which is highly promising due to its continuous demand, applicability, and suitability for public safety. The vehicular nodes establish communication with each other for exchanging traffic data and road maps, especially when warning messages need to be broadcasted on the network during emergency situations. Therefore, accurate vehicle information is necessary in such circumstances to improve the accuracy of Non‐Line‐of‐Sight (NLOS) node localization. In this paper, a Driving Training‐Based Optimization for NLOS Node Localization (DTO‐NL) Scheme is proposed for facilitating better warning message delivery during emergency situations in vehicular networks through a dynamic increase and decrease in the search based on the position of unknown NLOS nodes from the considered anchor nodes employed for localization. The performance of the suggested technique has been analyzed based on parameters like warning message delivery rate, latency, localization error, channel utilization rate, energy consumption, and packet delivery ratio. The proposed DTO‐NL scheme enhances the warning message delivery rate by 9.81%, 11.28%, 14.38%, and 17.64% compared to WIDVBA , ROA‐APS , ILL‐WOA‐NLOS , and WDHPBDS techniques. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANETs) is a promising technology that is used in the maximum of the applications of intelligent transport systems (ITS). VANETs become more attractive due to their communication methods such as vehicle-to-vehicle (V2V) and vehicle-to-roadside unit (RSU) communication. VANETs consist of a few special features such as unpredictable mobility, dynamic intervehicle spacing, high speed and so on which make communication ineffective. These features network delay and routing overhead increased which affects the stability and reliability of the network. In this paper Path Scheduling and Bandwidth Utilization for VANETs (PSBU-VANETs) are proposed. Through the path scheduling process, the changing topologies are predicted that the prediction path is scheduled for data transmission which leads to reduce the delay and overhead of the network. Through the effective utilization of bandwidth, the throughput and delivery rate of the network are increased. The simulation is performed in NS2 and SUMO and to measure the outcome the parameters which are considered are packet delivery ratio, end-to-end delay, routing overhead, and throughput. To perform a comparative analysis the results of the proposed PSBU-VANETs are compared with the earlier research works such as TDG-VANETs and ICB-VANETs. The proposed PSBU-VANETs achieve a high packet delivery ratio and throughput as well as lower end-to-end delay and routing overhead when compared with the earlier approaches. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks (VANETs) are the primary setting for smart driving, providing road information and instantaneous vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication between vehicles. Nevertheless, providing a VANET system with low computational costs and great serving capacities is critical. However, the efficiency of the whole VANET is significantly reduced since vehicles must re-authenticate with the current roadside unit (RSU) whenever they enter a new RSU coverage region. Handover authentication for VANETs has been the subject of many recent studies, notably secure V2V or V2I communications. We examine the security of recently proposed handover authentication methods for VANETs because it is an emerging and developing field. This study attempts to get an in-depth comprehension of handover authentication mechanisms for VANETs. We have considered Son et al., Maria et al., and Xie et al.'s schemes for our study. These protocols improve compatibility and give vehicles and RSUs a more straightforward way to send messages. However, we have discovered a fundamental security flaw in these systems that leaves them open to attacks, including replay and impersonation. Additionally, to develop a trade-off between efficiency and security, we propose a comparison based on execution cost and security qualities. [ABSTRACT FROM AUTHOR]

In recent years, the vehicular ad-hoc network (VANET), which is an ad-hoc network used by connected autonomous vehicles (CAV) for information processing, has attracted the interest of researchers in order to meet the needs created by the accelerating development of autonomous vehicle technology. The enormous amount of information and the high speed of the vehicles require us to have a very reliable communication protocol. The objective of this paper is to determine a topology-based routing protocol that improves network performance and guarantees information traffic over VANET. This comparative study was carried out using the simulation of urban mobility (SUMO) and network simulator (NS-3). Through the results obtained, we will show that the choice of the type of protocol to use depends on the size of the network and also on the metrics to be optimized. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs), as the most significant element of the Intelligent Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety by making the transportation system smarter and are still at the initial point of development. In this paper, we propose an ensemble-based machine learning model for network traffic prediction in VANET. We take advantage of Ensemble Learning (EL), which combines different Machine Learning (ML) models to achieve better performance and improve accuracy. We consider the most informative attributes of the VANET dataset using Boruta and LightGBM as ensemble feature selection methods. Our proposed model is based on Stacking Ensemble Learning with Booster Model (STK–EBM) designed with a stacking ensemble of heterogeneous ML models. The framework of the proposed model consists of two layers, including a base layer and a meta layer. The first layer integrates Random Forest (RF), K-Nearest Neighbor (KNN) and XGBoost as a booster of the base learners. An optimized Logistic Regression (LR) employs as our meta learner in the second layer. We evaluate the performance of our model considering classification metrics and then compare it with the most popular traffic predictive models. Simulation results show that the STK–EBM model gives a more stable prediction than the single algorithm, as well as better overall performance in terms of prediction accuracy and execution time.

Packet-based simulation is a key tool for the research and development of Vehicular Ad-hoc Networks (VANETs). Over the last decade, many models throughout the communication stack have been presented, which have increased the degree of realism that can be achieved with popular simulation frameworks. Nevertheless, the three-dimensional aspects of many real-world traffic situations barely find consideration. In this paper, we present a holistic approach to simulate large-scale three-dimensional VANET scenarios. We briefly summarize our previously presented models covering different aspects of communication in 3D scenarios, including an environmental diffraction model, an n-ray ground interference model, and the consideration of multi-floor communication. We then describe the principle of a model selector, which applies the appropriate models depending on the environment of the currently transmitted packet. Subsequently, we use the outlined methodology implemented in our Veins 3D framework to simulate a large urban reference scenario. The results differ significantly from comparable 2D simulations, demonstrating the necessity of three-dimensional considerations. However, they also show strongly increased execution times. Therefore, we further suggest different approaches to improve the simulation performance. Based on these optimizations, simulation durations in the same order of magnitude as a comparable 2D simulation can be achieved.

Intelligent Transportation System (ITS) provides services for proper traffic assistance. ITS helps in creating a transportation system that is smart, safe and efficient. Vehicular Ad-hoc Network supplies internet connectivity to vehicles and helps in traffic guidance. This paper uses a modified transformer architecture for time-series vehicular data to predict traffic flow. Time-series sequences are generated from the dataset for capturing temporal dependencies. Our proposed transformer-based model has been engineered to capture inter-feature correlations along with inter-sample correlations. The 2D-Transformers model has a significant decrease in error compared with Transformers and LSTM-based models. The prediction generated from the model can be transmitted throughout a network of vehicles. So, a holistic networking model is proposed where the vehicles will be connected to Road-side Units (RSUs) and the backbone network will be Software Defined Network (SDN). The traditional design principles, that incorporate data, control and management planes together in a network device, are incapable to adapt with this much data growth, bandwidth, speed, security, and scalability compared to SDN as it provides with centralized programmable mechanism reliably. The trained parameters learned using the transformer model can be passed throughout the network for traffic guidance.

Innovative framework on Vehicles in Network Simulation (VEINS) for Vehicular Ad-hoc Network (VANET) that use security aspect is mainly limited and dispersed. In order to offer valuable visions for technical settings and researchers, the study looked into the trends and gaps that were currently present. As a result, this systematic literature review was carried out to develop a comprehensive taxonomy of the research landscape. A thorough study was done for papers about (a) VANET, (b) VEINS, and (C) security aspects. This research used three databases, namely IEEE Xplore, ScienceDirect, and Scopus. These databases included in-depth research focused on VANET based on the VEINS framework. Then, on the basis of the security aspect, filtering was accomplished. The first class includes threats and vulnerabilities that evaluate the effects of threats and vulnerabilities on VANETs by using the VEINS framework and suggest ways to mitigate or lessen their effects. The second category includes articles on the solution technology that uses blockchain, machine learning, and Software-Defined Networking (SDN) techniques in VEINS-based VANET applications. The third class comprises the requirements that satisfy privacy, authentication, trust management, reliability, and revocation of the VANET security-based VEINS framework. Finally, this paper reviews the architecture and bidirectional coupling of the VEINS framework.

Intersections form a significant part of an urban area and are the nuclei of congestion. In this regard, traffic management in un-signalized intersections is a considerable challenge because the unorganized passage of vehicles may lead to accidents, traffic jams, or even deadlocks. This can also increase the average waiting time for vehicles. In this research, a context-aware mechanism (CATMI) is proposed to calculate the priority of vehicles for passing the intersection. To this end, multi-attribute decision-making is utilized, which obtains a formula based on the effectiveness of the contributed contextual elements. Based on the priority, a vehicle is either granted or denied to cross the intersection. In this scheme, traffic management is accomplished such that deadlocks and starvations are prevented. The simulation result of the CATMI mechanism is compared with the results of previous traffic control systems. The results indicate that at intersections with various input rates, CATMI reduces the delay in most scenarios. [ABSTRACT FROM AUTHOR]

Agriculture is essential for global livelihoods and economic stability, making efficient water management crucial. The Internet of Things (IoT) has revolutionized smart farming, offering advanced solutions such as precision irrigation systems. This paper presents an intelligent and automatic irrigation system that integrates IoT with vehicular ad-hoc networks (VANETs), with a particular focus on authentication and key agreement features. The system employs an energy-aware fuzzy routing algorithm and a neural network to optimize irrigation strategies based on real-time sensor data, including temperature and soil moisture. Key agreement mechanisms within the VANET framework ensure secure and authenticated communication between devices, safeguarding the integrity and confidentiality of irrigation data and control commands. This integration allows users to manage and monitor the irrigation system through mobile devices such as smartphones and computers, providing real-time insights and control. The proposed system is designed to be cost-effective, portable, and adaptable to various agricultural environments, including farms and greenhouses. Simulation results demonstrate that our system not only enhances network lifetime and power efficiency but also offers superior security and key management compared to existing protocols like WSN-IoT and LEACH. This approach addresses critical challenges in both secure communication and efficient water management, presenting a significant advancement in smart irrigation technologies. [ABSTRACT FROM AUTHOR]

With the rapid development of Telematics, the role of edge computing (Mobile Edge Computing, MEC) is becoming more and more significant. Mobile users can obtain massive computing and storage resources in a local way, thus effectively solving the congestion problem of the core network. However, in general urban edge network scenarios of VANET (Vehicular Ad-hoc Network), due to low node density and poor connectivity, there are few chances to encounter suitable forwarding nodes. In order to avoid the above situation and adapt to sparse scenarios, we propose new link-stabilized routing method & protocol based on sticky bacteria algorithm in this paper. The new idea and the significant findings of this proposed algorithm in enhancing the routing protocol is as follows: five factors affecting routing decision are identified: evaluation distance, deflection angle, number of neighboring nodes, rate difference, and the traffic of the road section at which it is located, and then these five factors are comprehensively evaluated by using the our designed Analytic Hierarchy Process (AHP) strategy, so as to determine the score of each node and the candidate routing paths; And the best routing path between two communicating nodes is found through the stronger global exploration capability of the sticky bacteria algorithm. Our experiments (that are based on our developed C++ programs) have proved that the method proposed in this paper has stronger link stability and the highest packet delivery rate. And the experiments and their results has enhanced our new method credibility in the field of ad hoc networks. [ABSTRACT FROM AUTHOR]

Continuous dynamic in vehicle movement creates a complex topology environment and connectivity relationship in Vehicular Ad-hoc Networks (VANETs). Consequently. overcoming longer transmission connections and reduced transmission reliability has been a significant problem. During the development of routing methods, fundamental research focuses on identifying and selecting paths with short distances or pulverization of information in environments with high traffic density to transmit packets successfully. As a result. transmission losses are minimized. and transport conditions are avoided: however, these techniques struggle to achieve more stable path routes in real-time environments because of sudden increases in traffic levels. Consequently, a model that effectively monitors the distance between vehicles and assists VANETs in selecting paths automatically and more accurately is proposed. To approximate and extend the lifetime of the communication path. we use an OpenAl Gym environment where a Reinforcement Learning (RL) agent can learn the route using the distance between cars as a model and define policies with higher lifetime rewards. The algorithm used a trained agent with the Proximal Policy Optimization (PPO) and Advanced Actor-Critic (A20 algorithms in our environment. Applying machine learning on VANETs results in improved network efficiency, which offers real-time routing information in mobile environments. The simulation results show that RL-based routing extends the useful life of the route among the investigated methods between the origin and destination hosts. [ABSTRACT FROM AUTHOR]

A software-defined vehicular network is made up of an IoT (Internet of Things) based vehicular ad-hoc network and a software-defined network. For better communication in IoT based vehicle networks, researchers are now working on the VANET (Vehicular Ad-hoc Network) to increase the overall system performance. To maximize the VANET ad-hoc network’s information application performance and reliability, edge computing has gained the attention of researchers. In current research, cloud computing is used for message related task execution, which increases the response time. We propose a Software-defined Fault Tolerance and QoS-Aware (Quality of Service) IoT-Based Vehicular Networks Using Edge Computing Secured by Blockchain to reduce overall communication delay, message failure fault tolerance, and secure service provisioning for VANET ad-hoc networks in this article. We proposed heuristic algorithms to solve the above mentioned problems of response delay, message failure, fault tolerance, and security provided by the Blockchain. The proposed model gets vehicle messages through SDN (Software defined network) nodes, which are placed on nearby edge servers, and the edge servers are validated by the blockchain to provide secure services to vehicles. The SDN controller, which exists on an edge server, which is placed on the road side to overcome communication delays, receives different messages from the vehicles and divides these messages in to two different categories. The message division is performed by the edge server by judging the time line, size, and emergency situation. SDN controller organized these messages and forwarded them to their destination. After the message is delivered to its destination, a fault tolerance mechanism checks their acknowledgements. If the message delivery fails, the fault tolerance algorithm will resend the failure message. The proposed model is implemented using a custom simulator and compared with the latest VANET based QoS and fault tolerance models. The result shows the performance of the proposed model, which decreased the overall message communication delay by 55% of the normal and emergency messages by using the edge server SDN controller. Furthermore, the proposed model reduces the execution time, security risk, and message failure ratio by using the edge server, cloud server and blockchain infrastructure.

Today, Vehicular Ad-hoc Networks (VANET) have become an interesting research topic for developing Intelligent Transport Systems. In urban environments, vehicles move continuously and at different speeds, which leads to frequent changes in the network topology. The main issue faced in an urban scenario is the performance of routing protocols when delivering data from one vehicle to another. This paper introduces ECRDP, an Efficient Clustering Routing approach using a new clustering algorithm based on Density Peaks Clustering (DPC) and Particle Swarm Optimization (PSO). First, the PSO algorithm is applied to determine the cluster heads, or a new fitness function for finding the best solutions is formulated using the DPC algorithm. Next, clustering is performed based on the reliability of links parameter between vehicles. Then, a maintenance phase is proposed to update the cluster heads and redistribute the vehicles in the clusters. Finally, the effectiveness of the suggested scheme is evaluated by a simulation operated by MATLAB on a real urban scenario. The results achieved show an overall increase in stability, proven by a reduction in change rate by 74%, and an improvement in performance indicated by an increase in intra-cluster throughput by 34% and inter-cluster by 47%, as well as an overall reduction of average delay by 16%.

During the last years, the volume of data produced in smart cities has been growing up, which can cause network traffic. Some of the challenges in an Intelligent Transportation System (ITS) are predicting the network traffic with the highest accuracy, keeping the security of data and being less complex. Artificial Intelligence (AI) algorithms are advantageous solutions to predict, control and avoid network traffic. However, such algorithms brought some costs to the privacy field. Accordingly, besides having an accurate prediction, preserving the privacy of data is an important challenge that should be considered. To cope with this problem, we propose a Federated learning algorithm for Network Traffic Prediction (Fed-NTP) based on Long Short-Term Memory (LSTM) algorithm to train the model locally, which can predict the network traffic flow accurately while preserving privacy. We implement the LSTM algorithm in a decentralized way by using the federate learning (FL) algorithm on the Vehicular Ad-Hoc Network (VANET) dataset and predict network traffic based on the most influential features of network traffic flow in the road and network. Simulation results reveal that the proposed model besides preserving the privacy of data, takes an obvious advantage over other well-known AI algorithms in terms of errors in prediction and the highest $R^{2}-SCORE$ (0.975).

Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are commonly used for message integrity and authentication of vehicles. However, cryptograhpic techniques alone may not be sufficient to protect against insider attacks. Many VANET safety applications rely on periodic broadcast of basic safety messages (BSMs) from surrounding vehicles that contain important status information about a vehicle such as its position, speed, and heading. If an attacker (misbehaving vehicle) injects false position information in a BSM, it can lead to serious consequences including traffic congestion or even accidents. Therefore, it is imperative to accurately detect and identify such attackers to ensure safety in the network. This paper presents a novel data-centric approach to detect position falsification attacks, using machine learning (ML) algorithms. Unlike existing techniques, the proposed approach combines information from 2 consecutive BSMs for training and testing. Simulations using the Vehicular Reference Misbehavior (VeReMi) dataset demonstrate that the proposed model clearly outperforms existing approaches for identifying a range of different attack types.

Nowadays, video streaming applications are becoming one of the tendencies driving vehicular network users. In this work, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different vehicles included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the "Dynamic Vehicular Clustering" (DVC) algorithm as a new scheme for video streaming systems over vehicular ad-hoc networks (VANET). The proposed algorithm takes advantage of the small cells concept and the introduction of wireless backhauls, inspired by the different features and the performance of the Long Term Evolution (LTE)-Advanced network. Vehicles are clustered together to form dynamically ad-hoc sub-networks included in the vehicular network. The goal of our clustering algorithm is to take into account several characteristics, such as the vehicle's position and acceleration to reduce latency and packet loss. Therefore, each cluster is counted as a small cell containing vehicular nodes and an access point that is elected regarding some particular specifications. Based on the exceptional features of the LTE-Advanced network (small cells and wireless backhauls) the DVC algorithm is a promising scheme for video streaming services over VANET systems. Experiments were carried out with a virtual topology of the VANET network created with four clusters to implement the DVC algorithm. The results were compared with other algorithms such as Virtual Trust-ability Data transmission (VTD), Named Data Networking (NDN), and Socially Aware Security Message Forwarding (SASMF). Our algorithm can effectively improve the transmission rate of data packets at the expense of a slight increase in end-to-end delay and control overhead. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles framework largely relies on Vehicular Ad-hoc Network (VANET) to achieve the vision of connected, smart cars with mobility as a core component. Since deploying and testing a VANET in real-time is expensive, simulations are an essential tool as part of research on vehicular communication. Moreover, including an appropriate mobility model for a successful simulation of VANET is quite challenging because of its less realistic nature. This paper presents a survey on vehicular mobility models, emphasizing the realistic nature of vehicular movements and their corresponding challenges. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITS), the vehicular ad-hoc network (VANET) is an enabling technology that can provide information exchange services among connected and autonomous vehicles (CAVs). Video streaming over VANETs is a potential application to ensure the safety of drivers and passengers and improve infotainment services. However, owing to the dynamic network topology, video transmission in VANETs is very challenging in terms of latency, reliability, and security. Therefore, a comprehensive summary of the state-of-art video streaming over VANETs is surveyed in this work. Firstly, related works and background knowledge are introduced. Then, a systematic survey on resource allocation (RA) scheme for video streaming in VANETs is provided, and some prevailing and feasible optimization tools are elaborated. Furthermore, enabling technologies of video streaming over VANETs are summarized with a special focus on the integration of video communication, caching, and computing. Finally, we give some challenges and future research directions. [ABSTRACT FROM AUTHOR]

Broadcasting on Vehicular Ad Hoc Networks (VANET) is quickly becoming a hot topic. One of the complications presented by this topic is that it is limited to Vehicle-to-Vehicle (V2V) situations rather than employing communications networks (such as broadband connections). Intelligent Transportation Systems (ITS) are primarily focused on the distribution of emergency messages as well as basic information. The IEEE has recognized Wireless Access in Vehicular Environment (WAVE) as the standard for this message distribution. IEEE 802.11p and Dedicated Short Range Communication (DSRC) are two Medium Access Control (MAC) layer protocols supported by this WAVE. At high vehicle density scenarios, the increasing rate of broadcasting will slow down the performance of the broadcasting method and increase the packet loss ratio. In this paper, various broadcasting algorithms are compared in terms of Quality of Service (QoS) to analyze the best algorithm which achieves high packet delivery ratio and improve the performance at high vehicle density scenario. A new broadcasting algorithm Traffic Condition Aware Customized Beacon Broadcasting (TCA-CBB) method is introduced and compared with other methods. At the end of the performance evaluation between the broadcasting methods, it is proved that the proposed TCA-CBB algorithms shows best results. [ABSTRACT FROM AUTHOR]