VANET Environment Research Articles

Detection of Sybil & DDoS attacks in VANET using intelligent technique

ABSTRACT This paper proposes a novel fuzzy logic controller (FLC) based model referred to as FBM to detect both the DDoS and the Sybil attacks in VANET using an intelligent fuzzy logic controller. A VANET environment is created with different numbers of attackers to check the efficacy of the proposed model using veins, which is used for simulation with an event-based network simulator OMNeT++ and a road traffic simulator SUMO. Furthermore, the performance of the proposed model is compared with a few other machine learning techniques, such as logistic regression (LR), the decision tree (DT), random forest classifier (RF), and the support vector machine (SVM). The proposed FBM model yields better performance than the existing machine-learning techniques. The proposed FBM performs better than RF in accuracy by 0.10492%, precision by 0.08065%, recall by 0.02505%, and F1 score by 0.088243% for the DDoS scenario. It is also observed that the FBM performs better than DT in accuracy by 3.91486%, precision by 14.39994%, and F1 score by 3.819618% for the Sybil scenario. The margin of error for the proposed FBM is also estimated for 95% of the confidence interval and is found to be 0.021691 for the DDoS scenario.

Enhancing Road Safety: Machine Learning-Driven Vehicle Speed Monitoring and Alerting in VANET Environments-A Review

Inexperienced and fast driving poses a significant threat to the safety of innocent people, resulting in severe automobile accidents. Presently, most efforts have been made in detecting the driver behavior, as traditional methods show limited success the researchers have delved into the machine learning and the deep learning methods for predicting the vehicle speed and as well as altering. This review explores at the manner in which machine learning and the deep learning can be used to improve road safety using Vehicle Ad-hoc Networks. The primary objective revolves around a Machine Learning-Driven Vehicle Speed Monitoring and Alerting System, which is intended to reduce the dangers associated with variable speeds in VANET environments. The paper reviews the existing research, approaches, and breakthroughs in the use of machine learning algorithms for real-time vehicle speed monitoring. This analysis intends to provide insights into the emerging environment of intelligent transportation systems, with a focus on the role of artificial intelligence in identifying and responding to potential risks. It presents an in-depth review of the challenges, opportunities, and future prospects for using machine learning to improve road safety within the VANETs.

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

An Adaptive Contention Window using Actor-Critic Reinforcement Learning Algorithm for Vehicular Ad-hoc NETworks

The MAC layer IEEE 802.11p standard’s collision avoidance mechanism is sub-optimal due to the use of a Binary Exponential Backoff (BEB). This technique has an increase in the backoff slot time to minimize the probability of subsequent collisions after the occurrence of a collision. This leads to a degradation of radio bandwidth wastage, especially when managing access to a dense number of stations. An incorrect backoff setting can also increase the likelihood of collisions and decrease network performance. For adaptive CW selection, the proposed model is based on a Reinforcement Learning (RL) Actor-Critic (AC) network to adapt the CW value by learning the VANET environment. NS-3 for VANET is utilized to test and compute the optimal CW values. The proposed AC and EAC outperform traditional BEB used in the IEEE 802.11p standard as the number of vehicles increases. The throughput is enhanced by 54% and 69% respectively for AC and Enhanced AC (EAC). Similarly, the end-to-end delay for AC is reduced by 19%, and for EAC it’s reduced by 25%.

Machine Learning-Based Risk-Aware Congestion Control Scheme for Minimization of Information Loss in Dense VANET Environment

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified [Formula: see text] -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique.

Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs

AbstractVehicular Adhoc Networks (VANET) facilitate inter‐vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing‐based Secure Protocol for VANET Environment (SC‐SPVE) method, which aims to tackle security challenges. The SC‐SPVE technique integrates an adaptive neuro‐fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC‐SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2.

Detection of Network Layer Attacks in Wireless Sensor Network

The Wireless Sensor Network (WSN) technology is being used in a huge number of monitoring applications. It consists of a large number of sensor nodes with limited battery life. These sensor devices are deployed randomly in a sensor zone to collect the data. But these are threatened and attacked by several malicious behaviors caused by some nodes, which result in security attacks. Several security attacks occur in different layers of the wireless sensor network. Due to these attacks, confidential information can be stolen by attackers or unauthorized users, which can cause several problems for authorized users. Cyber-attacks by sending large data packets that deplete computer network service resources by using multiple computers when attacking are called wormhole and Sybil attacks. It is important to identify these attacks to prevent further damage. To overcome these problems, we use a prediction module that consists of various machine learning algorithms to find the best-performing algorithm. we use XGBoost, Adaboost, Random Forest, and KNN algorithms. To train these algorithms, we have used the WHASA dataset which contains 10 different attacks of the VANET environment and benign (normal) class. By using these algorithms classification of attacks can be done which occur on the computer network service that is " normal " access or access under " attack " by Wormhole and Sybil attack as an output.

UAV-assisted ubiquitous communication architecture for urban VANET environment

UAV-assisted ubiquitous communication architecture for urban VANET environment

Embedded Federated Learning for VANET Environments

In the scope of smart cities, the sensors scattered throughout the city generate information that supplies intelligence mechanisms to learn the city’s mobility patterns. These patterns are used in machine learning (ML) applications, such as traffic estimation, that allow for improvement in the quality of experience in the city. Owing to the Internet-of-Things (IoT) evolution, the city’s monitoring points are always growing, and the transmission of the mass of data generated from edge devices to the cloud, required by centralized ML solutions, brings great challenges in terms of communication, thus negatively impacting the response time and, consequently, compromising the reaction in improving the flow of vehicles. In addition, when moving between the edge and the cloud, data are exposed, compromising privacy. Federated learning (FL) has emerged as an option for these challenges: (1) It has lower latency and communication overhead when performing most of the processing on the edge devices; (2) it improves privacy, as data do not travel over the network; and (3) it facilitates the handling of heterogeneous data sources and expands scalability. To assess how FL can effectively contribute to smart city scenarios, we present an FL framework, for which we built a testbed that integrated the components of the city infrastructure, where edge devices such as NVIDIA Jetson were connected to a cloud server. We deployed our lightweight container-based FL framework in this testbed, and we evaluated the performance of devices, the effectiveness of ML and aggregation algorithms, the impact on the communication between the edge and the server, and the consumption of resources. To carry out the evaluation, we opted for a scenario in which we estimated vehicle mobility inside and outside the city, using real data collected by the Aveiro Tech City Living Lab communication and sensing infrastructure in the city of Aveiro, Portugal.

Data Security Enhancement in 4G Vehicular Networks Based on Reinforcement Learning for Satellite Edge Computing

The vehicular network provides the dedicated short-range communication (DSRC) with IEEE 802.11p standard. The VANET model comprises of cellular vehicle-to-everything communication with wireless communication technology. Vehicular Edge Computing exhibits the promising technology to provide promising Intelligent Transport System Services. Smart application and urban computing. Satellite edge computing model is adopted in vehicular networks to provide services to the VANET communication for the management of computational resources for the end-users to provide access to low latency services for maximal execution of service. The satellite edge computing model implemented with the 4G vehicular communication network model subjected to data security issues. This paper presented a Route Computation Deep Learning Model (RCDL) to improve security in VANET communication with 4G technology. The RCDL model uses the route establishment model with the optimal route selection. The compute route is transmitted with the cryptographic scheme model for the selection of optimal route identified from the satellite edge computing model. The proposed RCDL scheme uses the deep learning-based reinforcement learning scheme for the attack prevention in the VANET environment employed with the 4G technology communication model. The simulation results expressed that proposed RCDL model achieves the higher PDR value of 98% which is ~6% higher than the existing model. The estimation of end-to-end delay is minimal for the RCDL scheme and improves the VANET communication.

PERFORMANCE ANALYSIS ON GPSR PROTOCOL IN VANETS ENVIRONMENT OF OVERLAY NETWORK AND FORWARDING NODE SELECTION METHODS

VANETs are technology in Intelligent Transport Systems (ITS) in driving safety and convenient network services for vehicle users. However, the high mobility of VANETs makes the topology change frequently, resulting in the unavailability of routes and causing communication failures between nodes. Greedy Perimeter Stateless Routing (GPSR) is a routing protocol that solves communication problems in the VANETs environment. GPSR is a geographic routing protocol that uses information on the location of neighbouring nodes and the transmission range to the nearest destination node. But, GPSR does not always find the optimal route because not all nearby nodes can forward packets to their destination during heavy traffic and many intersections. In this research, the overlay network and forwarding node selection methods will be used to solve problems in the GPSR routing protocol related to communication instability because of changing node positions. Based on the results of testing and analysis that has been carried out on a grid scenario with 50 nodes, the Packet Delivery Ratio results are 32.60. In contrast, the results for the real scenario with the same nodes produce a value of 64. The analysis of End to End Delay in the 50-node grid scenario produces 5.64, while the real scenario results with 50 nodes yield the value of 24.11. The analysis Routing Overhead in the grid scenario with 50 nodes produces a value of 39,978, while the results of the real scenario with 50 nodes get a value of 10,239. With the result of these two analysis methods, it is expected to increase the average value of package delivery.

UPSO-FSVRNET: Fuzzy Identification Approach in a VANET Environment Based on Fuzzy Support Vector Regression and Unified Particle Swarm Optimization

UPSO-FSVRNET: Fuzzy Identification Approach in a VANET Environment Based on Fuzzy Support Vector Regression and Unified Particle Swarm Optimization

A cryptographic paradigm to detect and mitigate blackhole attack in VANET environments

A cryptographic paradigm to detect and mitigate blackhole attack in VANET environments

A Simulation Approach to Improve the VANETs Communication

VANET ("vehicular ad hoc network") is a type of networks that consist of many vehicles acting as moving nodes. They are connected with other vehicles through an ad hoc wireless network so as to increase traffic security and provide relaxation to road users. Sending the messages to the final destination in VANETs is a challenging mission because of its relatively high mobility and dynamism. The clustering technique addresses such issues, as it gathers vehicles based upon several predefined metrics such us density, speed, and physical vehicles locations. Clustering in VANET is one of the controller techniques for dynamic form. In this paper, a new method is presented for clustering that suits the VANET environment with the purpose of improving the network cluster stability. This method takes a number of parameters into consideration, such as the coverage area and speed to make the cluster structure comparatively stable. In addition, and advance is obtained in tems of the cluster-head selections algorithm, and the data exchange is improved through clusters.

A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis

High vehicle mobility, changing vehicle density and dynamic inter-vehicle spacing are all important issues in the VANET environment. As a result, a better routing protocol improves VANET overall performance by permitting frequent service availability. Therefore, an ensemble-based machine-learning technique is used to forecast VANET mobility. Effective routing based on a hybrid metaheuristic algorithm combined with Ensemble Learning yields significantly improved results. Based on information collected from the Road Side Unit (RSU) or the Base Station, a hybrid metaheuristic (Seagull optimization and Artificial Fish Swarm Optimization) method is used to estimate (BS). The suggested approach incorporates an ensemble machine learning and hybrid metaheuristic method to reduce the latency. The current model's execution is calculated using a variety of Machine Learning techniques, including SVM, Nave Bayes, ANN, and Decision Tree. As a result, the performance of machine learning algorithms may be studied and used to achieve the best results. Comparative analysis between the proposed method (HFSA-VANET) and (CRSM-VANET was done on different performance parameters like throughput, delay, drop, network lifetime, and energy consumption to assess system performance on two factors Speed and Nodes. The HFSA-VANET method shows an overall drop in the delay of 33% and a decrease in the energy consumption of 81% and an increase of 8% in the throughput as compared with the CRSM-VANET method at 80 node. The proposed method that is HFSA-VANET has been implemented in the MATLAB and NS2 environment.

Traffic offloading model in a multilevel edge computing system for autonomous vehicles

Research subject is autonomous control networks for unmanned vehicles. Research method – study of the main functional elements and their locations for MEC, Fog technologies; studying the types of interaction between devices in vehicle networks; identification of the main problems arising in vehicle networks. Main results – MEC, V2X technologies are described in detail; considered a system that will be implemented using edge computing, and will also support ultra-low latency and high density for unmanned vehicles; a model was created for the VANET environment, demonstrating signal processing at two levels of edge computing. Practical significance – The presented architecture will reduce the load on the core network and ensure the provision of ultra-low transmission delays, which can also contribute to the introduction of unmanned vehicle networks into
 mass use in the future. The proposed architecture can be used as part of the «Smart City» concept. It is also worth noting that a multi-level system, due to its closer location to the user, will be more effective in forming a Fog cluster than with a standard network architecture, when a remote server will be used to form a cluster.

Exploiting trust, influence, and physical proximity for improving routing performance in vehicular ad hoc networks: The SCALE protocol

SummaryVehicular ad hoc networks (VANETs) are receiving increased attention, and several routing protocols have been proposed already by the research community. In such a framework, social aspects and human mobility are important performance enablers, which however are mostly overlooked. In this work, we propose the socially aware CLWPR (SCALE) protocol, significantly enhancing cross‐layer weighted position‐based routing (CLWPR), a routing protocol for urban VANET environments, combining social properties such as trust, influence, and users' individual mobility patterns to its core design in order to support an efficient content dissemination scheme. The design principles of SCALE that rely on both online and offline social metrics quantifying relationships on social networking platforms and opportunistic contacts of nodes due to physical proximity, respectively, are presented, while nodes with close online and offline social relationship are favored as next forwarder nodes. Subsequently we provide the performance evaluation of SCALE against other routing protocols in distributed vehicular networks, employing representative urban scenarios with synthetic and real traffic. It is shown that the proposed approach presents improved performance in terms of Packet Delivery Ratio (PDR) and throughput when compared with other similar protocols by an average of 37% in scenarios with synthetic dataset and 58% with real dataset for PDR and by an average of 45% in scenarios with synthetic dataset and 61% with real dataset for throughput.

Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities

The future Intelligent Transport System "ITS" is one of the major challenges of the smart city. It requires fast and efficient communication between vehicles (vehicle-to-vehicle “V2V”), to ensure information exchange in order to improve safety, which reduces accidents and consequently save lives, hence the need of the Vehicular Ad Hoc Network “VANET”, which makes possible the inter-vehicle communication. This network is characterized by a variable topology. Therefore, MANET (Mobile Adhoc NETwork) routing protocols need a few tweaks to be available for the vehicle environment. In this paper, we start by exposing some works related to the evaluation of the most well-known protocols. After a comparative study, we deduce that the OLSR (Optimized Link State Routing) protocol outperforms other routing protocols in terms of End-to-End Delay (EED) and Packet Delivery Ratio (PDR). In addition, we note that the Greedy forwarding “GF” mechanism is suited for the VANET environment, which has been improved and called Greedy forwarding Advanced “GFA”, to overcome the stationary node problem. Our approach improves the OLSR protocol to be more suitable and efficient for VANET by introducing the GFA mechanism. Moreover, we compare our approach to the OLSR classic version. In this work, we use a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO (Simulation of Urban MObility). The trace files generated from SUMO are used for further simulation in NS-3 (Network Simulator) to validate our proposition. The simulation results are analyzed and discussed. Our approach performs best compared to OLSR in terms of EED and PDR, especially for dense traffic.

Multilayer Video Encoding for QoS Managing of Video Streaming in VANET Environment

Efficient delivery and maintenance of the quality of service (QoS) of audio/video streams transmitted over VANETs for mobile and heterogeneous nodes are one of the major challenges in the convergence of this network type and these services. In this context, we propose an inter-layer approach for multimedia stream transmission in a VANET environment (VSMENET). The main idea of our work is based on the dynamic adaptation of the transmission rate according to the physical rate available in the VANET. VSMENET is all about eliminating downtime during video playback by vehicle users. This involves adapting the quality of the video to the actual performance of the VANETs, intelligent encoding of video on the Road Side Units (RSU) side, and finally continuous maintenance of the calculation tasks on the RSU side and sufficient video data on the vehicle node side. Thus, we are interested in the process of evaluating the strict parameters of the VANETs, influencing the video transmission. For example, we propose, on the one hand, an architecture for intelligent data selection and good clock synchronization, and, on the other hand, efficient management of the availability and consumption of video data. We used the NetSim simulator to test the proposed approach performance. To this end, several algorithms such as OCLFEC, MAC, ShieldHEVC, and AntArmour have been implemented for such a performance comparison. Our work suggests that VSMENET is well concerning the average lifetime of the video packets and their delivery rate (more than 9% gain compared with other approaches).

Correction to: Implementation of Efficient Security Algorithm and Performance Improvement Through ODMRP Protocol in VANET Environment

Correction to: Implementation of Efficient Security Algorithm and Performance Improvement Through ODMRP Protocol in VANET Environment