Detecting Traffic Conditions Model Based On Clustering Nodes Situations In VANET

In the last decade, cooperative vehicular network has been one of the most studied areas for developing the intelligent transportation systems (ITS). It is considered as an important approach to share the periodic traffic situations over vehicular ad hoc networks (VANETs) to improve efficiency and safety over the road. However, there are a number of issues in exchanging traffic data over high mobility of VANET, such as broadcast storms, hidden nodes and network instability. This paper proposes a new model to detect the traffic conditions using clustering traffic situations that are gathered from the nodes (vehicles) in VANET. The model designs new principles of multi-level clustering to detect the traffic condition for road users. Our model (a) divides the situations of vehicles into clusters, (b) designs a set of metrics to get the correlations among vehicles and (c) detects the traffic condition in certain areas. These metrics are simulated using the network simulator environment (NS-3) to study the effectiveness of the model

Utilising in-vehicle information to detect traffic conditions in vehicular ad-hoc networks

In the last decade, cooperative vehicular network has been one of the most studied areas for developing the intelligent transportation systems (ITS). It is considered as an important approach to share the periodic traffic situations over vehicular ad hoc networks (VANETs) to improve efficiency and safety over the road. In addition to the uses of ITS, VANETs will contribute in service access, cooperative driving, entertainment and navigation for cars of the future. Vehicle to vehicle and vehicle to infrastructure communication are two distinct avenues that make possible efficient delivery of messages through direct wireless transmissions in traffic regions. Furthermore, promising quality of communication performance is desirable for a communication system composed mostly if roaming participants; such a system needs to be dynamic, flexible and infrastructure-less. Thus VANET architecture is a natural fit for ITS. However, there are a number of issues in exchanging traffic data over high mobility of VANET, such as broadcast storms, hidden nodes and network instability. Therefore, vehicular traffic efficiency applications have been investigated recently using VANET. This aspect of research is primarily concerned with increasing the traffic awareness over roads. In this thesis, a novel model, Efficient Traffic Conditions Detection (ETraCD) is proposed to detect the traffic conditions utilising vehicles’ characteristics and in-vehicles sensors information to evaluate traffic situations that are gathered from the nodes (vehicles) in VANET. The model revolves around the core idea to what extent we will be considering the traffic characteristics between groups of cars rather than individual cars. This does not concern the physical transmission of data but the data processing in the network. More precisely, vehicles are clustered into traffic groups based on the similarity of sensors’s data. ETraCD (a) divides the situations of vehicles into clusters, (b) designs a set of metrics to get the correlations among vehicles and (c) detects the traffic condition in certain areas. These approaches have been simulated in NS3 network simulator to investigate the performance of stability of the network, latency, and the accuracy of traffic situations detection. The proposed model applies V2V clustering paradigm for detecting traffic conditions, it has been implemented and its features investigated through simulation runs. It shows the benefit of using the vehicular sensors informations such as ABS, windscreen lights and so on based on V2V communication to provide an efficient traffic conclusion in urban environment. Experiments also show improved overall performance when compared to previous protocols

RSU based Joint Congestion-Intrusion Detection System in Vanets Using Deep Learning Technique

Vehicular Ad hoc Network (VANET) is a technology that makes it possible to provide many practical services in intelligent transportation systems, but it is also susceptible to several intrusion threats. Through the identification of unusual network behavior, intrusion detection systems (ID Ss) can reduce security vulnerabilities. However, rather than detecting anomalous network behaviors throughout the whole VANET, current IDS systems are only able to do so for local sub-networks. Hence there is a need for a Joint Congestion and Intrusion Detection System (JCIDS). We designed an JCICS model that can collect network data cooperatively from vehicles and Roadside Units (RSUs).This paper, proposes a new deep learning model to improve the performance of JCIDS by using k-means and a posterior detection based on coresets to improve the detection accuracy and eliminate the redundant messages. The efficacy of the current Recurrent Neural Network (RNN) and Honey badger Algorithm (HBA)on the fundamental AODV protocol is combined with the advantages of the JCIDS is suggested in this protocol. First, formation of clusters using vehicle’s mobility parameters like, velocity and distance to enhance route stability. Moreover, a vehicle will be chosen as Cluster Head with highest route stability. Second, the efficient intrusion detection is achieved with the consumption using RNN method. In the RNN, the optimal weighting factor is selected with the help of HBA. The RNN is performing efficient prediction with the assistance of HBA. The finest path for data dissemination is selected by choosing link lifetime, hop count and residual energy along the path.As a result, multimedia data streaming is improved network life time, in terms of reduced packet loss ratio and energy consumption as compared to existing DNN and SVM scheme for different node density and speed

Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page

Secure Authentication Mechanism for Cluster based Vehicular Adhoc Network (VANET): A Survey

Vehicular Ad Hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS) by facilitating communication between vehicles and infrastructure. This communication aims to enhance road safety, improve traffic efficiency, and enhance passenger comfort. The secure and reliable exchange of information is paramount to ensure the integrity and confidentiality of data, while the authentication of vehicles and messages is essential to prevent unauthorized access and malicious activities. This survey paper presents a comprehensive analysis of existing authentication mechanisms proposed for cluster-based VANETs. The strengths, weaknesses, and suitability of these mechanisms for various scenarios are carefully examined. Additionally, the integration of secure key management techniques is discussed to enhance the overall authentication process. Cluster-based VANETs are formed by dividing the network into smaller groups or clusters, with designated cluster heads comprising one or more vehicles. Furthermore, this paper identifies gaps in the existing literature through an exploration of previous surveys. Several schemes based on different methods are critically evaluated, considering factors such as throughput, detection rate, security, packet delivery ratio, and end-to-end delay. To provide optimal solutions for authentication in cluster-based VANETs, this paper highlights AI- and ML-based routing-based schemes. These approaches leverage artificial intelligence and machine learning techniques to enhance authentication within the cluster-based VANET network. Finally, this paper explores the open research challenges that exist in the realm of authentication for cluster-based Vehicular Adhoc Networks, shedding light on areas that require further investigation and development

The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

It's difficult to deal with the dynamic nature of VANETs and WSNs in a way that makes sense. Machine learning (ML) is a preferred method for dealing with this kind of dynamicity. It is possible to define machine learning (ML) as a way of dealing with heterogeneous data in order to get the most out of a network without involving humans in the process or teaching it anything. Several techniques for WSN and VANETs based on ML are covered in this study, which provides a fast overview of the main ML ideas. Open difficulties and challenges in quickly changing networks, as well as diverse algorithms in relation to ML models and methodologies, are also covered in the following sections. We've provided a list of some of the most popular machine learning (ML) approaches for you to consider. As a starting point for further research, this article provides an overview of the various ML techniques and their difficulties. This paper's comparative examination of current state-of-the-art ML applications in WSN and VANETs is outstanding