ENHANCED ENSEMBLE DEEP LEARNING FRAMEWORK FOR OPTIMIZED COMMUNICATION IN VEHICULAR AD HOC NETWORKS

A vehicular ad hoc network (VANET) is a special kind of mobile ad hoc network built on top of the IEEE802.11p standard for a better adaptability to the wireless mobile environment. As it is used for both supporting vehicle-to-vehicle (V2V) as well as vehicle-to-infrastructure (V2I) communications, and connecting vehicles to external resources including cloud services, Internet, and user devices while improving the road traffic conditions, VANET is a Key component of intelligent transportation systems (ITS). As such, VANET can be exposed to cyber attacks related to the wireless environment, and those of traditional information technologies systems it is connected to. However, when looking at solutions that have been proposed to address VANET security issues, it emerges that guaranteeing security in VANET essentially amounts to resorting to cryptographic-centric mechanisms. Although the use of public key Infrastructure (PKI) fulfills most VANET’ security requirements related to physical properties of the wireless transmissions, simply relying on cryptography does not secure a network. This is the case for vulnerabilities at layers above the MAC layer. Because of their capability to bypass security policy control, they can still expose VANET, and thus, the ITS to cyber attacks. Thereby, one needs security solutions that go beyond cryptographic mechanisms in order cover multiple threat vectors faced by VANET. In this paper focusing on attack detection, we show how using an implementation combining observation of events and incidents from multiple sources at different layers Sybil nodes can be detected regardless of the VANET architecture.

Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.

Vehicular Ad hoc Network (VANET) is considered as an essential component of Intelligent Transport system. VANET has gained an ample amount of attention from the researchers and automobile industry. Security and privacy are the primary requirements in the successful deployment of vehicular communication in VANET. Lack of security and confidentiality in VANETs is the primary barricade in the successful deployment of VANET. To establish trust within the entities participating in VANET operations is the primary aim of VANET security and play a vital role in prevention of attack in VANET. This paper describes a comprehensive identity authentication scheme (CIAS) based on asymmetric encryption that facilitates the authentication for Vehicle-to-Infrastructure (V2I) and inter RSUs. The proposed scheme is validated by extensive simulations and compared with the related works on the basis of communication overhead (CO), Latency and packet delivery ratio (PDR). The result of simulations shows that proposed authentication scheme outperforms.

Intelligent Transportation Systems (ITS) is an integrated approach being developed to exchange relevant information to increase the safety and efficiency of the road transportation systems. Vehicular Ad hoc Network (VANET), a variant of mobile ad hoc networks (MANET), is a core component of ITS. Performance of this smart ITS mainly owes to the design of efficient routing protocols in VANETs. Distinct features of VANETs like unsteady connectivity, high mobility and partitioning of the network have made routing of the information in VANETs difficult and challenging, hence dictating the development of efficient routing protocols. The computation of the best route measures the performance of communication whereas routing protocols takes care of communication & routing of the data. Provision of smart communication, necessitates the analysis of routing protocols in VANET. Accordingly in this paper we have reviewed various types of existing routing protocols in VANET, listing their advantages and disadvantages. Lastly possible inclinations of forthcoming research linked to VANET routing are discussed.

Since importance of improving of Intelligent Transportation System (ITS) always follow modern trends by using new wireless communication technologies, the trend of latest research topics is focusing on Vehicular Ad Hoc Network (VANET). VANET networks play a vital role in ITS due to their increasing importance for the building of ITS. VANET is a subclass of mobile ad-hoc networks (MANET). VANET depends on wireless technologies to establish communication between moving vehicles (nodes). An appropriate and efficient routing protocol helps to successful exchange data between mobility nodes in vehicular ad-hoc networks. VANET has a lot of similar features to MANETs such as finite bandwidth, self-arrangement, self-administration, and unstable network topology. Except it has some important features of its characteristic such as very high node mobility, delay restrictions, and frequent network outages. For this reason, routing in VANET networks is much more complex than routing in MANET networks. The purpose of this study - to evaluate the performance of protocols AODV (Ad hoc On-Demand Distance Vector) and DSR (Dynamic Source Routing) and their impact on the performance of networks VANET. This paper differs in that it analyses the impact of network size at a large number of nodes, and different vehicle speeds on network performance metrics like packet delivery ratio, throughput, average delay, overhead and packet loss ratio and assessing the level of network performance at realistic mobility scenarios for the movement of vehicles in the street generated by Bonnmotion tool. Also, the simulation is carried out in NS-3 simulator to create VANET network topology and routing protocols.

The evolution of the Internet of Things (IoT) facilitates the emergence of the Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS). A crucial component of ITS is the vehicular ad hoc network (VANET) featuring smart vehicles (SV). This study introduces a dynamic traffic regulation method within VANET utilizing Deep Neural Networks (DNN) and Bat Algorithms (BA). The DNN is employed to direct vehicles through highly congested routes, thereby minimizing average delays and enhancing efficiency. To assess traffic congestion among network nodes, the BA is integrated with the IoT and implemented over VANETs. Experiments were carried out to evaluate the proposed method's effectiveness based on various parameters, including packet delivery ratio, average latency, and throughput, with results compared against several machine learning (ML) algorithms. The simulation outcomes indicate that the proposed approach effectively manages real-time traffic conditions while consuming less energy and incurring lower delays compared to existing techniques.

Nowadays, the construction of efficient intelligent transportation system (ITS) has become a new trend for metropolitan cities with increasingly large populations. As one of the most significant component of ITS, the vehicular ad hoc networks (VANETs) are capable of building temporary vehicular sensor networks for efficient and dynamic information exchange between vehicles and road side units (RSUs). As a matter of fact, the traditional VANETs have limited computing and storing capabilities, which restrict the rapid development VANETs services provided to the drivers. Hence, with the rapid development of big data facilities, the cloud-assisted VANETs structure is proposed in order to enhance the capabilities of VANETs. In addition, due to the inherent wireless communication characteristics, data transmissions of VANETs suffer from charted and uncharted security risks and attacks. Thus proper security strategies should be adopted to guarantee secure communication and driver privacy. Emphasizing on the above issues, we develop an efficient cloud-assisted certificateless grouping authentication scheme for VANETs. In our design, vehicle anonymity is provided during the entire communication process. Note that most of the current authenticating schemes assume the secure channel between the RSU and vehicles in order for initial key message transmission, which is not necessary in our scheme.

The intelligent transportation system (ITS), a worldwide initiative program utilizes novel information and communication technology for transport infrastructure and vehicles. Among extensive ITS components, efficient communication system is the most important role which connects numerous vehicles with roadside infrastructure and management center in the ITS program. Vehicular ad hoc network (VANET) has been a cornerstone of the envisioned ITS without infrastructure. However, multihop data delivery through VANET is difficult by the fact that vehicular networks are highly mobile and frequently disconnected. Thus, traditional ad hoc routing protocols are not well suited for these high dynamic network. In this paper we propose a new heterogeneous vehicular network (HVN) architecture and a mobility pattern aware routing for HVN. HVN integrates wireless metropolitan area network (WMAN) with VANET technology and reserves advantages of better coverage in WMAN and high data rate in VANET. Vehicles in HVN can communicate with each other and access Internet ubiquitously. We mainly focus on the routing issue for HVN, because the routing protocol for HVN is different from those used in MANET or VANET. With the WMAN infrastructure support in HVN, the mobility pattern aware routing protocol can adopt the whole information of the vehicular network and unique characteristics of vehicle mobility to establish a reliable route path in high dynamic vehicular network.

Over the past few decades, Intelligent Transportation System (ITS) has become a vital and extensive element of daily human life and activity. Vehicular Ad hoc Networks (VANETs) have become the most promising components of ITS, which promises to enhance transport efficiency, passenger safety, and comfort by exchanging traffic and infotainment information to intelligent vehicles. Moreover, VANETs have emerged with new paradigms (e.g., Cloud, SDN (Software-Defined Networking), Fog computing, Blockchain, and AI (Artificial Intelligence) techniques) to provide strategic and secure communications to increase their reliability. Therefore, efficient and robust mechanisms, such as trust management, are essential requirements in VANETs. This survey provides an extensive overview of the VANET and trust management’s main concepts. After that, we briefly review existing surveys, followed by the significant challenges of security and trust in VANETs. Then, we identify, review, classify, summarize, and compare related approaches. Finally, we give some future research directions.

As an important infrastructure of intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) provide navigation and positioning, information services, driver assistance, in-vehicle entertainment, and other functions for the owners improving the development of the future smart city. Due to the high dynamic nature of VANETs, choosing a relatively stable and reliable transmission path to reduce the link breakage is obviously very important. In this paper, we propose an efficient prediction-based data forwarding strategy to ensure the reliability and efficiency of data transmission in VANETs. Besides the link lifetime prediction, we introduce a new metric named link utility used in the process of forwarder selection, in order to improve data forwarding efficiency. Link utility reflects the impact of relative velocity and distance between the vehicles on the efficiency of intervehicle data transmissions. It can be used to optimize packet routing and minimize number of hops especially in the high-density VANETs. Finally, we evaluate the performance of the proposed forwarding strategy through simulation confirming its feasibility and effectiveness.

Vehicular Ad hoc Networks (VANETs) have become a cornerstone component of Intelligent Transportation Systems (ITS). VANET applications present a huge potential for improving road safety and travel comfort, hence the growing interest of both academia and industry. The main advantage of VANETs is its ad hoc nature which does not require fixed infrastructure or centralized administration. However, designing scalable information dissemination techniques for VANET applications remains a challenging task due to the inherent nature of such highly dynamic environments. Existing dissemination techniques often resort to simulation for performance evaluation and there are only few studies that offer mathematical modeling. In this paper we provide a comparative study of existing performance modeling approaches for data dissemination techniques designed for different VANET applications.

With the advent of new technologies, such as software-defined networks (SDN) and fog computing, and the development of communication technology and the vehicular industry, there has been a remarkable growth in intelligent transportation systems (ITS) in recent years. As a main component of ITS, vehicular ad hoc networks (VANETs) suffer from a host of problems, including architectural inflexibility, wireless communication instability, limited range of communications, and frequent topological changes due to the rapid mobility of vehicles. One of the challenges in vehicular ad hoc networks is the lack of efficient and reliable communication between vehicles. Also, given the huge amounts of data production and big data, the communication capabilities of vehicles can be utilized to effectively transmit and communicate data, thus reducing excessive bandwidth usage in internet networks and their infrastructure. The current paper’s aim is to present a method for improving the transmission of data packets in inter-vehicle communications. In this regard, software-defined networks and fog-based computing offer substantial benefits. The proposed routing method includes the switching of data packet transmission through the VANET infrastructure and internet transmission. SDN- and Fog computing-based Switchable Routing (SFSR) provides the best path for the inter-vehicle transmission of data packets. When data packet transmission via VANETs is not possible, transmission may be performed via internet. The results of the simulation indicate improved performance of SFSR in terms of the packet delivery ratio, packet loss ratio, end-to-end delay, routing overhead, and routing failure rate.

The fast development of wireless communication technologies allows to enhance intelligent transport systems and utilizes these technologies in communications between vehicles in order to achieve road safety and others benefits to drivers. Recent studies focus on a new aspect of ITS (Intelligent Transport System) that is VANET (Vehicular Ad-hoc Network). This paper gives an overview of vehicular ad-hoc networks (VANETs) and the existing VANET routing protocols. VANETs are a group of moving vehicles with wireless communication. Every vehicle is a node in the network that plays the role of sender, receiver, and router to exchange data such as warnings, traffic conditions with other vehicles. Because of the high mobility of nodes in VANETs, the routing of data is the most challenging task in VANETs which have a dynamic topology. There are various types of routing protocols in VANET which are required by the ITS. Thus, we need to do a detailed research on various routing protocols and their strengths/weakness. The routing protocols basically focus on terms of delay, packet delivery ratio, bandwidth utilization and many other factors. However, there are challenges faced by the choose a proper routing protocol due to a dynamic topology and characteristics of VANETs. VANET is very advantageous as it helps in improving the road safety through reducing the number of accidents by warning drivers about the danger before they actually face it and other facilities to comfort drivers. Researchers have started working to overcome the challenges of data exchange between vehicles to minimize its constraints and maximize its multiple objectives.

Vehicular Ad hoc Network (VANET) are the promising approach to provide safety and other beneficial applications to the drivers as well as passengers. It is recognized as an essential component of intelligent transport system. In this paper, we have discussed about some security attacks in vehicular Ad hoc Network. We have also discussed about some solutions that can be implemented against these attacks. Life saving characteristics of VANET has attracted the industry and researchers. In VANET vehicles are nodes so they have no fixed infrastructure. It serves safe and non safe application in wireless medium which makes it venerable to several attacks. Keywords: VANET characteristics, Overview of attacks, Solutions.

Vehicular ad hoc network (VANET) is a special class of mobile ad hoc Networks (MANET), and is an important component of Intelligent Transportation System (ITS). VANET has a future potential in terms of a rich set of applications that it can provide to its customer. Accident detection is a key functionality of ITS. Due to the effects of channel fading and shadowing, individual vehicles may not be able to reliably detect the existence of an accident. In this paper, we propose a linear cooperation framework for accident detection in a cluster-based VANET. In this framework, accident detection is based on the linear combination of local statistics gathered by individual vehicles. Our objectives are minimizing the probability of false alarm and avoiding broadcast storm. Based on the problem formulation and derivation, we proposed two efficient algorithms to do alarm detection: local detection and global detection. The numerical results show that our heuristic strategies perform well on false alarm detection.