Vehicular Ad Hoc Networks Architecture Research Articles

Optimal model of vehicular ad-hoc network assisted by unmanned aerial vehicles and information-centric networking to enhance network performance

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.

DDoS Attack Detection in SDN-Based Instrumented Ad Hoc Networks

The physical characteristics of today's urban intersection structures and the traffic flows caused by unplanned road intersections lead to many negative effects such as time/cash loss, stress, increased fuel consumption, and more. For this reason, many studies are being conducted on traffic management systems, an application of smart cities, in both academic and commercial circles. In recent years, it has been observed that the VANET (Vehicular Ad Hoc Networks) architecture, which easily enables communication between vehicles or with devices on the side of the field, thus transporting relevant traffic data to the center, is frequently used in these studies. When Software Defined Networking emerged as a new technology, it brought many advantages such as high availability, scalability, and performance, but also introduced new security vulnerabilities targeted by attackers. This research primarily focuses on a resource-based detection approach by combining the powers of Software Defined Networking and s-Flow-RT technology against Distributed Denial of Service Attacks. In the simulation study conducted within the scope of this work, an SDN (Software Defined Networking)-based DDoS (Distributed Denial of Service) attack was carried out, and changes in data before and after the attack were examined. Traffic for the DDoS attack was generated with the Hping3 application. The RYU controller (a component-based software-defined networking framework) was selected as the software-defined network controller to create software-defined networks, and the Mininet emulator was used. A traditional computer network's Ubuntu virtual machine was used to carry out the attack in the scope of the work.

MVR Delay: A Queueing Based Routing Model for C-V2X Mode 4 in VANET’s

During the last few years, the demand for vehicular ad-hoc networks (VANET) has gained great attraction in the intelligent transport system. The VANET architecture includes several critical features, such as distributed networking and the rapidly changing network topology. Due to the significant characteristics of road safety, it has gained a great deal of interest in industry and academia to enhance the safety of the road transport system. Efficient message exchange between vehicles and roadside units is a tedious task in VANET. Several techniques have been introduced to improve communication, but efficient packet delivery and delay reduction are challenging issues. Currently, the VANET technique has evolved as a “vehicle-to-everything (V2X)” communication standard. In addition, the 3GPP standards have introduced a new communication standard as an alternative to the IEEE 802.11p system. In this new release of 3GPP, two new communication modes are introduced, mode 3 and mode 4. Mode 3 requires a cellular infrastructure, whereas mode 4 can operate without cellular coverage. In this work, we focus on C-V2X mode 4 communications and present a novel routing scheme to deal with hidden terminal problems. The proposed approach generates a virtual queueing model in which efficient channel selection is performed so that packet collision and interference can be reduced by maximizing the distance in the virtual queue model. The experimental results demonstrate that the average performance of the proposed approach is obtained as 0.9383, 0.09 s, and 0.0617 in terms of packet delivery, end-to-end delay, and packet drop rate, respectively.

Prioritization and scheduling in the vehicle-to-infrastructure (V2I) scheme for vanets using enhanced congestion control source based ant colony optimization

Mobile ad hoc networks (MANETs), which include vehicular ad hoc networks (VANETs), are a promising method for intelligent transportation systems. The major challenges of VANET protocols are symmetric links and changing topology of the networks and hence a proper routing method is necessary for VANET. A crucial issue with VANETs is the transmission of data from a starting point to the base station. To discover the least path with the lowest cost and fewest hops between the source and the destination, existing work uses a novel priority-based direction-aware collision avoidance (P-DVCA). To direct the ants to develop a shorter path with fewer hops for data transmission, the suggested method uses the prioritising and scheduling enhanced congestion control-based source ant colony optimization (ECC-ACO) for VANETs. Based on the message type and network situation, this technique provides precedence for privacy messages communications. Privacy messages can also be divided into beacon messages and occurrence messages. At that point, each message is routed into the appropriate queue predicated on its primary consideration, and each message in each queue is planned. When compared to renowned VANET communication architecture, simulation and analytical findings show that proposed ECC-ACO offers a respectable reduction in collisions.

Increasing Efficiency and Reliability in Multicast Routing based V2V Communication for Direction-Aware Cooperative Collision Avoidance

Mobile ad hoc networks (MANETs), which are a promising method for the intelligent transportation system, include vehicular ad hoc networks (VANETs) (ITS). Developing reliable and strong cooperative collision avoidance (CCA) strategy to mitigate the growing number of road fatalities each year is one of the main difficulties facing vehicular ad hoc networks (VANETs).A proper and successful routing method aids in the successful expansion of vehicular ad hoc networks. This study explains the architecture, interface layers, safety features, and implementation of a novel priority-based direction-aware collision avoidance system (P-DVCA). It distinguishes our study in the collision area of VANETs by accounting for realistic bi-directional traffic. The scheme begins with the development of dynamic clusters, which is difficult because of the bi-directional diverse traffic and the need to avoid collisions within and between clusters. The target node is sent an early warning message that includes the safe speed and the likelihood of a collision in order to notify it of an impending danger. To determine the least expensive, shortest one with the fewest hops between the source and the endpoint. A crucial problem with VANETs is the transmission of data from a source node to the base station. Cross-layer issues must be solved for a robust and stable collision avoidance programme to function properly in a VANET communication architecture. The results of the simulation show that the suggested scheme significantly outperforms CCM and C-RACCA in terms of cluster stability, fewer collisions, low latency, and low communication overhead. According to the findings, P-DVCA offers stable clustering, minimises network congestion, and lowers communication overhead and latency.

Smart dynamic evacuation planning and online management using vehicular communication system

AbstractDuring disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for destination choice and a dynamic traffic assignment (DTA) for path choice toward the chosen destinations to obtain an initial evacuation plan. The initial plan is then enhanced by employing a vehicular ad hoc network (VANET) within the vehicular edge computing (VEC) architecture. This enhancement enables communication among evacuees, allowing them to revise their vehicle's route choice and planned destination. These revisions take into account the changing risk and traffic conditions. The proposed online DPE framework is applied to the real evacuation scenario of Mill Valley City, CA. The proposed model is evaluated with different VANET architectures, including vehicular cloud computing (VCC) and VEC. The results show that the VEC framework outperforms other configurations and improves the evacuation process compared to the scenario with an initial plan by more than 30% in network clearance time. Additionally, a performance analysis is carried out for evacuation scenarios with different penetration rates of connected vehicles in VANET.

Design and Modelling of hybrid network security method for increasing security in vehicular ad-hoc network

Vehicular Ad Hoc Networks (VANETs) are becoming more commonplace as a means for cars to communicate and share data on things like traffic patterns, road conditions, and travel times and speeds. Therefore, one of the key issues facing academics now is ensuring data communication safety in VANET. There are several privacy-preserving verification techniques for VANETs. However, they do have complex calculations and safety issues. This research presented an operating platform for the 5G-based VANET architecture that combines Software-Defined Networking (SDN) with Self-Organizing Maps (SOM). The proposed system provides SOM and SDN-based network (SOM-SDN) solutions that will improve safety in two dimensions by spotting and stopping threats. First, this research examines the network's efficiency threats while considering Distributed Denial of Service (DDoS) threats. The suggested system's safety was then compared with current DDoS attacks. Additionally, the proposed method is strong enough to fend off typical assaults and maintain the communication information's secrecy, thanks to an examination of its security qualities. The efficiency comparisons' outcomes demonstrate the suggested procedure's light and effectiveness. Additionally, the suggested model using SUMO and NS-3 demonstrates how effective and useful the method is for VANETs. The proposed study shows a 92% improvement in performance, a 4% reduction in end-to-end latency, a 92% improvement in transmission outbound, a 98% improvement in packet transmission rate, and a 94% improvement in the enumeration of bounce compared to prior studies.

A multi-dimensional trust model for misbehavior detection in vehicular ad hoc networks

Communication security is one of the focus issues of vehicular ad hoc networks (VANETs). Trust model is commonly used to identify malicious attackers or filter false messages in the network, which effectively guarantees secure communication between vehicles. Constrained by the distributed network architecture of VANETs, trust evaluation results may be biased due to uneven data distribution and lack of reference. By colluding with the partners, malicious vehicle launches imbalance attack and zigzag attack to bypass the detection of the trust model. To address these new intelligent attacks, we propose a multi-dimensional trust model (MDT) in this paper, which makes a combined evaluation of different trust attributes from vehicles. In MDT, data collection and trust calculation are deployed in vehicles and trust authority (TA), respectively. Since the model dynamically adjusts the corresponding weight coefficient according to the data fluctuations of each trust indicator by adopting entropy weight method and filters anomalous evaluation results by using the median absolute deviation (MAD) algorithm, it can effectively deal with complex and variable intelligent attacks. The legitimate identity of a vehicle will be revoked from the network by TA once its trust value is less than the threshold. We have conducted extensive experimental studies to evaluate the performance of MDT. The experimental results show that the MDT model can accurately and effectively calculate the global trust of vehicles and detect malicious attacks in the network under different scenarios and tests.

Flexible handover solution for vehicular ad-hoc networks based on software defined networking and fog computing

<p>Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and fog computing (FC) technologies. The SDN provides global knowledge, programmability and intelligence functions for simplified and efficient network operation and management. FC, on the other hand, alleviates the core network pressure by providing real time computation and transmission functionalities at edge network to maintain the demands of delay sensitive applications. The proposed solution overcomes frequent handover challenges and reduces the processing overhead at core network. Moreover, the simulation evaluation shows significant handover performance improvement of the proposed solution compared to current SDN based schemes, especially in terms of handover latency and packet loss ratio under various simulation environments.</p>

Privacy-Preserving Deep Learning Model for Decentralized VANETs Using Fully Homomorphic Encryption and Blockchain

In Vehicular Ad-hoc Networks (VANETs), privacy protection and data security during network transmission and data analysis have attracted attention. In this paper, we apply deep learning, blockchain, and fully homomorphic encryption (FHE) technologies in VANETs and propose a Decentralized Privacy-preserving Deep Learning (DPDL) model. We propose a Decentralized VANETs (DVANETs) architecture, where computing tasks are decomposed from centralized cloud services to edge computing (EC) nodes, thereby effectively reducing network communication overhead and congestion delay. We use blockchain to establish a secure and trusted data communication mechanism among vehicles, roadside units, and EC nodes. In addition, we propose a DPDL model to provide privacy-preserving data analysis for DVANET, where the FHE algorithm is used to encrypt the transportation data on each EC node and input it into the local DPDL models, thereby effectively protecting the privacy and credibility of vehicles. Moreover, we further use blockchain to provide a decentralized and trusted DPDL model update mechanism, where the parameters of each local DPDL model are stored in the blockchain for sharing with other distributed models. In this way, all distributed models can update their models in a credible and asynchronous manner, avoiding possible threats and attacks. Extensive simulations are conducted to evaluate the effectiveness, practicality, and robustness of the proposed DVANET system and DPDL models.

A Survey on Mobile Road Side Units in VANETs

The number of vehicles on the road increases daily, causing many fatal accidents and wasting much time for the average commuter every day due to congestion. Vehicular ad hoc networks (VANETs) were introduced to overcome these issues by enabling vehicle-to-vehicle communication and vehicle-to-infrastructure communication. The prime challenge in VANETs is the necessity of very low communication delays, especially for safety-related applications due to the high mobility nature of vehicles. The VANET architecture introduces a network component, the Road Side Unit (RSU), to meet the required delay limitations. Even though the RSU is a critical component in VANETs, as expected, the RSUs were not deployed throughout the world because of their high investment cost. As a solution, the idea of mobile RSU (mRSU) was introduced, and, ever since, several techniques of mRSU deployment strategies have been proposed. In this survey, we first analyze the importance of the RSU to the VANET architecture with real-world data incorporating the new 5G standard. Then, we investigate the research done in the areas of mRSU and exploit the pros and cons of each mRSU deployment strategy. Finally, we also discuss the future research directions of mRSU, and we explain the challenges connected to these future trends.

A quality of service‐aware high‐security architecture design for software‐defined network powered vehicular ad‐hoc networks using machine learning‐based blockchain routing

AbstractSoftware‐defined networks (SDNs) have proven to be highly flexible for vehicular ad‐hoc network (VANET) routing. This is due to packet‐level and bandwidth‐level controls available with SDNs. But SDNs have inherent security issues which include forwarding device attacks, control plane threats, communication channel vulnerability, etc. To counter these attacks, this paper initially proposes a proof‐of‐work‐based blockchain framework that can reduce attack probability by 99%. But, the overall network quality of service (QoS) reduces in terms of end‐to‐end delay, energy consumption, and throughput. To improve these parameters and maintain a high level of security, this work proposes a machine‐learning algorithm that optimizes blockchain parameters in a QoS‐aware manner. These parameters include selected chain length, encryption block length, etc. The machine learning algorithm is inspired by Q‐Learning and aims at reducing the performance effect of blockchain operations on the overall QoS of VANETs. Thus, the underlying network uses sidechain‐based blockchain implementation, which makes it faster, more secure, and less complex when compared with single‐chain implementations. Upon observing the results, it is seen that the proposed architecture can reduce the end‐to‐end delay by 15%, energy consumption by 18%, and increase overall throughput by 38% when compared with existing blockchain‐based SDN VANET architectures.

Vehicular multihop intelligent transportation framework for effective communication in vehicular ad‐hoc networks

AbstractIn the smart city, the presence of intelligent transportation systems (ITS) is much essential. The emerging vehicular ad‐hoc networks (VANET) technology offers a wide range of applications in transportation, mobility, connectivity, and safety applications. In this decade, vehicular traffic is a prime hassle. The actual‐instant traffic data to drivers along with navigation system would facilitate vehicles to select an excellent path. This article proposes hybrid VANET architecture for effective communication between vehicles. The proposed hybrid architecture uses the multihop routing algorithm to permit vehicles to enlarge their driving performance and road safety. The proposed hybrid vehicular multihop routing algorithm with intelligent transportation system (Hybrid VMR‐ITS) allows the transportation system for instantaneous communications between vehicles and road‐side units (RSUs) and vehicles‐traffic servers effectively. The proposed work enhanced the whole spatial usage of a road network by reducing average vehicle travel costs with a reduced traffic overhead of 4%–9% compared with existing works.

Lightweight VANET Architecture for Efficient Secure Data Transmission using CPABE & IBOOS

Cloud computing is a network access concept that provides simple, on-demand network access to a shared pool of customizable computing resources (such as networks, servers, storage, applications, and services) that can be quickly provided and released with minimal administrative effort or service provider involvement. The idea of having a communication node on-board a vehicle that may make wireless connections with other adjacent communication nodes visible in the radio range is referred to as a Vehicular Adhoc Network (VANET). The main focus of this paper is to design lightweight Vehicular Adhoc Network architecture for efficient secure data transmission. An improved Ciphertext Policy Attribute Based Encryption scheme and Identity Based offline/online Signature scheme are proposed to reduce the computational cost, storage overhead and communication overhead in vehicular ad-hoc network. Finally, concluded that the proposed work yields high performance than the existing work.

Security in Vehicular Ad Hoc Networks: Challenges and Countermeasures

Recently, vehicular ad hoc networks (VANETs) got much popularity and are now being considered as integral parts of the automobile industry. As a subclass of MANETs, the VANETs are being used in the intelligent transport system (ITS) to support passengers, vehicles, and facilities like road protection, including misadventure warnings and driver succor, along with other infotainment services. The advantages and comforts of VANETs are obvious; however, with the continuous progression in autonomous automobile technologies, VANETs are facing numerous security challenges including DoS, Sybil, impersonation, replay, and related attacks. This paper discusses the characteristics and security issues including attacks and threats at different protocol layers of the VANETs architecture. Moreover, the paper also surveys different countermeasures.

Towards the Design of Efficient and Secure Architecture for Software-Defined Vehicular Networks.

Recently, by the rapid development of Vehicular Ad Hoc Networks (VANETs) and the advancement of Software Defined Networking (SDN) as an emerging technology, the Software-Defined Vehicular Network (SDVN) has a tremendous attraction in the academia and research community. SDN’s unique properties and features, such as its flexibility, programmability, and centralized control, make the network scalable and straightforward. In VANETs, traffic management and secure communication of vehicle information using the public network are the main research dimensions in the current era for the researchers to be considered while designing an efficient and secure VANETs architecture. This paper highlights the possible identified threat vectors and efficiently resolves the network vulnerabilities to design a novel and secure hierarchic architecture for SDVN. To solve the above problem, we proposed a Public Key Infrastructure-based digital signature model for efficient and secure communication from Vehicle to Vehicle. We also used the public key authority infrastructure for Vehicle to Infrastructure and the three-way handshake method for secure session creation and secure data communication in the SDN controller. The proposed security is validated through the well-known simulation tool AVISPA. Additionally, a formal security model is applied to validate the design hierarchic architecture’s fundamental security properties for SDVN in an efficient and desirable way. In a comparative analysis, we prove that our proposed scheme fulfills all the essential security properties compared to other states of the art schemes.

MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET

The wireless nature of the Vehicular Ad Hoc Network (VANET), a technology that offers facilities such as traffic management and safety services, makes it vulnerable to distributed denial‐of‐service (DDoS) attacks that exploit network communications and reduce network reliability and performance. This paper proposes a design of a secure VANET architecture using a Software‐Defined Networking (SDN) controller and Neural Network Self‐Organizing Maps (SOMs). In the proposed design, we adopt the SDN architecture by using its separation of the control plane from the data plane and adding intelligent capabilities to the VANET. To resolve the drawbacks of standard SOMs and to enhance the SOM’s efficiency, a Multilayer Distributed SOM (MSOM) model based on two levels of clustering and classification is used. Experimental results show that our solution can efficiently detect malicious traffic, prevent and mitigate DDoS attacks, and increase system security and recovery speed from the attacking traffic. Moreover, the proposed scheme achieves a high accuracy rate (99.67%). Simulation results demonstrate the effectiveness and efficiency of the MSOM regarding detection accuracy and other studied metrics.

Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ETX$ </tex-math></inline-formula> ) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.

DT-VAR: Decision Tree Predicted Compatibility-Based Vehicular Ad-Hoc Reliable Routing

Reliable routing and efficient message delivery in vehicular ad-hoc networks (VANETs) is a significant challenge owing to underlying environment constraints, such as dynamic nature, mobility, and limited connectivity. With the increasing number of machine learning (ML) applications in wireless networks, VANETs can benefit from these data-driven predictions. In this letter, we innovate and investigate ML-based classifications in VANETs to predict the most suitable path with the longest compatibility time and trust using a fog node based VANET architecture. The proposed scheme in SUMO VANET traces achieves up to a 16% packet delivery ratio (PDR) with a 99% accuracy and longer connectivity with only 3 ~ 4 hops, compared with existing AOMDV and TCSR solutions with merely a 4% PDR.

Volunteer Computing in Connected Vehicles: Opportunities and Challenges

With the exponential increase in the number of smart devices and the proliferation of the Internet of Things (IoT) over the last two decades, a huge demand for computational and storage resources has been observed. This trend has supported a vision toward exploring a novel set of resources beyond the traditional distributed computing paradigms. To deliver this vision, vehicular ad-hoc networks (VANETs) have garnered a great deal of attention due to a number of vehicles on the roads that can be served as the prospective computational resources in the form of processing and storage units. Given the perspective of resources not being used collectively, we propose volunteer computing based VANET (VCBV) architecture, aimed at utilizing the surplus vehicular resources to deal with the future computational requirements of varied applications including autonomous driving, infotainment systems, healthcare services, and complex simulations. In this context, this study investigates the different aspects of possible integration of VANET and volunteer computing (VC). For this purpose, we describe the system model of VCBV by identifying the major components and their roles. Furthermore, a comprehensive taxonomic classification of VCBV is presented. Moreover, some prospective applications and multiple VCBV scenarios are also envisioned. Finally, a set of challenges is indicated that must be addressed by future studies to fully realize the potential of the proposed architecture.