Vehicular Network Environment Research Articles

VAIDANSHH: Adaptive DDoS detection for heterogeneous hosts in vehicular environments

Vehicular networks are vulnerable to Distributed Denial of Service (DDoS), an extension of a Denial of Service (DoS) attack. The existing solutions for DDoS detection in vehicular networks use various Machine Learning (ML) algorithms. However, these algorithms are applicable only in a single layer in a vehicular network environment and are incapable of detecting DDoS dynamics for different layers of the network infrastructure. The recently reported attacks on transport networks reveal the fact that a research gap exists between the existing solutions and the multi-layer DDoS detection strategy requirements. Additionally, the majority of the current detection methods fail in the consideration of traffic heterogeneity and are not rate-adaptive, where both the mentioned parameters are important for an effective detection system.In this paper, we introduce a comprehensive ML-based Network Intrusion Detection System (NIDS) against DDoS attacks in vehicular networks. Our proposed NIDS combines a three-tier security model, traffic adaptivity, and heterogeneity traffic provisions. We call our model Vehicular Adaptive Intrusion Detection And Novel System for Heterogeneous Hosts (VAIDANSHH). As mentioned earlier, VAIDANSHH has a three-tier security system: at RSU's hardware, communication channel, and RSU application level. VAIDANSHH combines the Adaptive Alarming Module (AAM) and the Detection Module (DM) for data generation, collection of generated data, flow monitoring, pre-processing, and classification. We use the NS3 simulation tool for our experiments to generate synthetic data and apply ML with WEKA. We run a thorough set of experiments, which show that VAIDANSHH detects UDP flooding, a form of DDoS attack, with 99.9% accuracy within a very short time. We compare VAIDANSHH with other state-of-the-art models; the comparative analysis shows that VAIDANSHH is superior in terms of accuracy and its multi-tier workflow.

Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment

Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment

Joint energy and spectral optimization in Heterogeneous Vehicular Network

With the latest developments in both the automotive and communications industries, especially concerning the emerging 5G networks, IoV, and the adoption of Vehicle-to-Everything (V2X) connectivity, there has been a shift towards the establishment of Heterogeneous Vehicular Networks (HetVNets) environments. The rapid growth of data traffic and the drastic expansion of heterogeneous network infrastructure have resulted in a significant increase in energy consumption within wireless communication systems. Balancing energy efficiency and spectral efficiency has become a major challenge in Heterogeneous Vehicular networks, particularly concerning energy optimization, making the design of network systems considerably more challenging. Therefore, this paper attempts to optimize the energy utilized for each packet transmission, considering its stochastic nature and the optimized control parameters of two meta-heuristic algorithms-Particle Swarm Optimization and Artificial Bee Colony Optimization. The optimization process is executed using the Particle Bee Colony Swarm algorithm. Subsequently, a comparison is made with other proposed algorithms, namely LDOD, FO, RO, and MATO, in terms of energy efficiency and spectral efficiency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating a 30.32% increase in spectral efficiency and 73.25% increase in energy efficiency.

A metaheuristic MAC protocol for safety applications in cognitive vehicular networks

In a dynamic cognitive vehicular network (CVN) environment, ensuring reliable data delivery of safety messages (sm) is crucial. However, fast-moving vehicles and rapidly changing channel accessibility make sm broadcasting challenging. To address this issue, we propose a cooperative make-up technique, which leverages the 2-Hop Harris Hawks Optimization (HHO) algorithm to choose an optimal helper for sm retransmission in case of failed direct transmission. Our simulations using the Network Simulator version 2 with SUMO demonstrate that the proposed approach achieves cooperative communication within the tight 100ms sm broadcast time frame and outperforms contemporary CVN protocols regarding throughput and packet delivery ratio. Our results suggest that our approach holds great promise for reliable and efficient data delivery in CVNs.

FogJam: A Fog Service for Detecting Traffic Congestion in a Continuous Data Stream VANET

In a continuous data stream vehicular network environment, a Traffic Congestion Detection Service (TCDS) receives many periodic information to update and discover road segments with low speeds and high vehicular density. The use of Cloud computing to support the massive amounts of traffic data received from multiple vehicles significantly increases network traffic. Therefore, we propose FogJam, a Fog service to detect traffic congestion directly at the edge of the vehicular network. In the network edge, FogJam leverages sampling and clustering-based methods to reduce the traffic data stream transmitted by all vehicles on the network links to the Cloud. In the Cloud, FogJam is used as a macro-control of all vehicles geographic positions, acquiring traffic flow data to detect traffic congestion. We evaluate FogJam using OMNeT++, Veins and SUMO simulators. The results suggest that FogJam is highly accurate in detecting traffic congestion at a lower cost, even in a high vehicular density scenario. Furthermore, using clustering-based methods, FogJam is able to, on average, reduce the impact on network usage by approximately 70% compared to the sampling methods, while maintaining an acceptable level of congestion detection accuracy.

Dynamic mode selection and resource allocation approach for 5G-vehicle-to-everything (V2X) communication using asynchronous federated deep reinforcement learning method

5G vehicle-to-everything (V2X) connectivity is crucial to enable future complex vehicular networking environment for enabling intelligent transportation systems (ITS). But, for mission critical applications like safety applications, unreliable vehicle-to-vehicle (V2V) connections and heavy signaling overheads in centralized resource distribution methods are becoming key obstacles. This work discusses the popular optimization issue of the selection of transmission mode and the allocation of resources blocks for 5G-V2X communication scenario. The stated problem is conceived as a Markov decision-making mechanism, and a Decentralized Deep reinforcement Learning (DRL) algorithm is presented to optimize the aggregate potential in terms of channel capacity of vehicle-to-infrastructure users while fulfilling the latency and reliability constraints of V2V communication link sets. In addition, considering training limitation of local DRL models, a two-timed synchronous federated DRL algorithm is used for making system robust. Therefore, we use two-timescale asynchronous federated deep reinforcement learning algorithm, i.e. Large-scale model and small- scale model. In large-scale model a graph-centered vehicle clustering is done to form the cluster of neighboring vehicles on a large timescale, whereas in small timescale model the vehicles in the similar cluster are used to train using robust global asynchronous federated deep reinforcement learning algorithm. The effects of outage threshold and vehicular density with respect to the network performance is presented. The simulation findings have shown that the proposed work outperforms the other previous state of the art works. The overall preeminence and convergence of the proposed work is verified.

FMAA: A Flexible Signal Timing Method for An Isolated Intersection with Conflicting Traffic Flows

Optimizing traffic signal timing can effectively alleviate urban traffic congestion. However, most of the existing signal timing methods struggle to deal with conflicting traffic flows in heavy traffic; therefore, more effective methods are urgently required. In this paper, we propose a flexible signal timing method that combines all-red control and adaptive timing (FMAA) to deal with conflicting traffic flows at an isolated intersection. First, we consider a Vehicle-to-Infrastructure (V2I) communication-based vehicular network environment, in which fine-grained traffic information can be collected by Road Side Units (RSUs) and uploaded to a cloud server for designing signal timing methods. Second, the congestion degree of Conflict Area (CA) is defined and utilized to trigger all-red control in congested cases. Third, the tolerance degree of the Waiting Area (WA) is defined and utilized to perform adaptive timing in other cases. Finally, simulations were conducted using SUMO, and the proposed FMAA method performed better than the comparative methods in terms of average speed, waiting time, and congested vehicles, thus improving traffic efficiency at an isolated intersection with conflicting traffic flows.

Blockchain and deep learning based trust management for Internet of Vehicles

Internet of Vehicles (IoVs) works as the most advanced component of Intelligent Transportation Systems (ITSs). In IoVs, vehicles are able to communicate with nearby vehicles or RoadSide Units (RSUs). Traffic safety and efficiency can be provided by collecting and uploading real-time traffic information through vehicles, as well as broadcasting information by RSUs. However, there may be malicious vehicles in the network uploading false information, which will lead to serious traffic problems. To alleviate this problem, a trust management system based on blockchain technology is proposed in this paper. In this system, vehicles in the network firstly collect information about their surroundings and then upload valid information to nearby RSUs. To prevent malicious vehicles from uploading false messages, this paper designs a deep learning based verification model to calculate the trustworthiness of uploaded messages, and to further obtain the credibility scores of vehicles using the calculated results, and detect malicious vehicles accordingly. Moreover, a public blockchain framework is proposed and a Proof-Of-Trust (POT) consensus algorithm is designed. Vehicles are motivated to report true and valid information, and are penalized for uploading false data under this framework. Simulation results show that this mechanism can effectively detect malicious vehicles and motivate unfamiliar vehicles to upload true and reliable information to achieve trust management in the open and dynamic vehicular network environments.

Enhanced request adaptive scheme for information-centric vehicular networks

In this paper, we propose an enhanced request adaptive (EReA) scheme for content service provision in dynamic information-centric vehicular networking (ICVN) environment. The EReA scheme manages the cache update at the roadside units (RSUs) and provides low-latency content delivery to the end-users. User requests trigger the cache update at the RSU for a particular content item on the basis of its update−probability. We introduce an additional parameter tolerancei for each content item i. This parameter suppresses the frequency of content updates triggers at the RSU cache during high network load for the non-critical contents. This provides an additional saving in bandwidth. The simulation setup is created by ndnSIM 2.0 to validate the performance of this proposed scheme. The working and performance of the other content delivery schemes are also discussed in details. Results show that the proposed scheme provides a better hit-ratio compared to the existing ReA scheme under high network load, which results in an additional saving in bandwidth. Additionally, it also inherits the advantages of the ReA scheme.

Adaptive mobility-aware and reliable routing protocols for healthcare vehicular network.

Healthcare vehicles such as ambulances are the key drivers for digital and pervasive remote care for elderly patients. Thus, Healthcare Vehicular Ad Hoc Network (H-VANET) plays a vital role to empower the digital and Intelligent Transportation System (ITS) for the smart medical world. Quality of Service (QoS) performance of vehicular communication can be improved through the development of a robust routing protocol having enhanced reliability and scalability. One of the most important issues in vehicular technology is allowing drivers to make trustworthy decisions, therefore building an efficient routing protocol that maintains an appropriate level of Quality of Service is a difficult task. Restricted mobility, high vehicle speeds, and continually changing topologies characterize the vehicular network environment. This paper contributes in four ways. First, it introduces adaptive, mobility-aware, and reliable routing protocols. The optimization of two routing protocols which are based on changing nature topologies of the network used for vehicular networks has been performed, amongst them, Optimized Link State Routing (Proactive) and Ad-hoc on Demand Distance Vector (Reactive) are considered for Packet Delivery Ratio (PDR) and throughput. Furthermore, Packet Loss Ratio (PLR), and end-to-end (E2E) delay parameters have also been calculated. Second, a healthcare vehicle system architecture for elderly patients is proposed. Third, a Platoon-based System model for routing protocols in VANET is proposed. Fourth, a dynamic channel model has been proposed for the vehicle to vehicle communication using IEEE8011.p. To optimize the QoS, the experimental setup is conducted in a discrete Network Simulator (NS-3) environment. The results reveal that the AODV routing protocol gives better performance for PDR as well as for PLR and the communication link established is also reliable for throughput. Where OLSR produces a large average delay. The adoptive mobility-aware routing protocols are potential candidates for providing Intelligent Transportation Systems with acceptable mobility, high reliability, high PDR, low PLR, and low E2E delay.

An Application-Driven Framework for Intelligent Transportation Systems Using 5G Network Slicing

Vehicular networks are critical pieces in support of advanced intelligent transportation systems (ITS). These networks are formed by vehicles that can be connected to one another as well as to the infrastructure, and are subject to constant topology changes, disconnections, and data congestion. Each ITS application could have a different set of communication requirements, such as delay, bandwidth, and packet delivery ratio. Meeting these heterogeneous requirements in the complex dynamic environment of vehicular networks is a challenge. This paper develops a new framework for application-driven vehicular networks using 5G network slicing. We present the architecture of the proposed solution and design algorithms for heterogeneous traffic in a dynamic vehicular environment. Our simulations on realistic vehicular scenarios show significant improvements in network performance compared to the state-of-the-art approaches.

Network slicing for 5G edge services

In the current 5G technology domain network slicing already plays an important role as a critical enabler. An industry that 5G aims to disrupt is the vehicular one. In this paper the brief scope of the 5G‐DRIVE research project is presented, regarding 5G vehicular research between the EU and China. In the frame of 5G‐DRIVE a set of slicing mechanisms are investigated and evaluated in regard to their performance. Firstly, related to slicing mechanisms in the NFV domain the OSM orchestrator is measured in terms of scalability and performance. In the next experimental set related to RAN slicing, the Katana Slice Manager is evaluated and depicts how different slicing configurations can achieve different performance results. Furthermore, the paper showcases how 5G network slicing can be integrated as a key enabler to the stringent demands of a vehicular network environment. Finally, the paper concludes, setting future directions in the related field.

Computing Paradigms in Emerging Vehicular Environments: A Review

Determining how to structure vehicular network environments can be done in various ways. Here, we highlight vehicle networks' evolution from vehicular ad-hoc networks (VANET) to the internet of vehicles (IoVs), listing their benefits and limitations. We also highlight the reasons in adopting wireless technologies, in particular, IEEE 802.11p and 5G vehicle-to-everything, as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments. We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems' requirements. The presentation of each paradigm is given from a historical and logical standpoint. In particular, vehicular fog computing improves on the deficiences of vehicular cloud computing, so both are not exclusive from the application point of view. We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks, showing that they complement each other and share problems and limitations. As these networks still have many opportunities to grow in both concept and application, we finally discuss concepts and technologies that we believe are beneficial. Throughout this work, we emphasize the crucial role of these concepts for the well-being of humanity.

Corrections to “A Mutual Authentication Scheme for Secure Fog Computing Service Handover in Vehicular Network Environment”

In the above article [1] , there is an inconsistency in defining the equation $HV$ in the login phase and mutual authentication phase as shown in Figs. 3 and 5, respectively. In the Cloud Server $CS$ of Fig. 3, $HV$ is equal to $h\left(RV_{i} \oplus CID_{i} \| HS\right)$ in which $RV_{i} \oplus CID_{i}$ is equal to $h\left(VID_{i}\right)$ as described in Fig. 2 and $HS=h\left(S_{i, t}^{\prime}\right)$ as shown in the previous line of equation $HV=h\left(RV_{i} \oplus CID_{i} \| HS\right)$ . Then, in the User $U_{i}$ of Fig. 3, $V K_{x}=h\left(VID_{i}^{*} \| h\left(S_{i, t}^{\prime}\right)\right) \oplus K_{x}^{\prime}$ in which the value of $V K_{x}$ is equal to $HV \oplus K_{x}$ as derived in the Vehicle $V_{i}$ of Fig. 5. However, the $HV$ in the previous $V K_{x}$ of Fig. 3 is wrong, as the consequence, it should be $V K_{x}=h\left(h\left(VID_{i}^{*}\right) \| h\left(S_{i, t}^{\prime}\right)\right) \oplus K_{x}^{\prime}$ in order to make it consistent with the previous definition of $HV$ in the Cloud Server $CS$ of Fig. 3. Therefore, the $HV^{\prime}= h\left(VID_{i} \| HS^{\prime}\right)$ in the Vehicle $V_{i}$ of Fig. 5 should be modified into $HV^{\prime}=h\left(h\left(VID_{i}\right) \| HS^{\prime}\right)$ .

Toward Physical-Layer Security for Internet of Vehicles: Interference-Aware Modeling

The physical-layer security (PLS) of wireless networks has witnessed significant attention in next-generation communication systems due to its potential toward enabling protection at the signal level in dense network environments. The growing trends toward smart mobility via sensor-enabled vehicles are transforming today's traffic environment into Internet of Vehicles (IoVs). Enabling PLS for IoVs would be a significant development considering the dense vehicular network environment in the near future. In this context, this article presents a PLS framework for a vehicular network consisting a legitimate receiver and an eavesdropper, both under the effect of interfering vehicles. The double-Rayleigh fading channel is used to capture the effect of mobility within the communication channel. The performance is analyzed in terms of the average secrecy capacity (ASC) and secrecy outage probability (SOP). We present the standard expressions for the ASC and SOP in alternative forms, to facilitate analysis in terms of the respective moment generating function (MGF) and characteristic function of the joint fading and interferer statistics. Closed-form expressions for the MGFs and characteristic functions were obtained and Monte Carlo simulations were provided to validate the results. Approximate expressions for the ASC and SOP were also provided, for easier analysis and insight into the effect of the network parameters. The results attest that the performance of the considered system was affected by the number of interfering vehicles as well as their distances. It was also demonstrated that the system performance closely correlates with the uncertainty in the eavesdropper's vehicle location.

SMAKA: Secure Many-to-Many Authentication and Key Agreement Scheme for Vehicular Networks

With the rising popularity of the Internet and communication technology, vehicles can analyze and judge the real-time data collected by various cloud service providers (CSPs) in a vehicular network. However, in a vehicular network environment, real-time data are transmitted via wireless channels, which can lead to security and privacy issues. To avoid illegal access by adversaries, vehicle authentication and key agreement mechanism has been considered as one of the promising security measures in vehicular network environments. Besides, most of the solutions focus on authentication between one vehicle and one CSP. In such strategies, the implementation of efficient authentication for multiple vehicles and CSPs simultaneously is usually challenging. Further, they are also subjected to performance limitations due to the overhead incurred. To solve these issues, we propose a many-to-many authentication and key agreement scheme for secure authentication between multiple vehicles and CSPs. The proposed scheme can prevent unauthorized access and provide SK-security even if temporary information is leaked. To improve the service, the CSP only needs to broadcast an anonymous message periodically instead of having to generate a unique anonymous message for each of vehicles. Similarly, when a vehicle wants to request the services of m CSPs, it only needs to send one request message instead of m. Therefore, the proposed scheme not only implements many-to-many communication but also significantly reduces the computation and communication overhead. Moreover, a thorough security analysis shows that the proposed scheme provides better security compared to other related schemes.

Mobility Management in 5G-enabled Vehicular Networks

Over the past few years, the next generation of vehicular networks is envisioned to play an essential part in autonomous driving, traffic management, and infotainment applications. The next generation of intelligent vehicular networks enabled by 5G systems will integrate various heterogeneous wireless techniques to enable time-sensitive services with guaranteed quality of service and ultimate bandwidth usage. However, to allow the dense diversity of wireless technologies, seamless and reliable wireless communication protocols need to be thoroughly investigated in vehicular networks environment. Henceforth, efficient mobility management protocols that mitigate the challenges of vehicles’ mobility is essential to support massive data loads throughout various applications. In this article, we review different mobility management protocols and their ability to address issues related to 5G-enabled vehicular networks within the related works. First, we provide a broad view of existing models of vehicular networks and their applicability to the next generation of wireless networks. Next, we propose a classification of several vehicular network models that suit the 5G wireless network, followed by a thorough discussion of the mobility management challenges in each of these network models that need to be addressed and then discuss each of their benefits and drawbacks accordingly.

Geographic information-based Data Transmission and Cooperative Communication for Vehicular Networks

With the advancements in Vehicular communication technologies in automobile engineering leads to enhancement of modern societies by utilizing Internet based data communication in a vehicular network to effectively avoid accidents and traffic congestions using Multi Input Multi Output (MIMO) cooperative relay technique for enhancing the aspects of performance by reduction of transmission energy consumption by taking the advantage of spatial and temporal diversity gain in a vehicular network as the conventional routing based on topology is merely not suitable over a dynamic vehicular network environment as GPS is used to identify effective route [4].In this paper we propose applications of cooperative communication techniques and their survey for identifying close relationship between forwarding and addressing techniques in a vehicular network and further we compare performance and energy consumption of cooperative techniques with the traditional multi-hop technique over Rayleigh channel using MQAM for optimization.

Geographic information-based Data Transmission and Cooperative Communication for Vehicular Networks

With the advancements in Vehicular communication technologies in automobile engineering leads to enhancement of modern societies by utilizing Internet based data communication in a vehicular network to effectively avoid accidents and traffic congestions using Multi Input Multi Output (MIMO) cooperative relay technique for enhancing the aspects of performance by reduction of transmission energy consumption by taking the advantage of spatial and temporal diversity gain in a vehicular network as the conventional routing based on topology is merely not suitable over a dynamic vehicular network environment as GPS is used to identify effective route [4].In this paper we propose applications of cooperative communication techniques and their survey for identifying close relationship between forwarding and addressing techniques in a vehicular network and further we compare performance and energy consumption of cooperative techniques with the traditional multi-hop technique over Rayleigh channel using MQAM for optimization.

AI‐assisted data dissemination methods for supporting intelligent transportation systems†

As an essential component of Smart Cities, the intelligent transportation system (ITS) utilizes a large number of deployed traffic monitoring equipment and Internet‐of‐Vehicles technologies to timely transfer traffic management measures formulated based on accurately grasped real‐time traffic conditions to all transportation system participants for improving the operating efficiency and safety of the transportation system. The key to achieving this advantage is effective data transmission. Correspondingly, by exploiting these recorded massive traffic data, a variety of AI‐assisted data transmission methods are designed to improve the data transmission performance in the vehicular network environment (VNE) to ensure the effective operation of ITS. To help readers get an initial understanding of how AI technology can help with data transmission in VNE, in this letter, we will discuss two types of AI‐assisting methods targeting the data dissemination performance enhancement in vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communications respectively in detail, that is, predictive handover/pre‐caching algorithm and predicted traffic flow‐assisted data routing protocols. Additionally, empirical evaluation is conducted to demonstrate the effectiveness of the discussed methods.