Intelligent Vehicular Networks Research Articles

A vehicular network–based intelligent transport system for smart cities

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. In this article, we propose an intelligent vehicular network framework for smart cities that enables route selection based on real-time data received from neighboring vehicles in an ad hoc fashion. We used Wi-Fi Direct–enabled Android-based smartphones as embedded devices in vehicles. We used a vehicular ad hoc network to implement an intelligent transportation system. Data gathering and preprocessing were carried on different routes between two metropolitan cities of a developing country. The framework was evaluated on different fixed route-selection and dynamic route-selection algorithms in terms of resource usage, transmission delay, packet loss, and overall travel time. Our results show reduced travel times of up to 33.3% when compared to a traditional fixed route-selection algorithm.

Analysis on Fog Computing Enabled Vehicular Ad hoc Networks

The vehicular ad-hoc networks make a simple case of networks which are supposed to be very smart because of the tasks and crucial decision making they have to carry out. Since they are on the move, the transmission and reception of information has to be quick in order to make the networks efficient in term of computing time. Fog enabled VANET make the systems more capable by processing much of the information locally and only sending crucial decision making to the cloud which saves time and worth for all sub dependent systems. In this work we have reviewed the two fog enabled VANET schemes, one is SIVNFC (Secure intelligent vehicular network using fog computing) and the other is SOLVE (localization system frameworks).

Dynamic Resource Management of Real-Time Edge Services for Intelligent Vehicular Networks: A Case Study

The Internet of Vehicles requires high bandwidth and low latency services to unleash the potential of fully connected vehicles. Thus, the offloading proposals that successfully manage massive real-time service requests from vehicle nodes are needed. In this paper, we analyse the dynamic resource management for intelligent vehicular networks based on Multi-access Edge Computing architecture services. Using a combination of CloudSim and SUMO simulators, we present a case study of infotainment services in the city centre of Alicante, in Spain, that shows a high degree of optimality both in service allocation and migration when considering dense urban environments.

A two-tier machine learning-based handover management scheme for intelligent vehicular networks

With the increasing demand for real-time road safety services and infotainment applications on vehicles, the development of an efficient wireless mobile communication became crucial for the content delivery of such services in Intelligent Vehicular Networks (IVN). Mobility management enables mobile hosts to communicate over the Internet from foreign networks. However, vehicles' high mobility and the rapid shifts in network topology affect the performance of traditional mobility management protocols. Hence, raising the challenge for seamless wireless communications over IVN. In this paper, we propose a two-tier Machine Learning-based scheme for handover management in intelligent vehicular networks. In the first tier, we use a recurrent neural network model to predict the receiving signal strength of Access Points (APs), to derive a handover trigger decision. In the second tier, a stochastic Markov model is used to select the next access point by utilizing the vehicle flow projections. The performance of the proposed protocol is evaluated using NS-2 simulator and generated vehicles mobility. Simulation results show that the proposed ML-based model outperformed related work in term of prediction accuracy, while the integration of the handover trigger scheme and the access point selection method improved network performance.

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

The vehicular ad hoc network (VANET) is a method through which Intelligent Transportation Systems (ITS) have become important for the benefit of daily life. Real-time detection of all forms of attacks, including hybrid DoS attacks in IEEE 802.11p, has become an urgent issue for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate an enormous amount of messages. This leads to overutilization of the road side unit (RSU) or the central processing unit (CPU) for computation. Therefore, efficient storage and intelligent VANET infrastructure architecture (VIA), which includes trustworthiness, are required. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computation, and communication needs for VANET. This research utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence, including Cuckoo/CSA Artificial Bee Colony (ABC) and Firefly/Genetic Algorithm (GA), to provide real-time detection of DoS attacks in IEEE 802.11p, using VFC for a secure intelligent vehicular network. Vehicles move ar a certain speed and the data is transmitted at 30 Mbps. Firefly Feed forward back propagation neural network (FFBPNN) is used as a classifier to distinguish between the attacked vehicles and the genuine vehicles. The proposed scheme is compared with Cuckoo/CSA ABC and Firefly GA by considering jitter, throughput, and prediction accuracy.

Persistent Traffic Measurement through Vehicle-to-Infrastructure Communications in Cyber-Physical Road Systems

Measuring traffic volume in a road system has important applications in transportation engineering. The connected vehicle technologies integrate wireless communications and computers into transportation systems, allowing wireless data exchanges between vehicles and road-side equipment, and enabling large-scale, sophisticated traffic measurement. This paper investigates the problem of persistent traffic measurement, which was not adequately studied in the prior art, particularly in the context of intelligent vehicular networks. We propose three estimators for privacy-preserving persistent traffic measurement: one for point traffic, one for point-to-point traffic, and another for three-point traffic. After that, we present a general framework to measure persistent traffic that go through more than three locations. The estimators are mathematically derived from the join result of traffic records, which are produced by the electronic roadside units with privacy-preserving data structures. We evaluate our estimation methods using simulations based on both real transportation traffic data and synthetic data. The numerical results demonstrate the effectiveness of the proposed methods in producing high measurement accuracy and allowing accuracy-privacy tradeoff through parameter setting.

NextRoute: a lossless model for accurate mobility prediction

Mobility prediction of vehicles has become a key feature in mobility management in smart cities, mobile computing environments, intelligent transportation systems, vehicular networks, and location-based services. It has several important applications in traffic congestion forecasting, location-based routing protocol designs and targeting advertisements generation, etc. Mobility prediction (aka route prediction) consists of forecasting future routes to be traversed by a vehicle. Several models have been proposed for route prediction. These models are generally probabilistic models (e.g., Markov models) or data mining-based models (e.g., sequential rule-based models), and are trained with historical location data. Although these models were shown to perform well, one important drawback is that they are lossy. In other words, a large amount of information found in the location data is discarded by their training processes. Consequently, these models do not perform well for predicting routes where detailed information is required to perform accurate predictions. Besides, several prediction models assume the Markovian hypothesis that the next location of a user only depends on his/her current location and any previous movement of the user is ignored in prediction. This hypothesis has been employed, such as in Lian et al. (ACM Trans Intell Syst Technol 6:1–27, 2015. https://doi.org/10.1145/2629557), as a simplifying assumption because it allows building models having a small size. However, this assumption is often unrealistic and thus greatly decreases the accuracy of route prediction. Moreover, these models are noise sensitive as they do not tolerate the smallest deviation in location data principally prone to several disturbances and uncertainty issues. To address these limitations, this paper introduces a novel route prediction model named NextRoute. The proposed model is lossless as it compresses location data in a prediction tree without information loss, and it is designed to use all the relevant information contained in the training data to perform prediction. In contrast to some other proposals, NextRoute provides efficient noise tolerance strategy that loosened the similarity measure when matching the current trajectory of vehicle with historical data. An extensive experimental evaluation was conducted with real-world and synthetic datasets providing quite encouraging results.

LoICen: A novel location-based and information-centric architecture for content distribution in vehicular networks

The information-centric networking (ICN) paradigm has gained increasing attention as a solution for boosting content delivery in vehicular network applications. The content-naming oriented search and in-network data caching procedures of ICN can improve content delivery by avoiding the need to determine and maintain end-to-end routing paths in vehicular networks, which is challenging due to the mobility of vehicles, and intermittent and short-lived connections between them. However, the use of the ICN paradigm in vehicular networks aggravates the broadcast storm problem, and constantly suffers from breaks in the reverse path used for content discovery and delivery. In this paper, we propose the location-based and information-centric (LoICen) architecture to improve the content request procedure and reduce the broadcast storm problem in intelligent vehicular networks. In the LoICen architecture, vehicles opportunistically obtain the location information of the vehicles that might have desired content in their cache. This location information is used whenever possible to improve content search and discovery by directing Interest packets to the area where the content may be located, mitigating the broadcast storm problem by selecting only the most suitable neighboring vehicle at each hop to continue forwarding the Interest packet, and improving content delivery by selecting more stable paths. Simulation results show that the LoICen architecture outperformed related work in terms of content delivery ratio, while reducing the delay for the content delivery and the number of transmissions of Interest packets for content search.

Secure Intelligent Vehicular Network Using Fog Computing

VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.

Safety and efficiency control protocol for highways using intelligent vehicular networks

Several emergency cases negatively affect the traffic safety and efficiency over highway roads. Un-expected areas of traffic congestion are usually caused by the occurrence of an accident, existence of a damaged vehicle, or the appearance of an emergency vehicle. These scenarios have safety considerations regarding the involved vehicles in the event of emergency. However, the effect of these cases on surrounding vehicles is usually restricted to the parameters of traffic fluency and efficiency. However, in extreme scenarios, nearby vehicles may become involved in the emergency, such as in a chained accident. In this paper, we first aim to investigate the possible emergency cases over the highway roads. Secondly, we study the effects of these cases on the surrounding traveling vehicles, based on the relative location of each vehicle from the detected case, and the speed and moving direction of each vehicle. Third, we introduce a recommendation protocol that provides the most appropriate response to each vehicle in the vicinity of the detected emergency case. Reducing/increasing the vehicle’s speed, changing the traveling lane, and completely stopping the vehicle are the most popular responses in these scenarios. From the experimental results, we can see that the proposed protocol has increased the traffic fluency in the vicinity of the emergency cases over highways. It decreases the delay in vehicles’ traveling time and increases the throughput of each lane compared to the typical highway protocol. Moreover, it has increased the safety conditions for vehicles and decreased the percentage of vehicles to become involved in accidents.

Cognitive Internet of Vehicles

To fully realize future autonomous driving scenarios, Internet of Vehicles (IoV) has attracted wide attention from both academia and industry. However, suitable cost and stable connectivity cannot be strongly guaranteed by existing architectures such as cellular networks, vehicular ad hoc networks, etc. With the prosperous development of artificial intelligence, cloud/edge computing and 5G network slicing, a more intelligent vehicular network is under deliberation. In this paper, an innovative paradigm called Cognitive Internet of Vehicles (CIoV) is proposed to help address the aforementioned challenge. Different from existing works, which mainly focus on communication technologies, CIoV enhances transportation safety and network security by mining effective information from both physical and network data space. We first present an overview of CIoV including its evolution, related technologies, and architecture. Then we highlight crucial cognitive design issues from three perspectives, namely, intra-vehicle network, inter-vehicle network and beyond-vehicle network. Simulations are then conducted to prove the effect of CIoV and finally some open issues will be further discussed. Our study explores this novel architecture of CIoV, as well as research opportunities in vehicular network.

Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management

As vehicle applications, mobile devices and the Internet of Things are growing fast, and developing an efficient architecture to deal with the big data in the Internet of Vehicles (IoV) has been an important concern for the future smart city. To overcome the inherent defect of centralized data processing in cloud computing, fog computing has been proposed by offloading computation tasks to local fog servers (LFSs). By considering factors like latency, mobility, localization, and scalability, this article proposes a regional cooperative fog-computing-based intelligent vehicular network (CFC-IoV) architecture for dealing with big IoV data in the smart city. Possible services for IoV applications are discussed, including mobility control, multi-source data acquisition, distributed computation and storage, and multi-path data transmission. A hierarchical model with intra-fog and inter-fog resource management is presented, and energy efficiency and packet dropping rates of LFSs in CFC-IoV are optimized.

Smart Collision Avoidance and Hazard Routing Mechanism for Intelligent Transport Network

The smart vehicular ad-hoc network is the network that consists of vehicles for smooth movement and better management of the vehicular connectivity across the given network. This research paper aims to propose a set of solution for the VANETs consisting of the automatic driven vehicles, also called as the autonomous car. Such vehicular networks are always prone to collision due to the natural or un-natural reasons which must be solved before the large-scale deployment of the autonomous transport systems. The newly designed intelligent transport movement control mechanism is based upon the intelligent data propagation along with the vehicle collision and traffic jam prevention schema [8], which may help the future designs of smart cities to become more robust and less error-prone. In the proposed model, the focus is on designing a new dynamic and robust hazard routing protocol for intelligent vehicular networks for improvement of the overall performance in various aspects. It is expected to improve the overall transmission delay as well as the number of collisions or adversaries across the vehicular network zone.

Exploiting Traveling Information for Data Forwarding in Community-Characterized Vehicular Networks

In intelligent vehicular communication networks, a hybrid communication architecture is used which combines both centralized and ad hoc transmission schemes. In order to maximize the end-to-end delivery ratio while reducing the network overhead, one important problem is to efficiently design the data forwarding algorithm to guarantee the quality of data transmission. In this paper, by considering the traveling information and vehicular space-crossing community structure, two metrics, “space–time approachability” and “social approachability,” are defined to provide the absolute and relative geographical information of the forthcoming contacts, respectively. Then, a novel data-forwarding algorithm, called approachability-based algorithm, is proposed, which utilizes two metrics together for better routing quality. We evaluate the proposed approachability-based algorithm utilizing San Francisco Cabspotting and Shanghai Taxi Movement datasets. Simulation results show that the approachability-based data forwarding algorithm can achieve better performance than the popular data forwarding algorithms ZOOM and BUBBLE RAP in all the interested scenarios.

A Review on MANET and DTN for Hybrid Network Architecture

The introduction of intelligent devices with short range wireless communication techniques has motivated the development of Mobile Ad hoc Networks (MANETs) during the last few years. However, traditional end-to-end based routing algorithms designed for MANETs are not much robust in the challenged networks suffering from frequent disturbance, less network density and limited device capability. Such challenge requires the combination use of network technologies. Since DTNs has been used for different applications with a large number of proposed routing algorithms. This paper describes the vital features of communication model aimed to enhance the protection of energy consumed assets against man-made attacks (terrorism) or natural disasters. The model is reviewed based on a hybrid network architecture, which combines the advantages of different wireless technologies, such as Delay Tolerant Networking and Mobile Ad-hoc Networks. The solution includes the definition of an extended routing protocol which is considered on account of the different networking technologies that already exist. Keywords— MANET (Mobile Ad-hoc Network), DTN (Delay Tolerant Network), iMANETS (Internet Based Mobile Ad-hoc Networks), VANET (Vehicular Ad-hoc Networks),iVANETs(Intelligent Vehicular Ad-hoc Networks),AT(Artificial Intelligence).

Integrated Safety and Efficiency in Intelligent Vehicular Networks: Issues and Novel Constructs

We present the cohort and the group constructs which are aimed at reconciling safety and efficiency for intelligent vehicular networks on roads and highways, and show how platoons and vehicular ad hoc networks can be structured as cohorts and groups. Fundamental implications of safety requirements are reviewed. A rationale for on–board systems based on diversified functional redundancy is developed, illustrated with a proposal for neighbor–to–neighbor periodic beaconing based on short range unidirectional communications meant to withstand telemetry failures. Worst–case analytical results are given for safe inter–vehicle spacing in cohorts despite inaccurate vehicle space–time coordinates and failing telemetry capabilities. The group construct is based on prefixing usage of sensing–based solutions with omnidirectional communications. Benefits resulting from prefixing vehicle maneuvers with vehicle role assignments are illustrated with the on–ramp–merging safety–critical scenario.