Internet Of Vehicles Environment Research Articles

An efficient certificateless aggregate signature scheme for the Internet of Vehicles

AbstractIn recent years, the research of Internet of vehicles (IoV) has received extensive attention. In IoV, vehicles can make intelligent decisions by exchanging the real‐time traffic information between other vehicles and IoV infrastructures, thereby reducing the probability of traffic jams and accidents. Although IoV has many advantages, it is necessary to ensure that the data are not edited, forged, or disclosed during the transmission. Also, the certificateless signature (CLS) seems to be the solution to this problem. While in the high‐intensity data transmission environment, the CLS scheme is inefficient for application. To improve the efficiency, the function of aggregation was introduced into the CLS. The certificateless aggregate signature (CL‐AS) can aggregate multiple signatures into a brief signature, reducing the computational cost and the size of signature. Nevertheless, most of the recent proposed CL‐AS schemes have problems of security or efficiency. In this paper, we presented an advanced efficient CL‐AS scheme with elliptic curve cryptography for the IoV environment. In addition, the proposed scheme uses pseudonyms in communications to prevent vehicles from revealing their identity. We proved the security of our proposal in the random oracle based on computational Diffie‐Hellman assumption. Also, the efficiency analysis and simulation show the superiority of our scheme.

QBPP: Quality of services–based location privacy protection method for location-based services in cloud-enabled Internet of vehicles

Location-based services has been widely applied in cloud-enabled Internet of vehicles. Within these services, location privacy issues have captured significant attention. Vehicles use the technology of anonymity to implement occultation, the location is not revealed. In this process, large-scale data transmissions can reduce the quality of services. In order to ensure location privacy and high-quality services, the cloud manager customizes virtual machines for vehicles to support location-based services according to the vehicles’ demands. To achieve better performance, this article presents a conditional anonymity method that does not use bilinear pairings to address the problem of privacy disclosure by using discrete logarithm problem and Diffie–Hellman problem. Moreover, asymmetric key algorithms are used in the Internet of vehicles environment to reduce the cost. To guarantee secure data transmission in Internet of vehicles, the batch validation technique is used to address data integrity. Our theoretical security analysis and experiments show that the proposed scheme is secure in compared attack models, such as impersonation attacks, replay attacks, the man-in-the-middle attacks, and so on. Our proposed scheme ensures the security requirements such as message authentication, location privacy protection, and traceability, while lowering transmission and computation cost.

Internet of Vehicles and Cost-Effective Traffic Signal Control.

The Internet of Vehicles (IoV) is attracting many researchers with the emergence of autonomous or smart vehicles. Vehicles on the road are becoming smart objects equipped with lots of sensors and powerful computing and communication capabilities. In the IoV environment, the efficiency of road transportation can be enhanced with the help of cost-effective traffic signal control. Traffic signal controllers control traffic lights based on the number of vehicles waiting for the green light (in short, vehicle queue length). So far, the utilization of video cameras or sensors has been extensively studied as the intelligent means of the vehicle queue length estimation. However, it has the deficiencies like high computing overhead, high installation and maintenance cost, high susceptibility to the surrounding environment, etc. Therefore, in this paper, we propose the vehicular communication-based approach for intelligent traffic signal control in a cost-effective way with low computing overhead and high resilience to environmental obstacles. In the vehicular communication-based approach, traffic signals are efficiently controlled at no extra cost by using the pre-equipped vehicular communication capabilities of IoV. Vehicular communications allow vehicles to send messages to traffic signal controllers (i.e., vehicle-to-infrastructure (V2I) communications) so that they can estimate vehicle queue length based on the collected messages. In our previous work, we have proposed a mechanism that can accomplish the efficiency of vehicular communications without losing the accuracy of traffic signal control. This mechanism gives transmission preference to the vehicles farther away from the traffic signal controller, so that the other vehicles closer to the stop line give up transmissions. In this paper, we propose a new mechanism enhancing the previous mechanism by selecting the vehicles performing V2I communications based on the concept of road sectorization. In the mechanism, only the vehicles within specific areas, called sectors, perform V2I communications to reduce the message transmission overhead. For the performance comparison of our mechanisms, we carry out simulations by using the Veins vehicular network simulation framework and measure the message transmission overhead and the accuracy of the estimated vehicle queue length. Simulation results verify that our vehicular communication-based approach significantly reduces the message transmission overhead without losing the accuracy of the vehicle queue length estimation.

A Heterogeneous IoV Architecture for Data Forwarding in Vehicle to Infrastructure Communication

The Internet of vehicles (IoV) is a newly emerged wave that converges Internet of things (IoT) into vehicular networks to benefit from ubiquitous Internet connectivity. Despite various research efforts, vehicular networks are still striving to achieve higher data rate, seamless connectivity, scalability, security, and improved quality of service, which are the key enablers for IoV. It becomes even more critical to investigate novel design architectures to accomplish efficient and reliable data forwarding when it comes to handling the emergency communication infrastructure in the presence of natural epidemics. The article proposes a heterogeneous network architecture incorporating multiple wireless interfaces (e.g., wireless access in vehicular environment (WAVE), long-range wireless fidelity (WiFi), and fourth generation/long-term evolution (4G/LTE)) installed on the on-board units, exploiting the radio over fiber approach to establish a context-aware network connectivity. This heterogeneous network architecture attempts to meet the requirements of pervasive connectivity for vehicular ad hoc networks (VANETs) to make them scalable and adaptable for IoV supporting a range of emergency services. The architecture employs the Best Interface Selection (BIS) algorithm to always ensure reliable communication through the best available wireless interface to support seamless connectivity required for efficient data forwarding in vehicle to infrastructure (V2I) communication successfully avoiding the single point of failure. Moreover, the simulation results clearly argue about the suitability of the proposed architecture in IoV environment coping with different types of applications against individual wireless technologies.

Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues

The intelligent transportation system (ITS) concept was introduced to increase road safety, manage traffic efficiently, and preserve our green environment. Nowadays, ITS applications are becoming more data-intensive and their data are described using the “5Vs of Big Data”. Thus, to fully utilize such data, big data analytics need to be applied. The Internet of vehicles (IoV) connects the ITS devices to cloud computing centres, where data processing is performed. However, transferring huge amount of data from geographically distributed devices creates network overhead and bottlenecks, and it consumes the network resources. In addition, following the centralized approach to process the ITS big data results in high latency which cannot be tolerated by the delay-sensitive ITS applications. Fog computing is considered a promising technology for real-time big data analytics. Basically, the fog technology complements the role of cloud computing and distributes the data processing at the edge of the network, which provides faster responses to ITS application queries and saves the network resources. However, implementing fog computing and the lambda architecture for real-time big data processing is challenging in the IoV dynamic environment. In this regard, a novel architecture for real-time ITS big data analytics in the IoV environment is proposed in this paper. The proposed architecture merges three dimensions including intelligent computing (i.e. cloud and fog computing) dimension, real-time big data analytics dimension, and IoV dimension. Moreover, this paper gives a comprehensive description of the IoV environment, the ITS big data characteristics, the lambda architecture for real-time big data analytics, several intelligent computing technologies. More importantly, this paper discusses the opportunities and challenges that face the implementation of fog computing and real-time big data analytics in the IoV environment. Finally, the critical issues and future research directions section discusses some issues that should be considered in order to efficiently implement the proposed architecture.

Context-Aware Data-Driven Intelligent Framework for Fog Infrastructures in Internet of Vehicles

Internet of Vehicles (IoV) is the evolution of vehicular ad-hoc networks and intelligent transportation systems focused on reaping the benefits of data generated by various sensors within these networks. The IoV is further empowered by a centralized cloud and distributed fog-based infrastructure. The myriad amounts of data generated by the vehicles and the environment have the potential to enable diverse services. These services can benefit from both variety and velocity of the generated data. This paper focuses on the data at the edge nodes to enable fog-based services that can be consumed by various IoV safety and non-safety applications. This paper emphasizes the challenges involved in offering the context-aware services in an IoV environment. In order to overcome these challenges, this paper proposes a data analytics framework for fog infrastructures at the fog layer of traditional IoV architecture that offers context-aware real time, near real-time and batch services at the edge of a network. Finally, the appropriateness of the proposed framework is verified through different use cases in the IoV environment.

Cellular automata model for traffic flow at intersections in internet of vehicles

Considering the effect of the front vehicle’s speed, the influence of the brake light and the conflict of the traffic flow, we established a cellular automata model called CE-NS for traffic flow at the intersection in the non-vehicle networking environment. According to the information interaction of Internet of Vehicles (IoV), introducing parameters describing the congestion and the accurate speed of the front vehicle into the CE-NS model, we improved the rules of acceleration, deceleration and conflict, and finally established a cellular automata model for traffic flow at intersections of IoV. The relationship between traffic parameters such as vehicle speed, flow and average travel time is obtained by numerical simulation of two models. Based on this, we compared the traffic situation of the non-vehicle networking environment with conditions of IoV environment, and analyzed the influence of the different degree of IoV on the traffic flow. The results show that the traffic speed is increased, the travel time is reduced, the flux of intersections is increased and the traffic flow is more smoothly under IoV environment. After the vehicle which achieves IoV reaches a certain proportion, the operation effect of the traffic flow begins to improve obviously.

Establishing an Intelligent Transportation System With a Network Security Mechanism in an Internet of Vehicle Environment

The Internet of Vehicle (IoV) utilizes networks to conduct message exchange and related services or application. In recent years, smart cities and IoVs have become areas of interest in the new generation Internet of Things development, especially since the development of intelligent transportation system has focused on bettering traffic conditions. This paper proposes establishing an intelligent transportation system with a network security mechanism in an IoV environment, with emphasis on the following aspects: 1) this paper integrates intelligent transportation systems in traffic signal control to aid emergency vehicles in more promptly arriving at its destination; 2) in the case of traffic incidents, this paper’s approach allows regular vehicles to obtain proof of incident from pertaining authorities and learn about nearby vehicles global positioning system information, such as position and speed, and utilize their car camcorder data for proving purposes; and 3) this paper combines roadside units (RSUs) with traffic signal control and transmits important information to the certificate authority (CA) for storage. Given that RSUs are limited in computation ability and storage space, we can assess and filter the information before sending it to the CA, reducing RSUs computational burden and storage space usage. This paper satisfies IoVs network security requirements of authentication, non-repudiation, conditional anonymity, and conditional untraceability, and, as seen from experiment results, the proposed method is superior to that of other studies.

Coalition Games for Spatio-Temporal Big Data in Internet of Vehicles Environment: A Comparative Analysis

The evolution of Internet of Things (IoT) leads to the emergence of Internet of Vehicles (IoV). In IoV, nodes/vehicles are connected with one another to form a vehicular ad hoc network (VANET). But, due to constant topological changes, database repository (centralized/distributed) in IoV is of spatio-temporal nature, as it contains traffic-related data, which is dependent on time and location from a large number of inter-connected vehicles. The nature of collected data varies in size, volume, and dimensions with the passage of time, which requires large storage and computation time for processing. So, one of the biggest challenges in IoV is to process this large volume of data and later on deliver to its destination with the help of a set of the intermediate/relay nodes. The intermediate/relay nodes may act either in cooperative or non-cooperative mode for processing the spatio-temporal data. This paper analyze this problem using Bayesian coalition game (BCG) and learning automata (LA). The LA stationed on the vehicles are assumed as the players in the game. For each action performed by an automaton, it may get a reward or a penalty from the environment using which each automaton updates its action probability vector for all the actions to be taken in future. A detailed comparison has been provided by analyzing the cooperative and noncooperative nature of the players in the game. The existence of Nash equilibrium (NE) with respect to the probabilistic belief of the strategies of the other players in the coalition game is also analyzed.

Bayesian Coalition Game as-a-Service for Content Distribution in Internet of Vehicles

With the latest developments in cloud computing and Internet of Vehicles (IoV), now it is possible to construct an efficient next generation infrastructure, which may act as an interface for the connection of a number of objects such as vehicles, intelligent sensors, actuators, home appliances, high-computing servers, and computers to the Internet. In such an environment, vehicles on the road may act as source provider or consumer to facilitate various users connected to the Internet. Content distribution to all these objects especially to the vehicles in this environment is a challenging task due to the tight constraints of maintaining connectivity, coverage, and topology of the vehicles. To address these issues, in this paper, we proposed a new Bayesian coalition game (BCG) as-a-service for content distribution among these objects using support from cloud. We have addressed the problem of content distribution to vehicles from the perspective of BCG and learning automata (LA). Content are assumed to be located at the cloud, which is accessed by the vehicles through Internet even on-the-fly. Vehicles are assumed to be the players in the game and form a coalition among themselves using Markov decision process (MDP). For each action taken by an automaton, it may get a reward or a penalty from the environment according to which it updates its action probability vector. The performance of the proposed scheme is evaluated be selecting various parameters. The results obtained show that proposed scheme is effective in building next generation IoV environment.

Reservation based optimal parking lot recommendation model in Internet of Vehicle environment

In order to solve the problem that the drivers can't find the optimal parking lot timely, a reservation based optimal parking lot recommendation model in Internet of Vehicle (IoV) environment is designed. Based on the users oriented parking information recommendation system, the model considers subjective demands of drivers comprehensively, makes a deeply analysis of the evaluation indicators. This recommendation model uses a phased selection method to calculate the optimal objective parking lot. The first stage is screening which based on the users' subjective parking demands; the second stage is processing the candidate parking lots through multiple attribute decision making. Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot, save the driver's parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.