VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Capacity analysis in different systems exploiting mobility of VANETs

Improving road safety and traffic efficiency has been a long-term endeavor for not only government but also automobile industry and academia. After the U.S. Federal Communication Commission (FCC) allocated a 75 MHz spectrum at 5.9 GHz for vehicular communications, the vehicular ad hoc network (VANET), as an instantiation of the mobile ad hoc network (MANET) with much higher node mobility, opens a new door to combat the road fatalities. In VANETs, a variety of applications ranging from safety related (e.g. emergency report, collision warning) to non-safety-related (e.g. infotainment and entertainment) can be enabled by vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications. However, the flourish of VANET still hinges fully understanding and managing the challenges that the public concerns, for example, capacity and connectivity issues due to the high mobility of vehicles. In this thesis, we investigate how vehicle mobility can impact the performance in three important VANET-involved systems, i.e., pure VANET, VANET-enhanced intelligent transportation systems (ITS), and fast electric vehicle (EV) charging systems. First, in pure VANET, our work shows that the network data-traffic can be balanced and the network throughput can be improved with the help of the vehicle mobility differentiation. Furthermore, leveraging vehicular communications of VANETs, the mobility-aware real-time path planning can be designed to smooth the vehicle traffic in an ITS, through which the traffic congestion in urban scenarios can be effectively relieved. In addition, with the consideration of the range anxiety caused by mobility, coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. To this end, we try to answer the following questions: Q1) How to utilize mobility characteristics of vehicles to derive the achievable asymptotic throughput capacity in pure VANETs? Q2) How to design path planning for mobile vehicles to maximize spatial utility based on mobility differentiation, in order to approach vehicle-traffic capacity in a VANET-enhanced ITS? Q3) How to develop the charging strategies based on mobility of electric vehicles to improve the electricity utility, in order to approach load capacities of charging stations in VANET-enhanced smart grid? To achieve the first objective, we consider the unique features of VANETs and derive the scaling law of VANETs throughput capacity in the data uploading scenario. We show that in both free-space propagation and non-free-space propagation environments, the achievable throughput capacity of individual vehicle scales as $\Theta (\frac{1}{{\log n}}) with$n$denoting the population of a set of homogenous vehicles in the network. To achieve the second objective, we first establish a VANET-enhanced ITS, which incorporates VANETs to enable real-time communications among vehicles, road side units (RSUs), and a vehicle-traffic server in an efficient way. Then, we propose a real-time path planning algorithm, which not only improves the overall spatial utilization of a road network but also reduces average vehicle travel cost for avoiding vehicles from getting stuck in congestion. To achieve the third objective, we investigate a smart grid involved EV fast charging system, with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits VANETs to support real-time communications among RSUs and highly mobile EVs for real-time vehicle mobility information collection or charging decision dispatch. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. In summary, the analysis developed and the scaling law derived in$Q1$of this thesis is practical and fundamental to reveal the relationship between the mobility of vehicles and the network performance in VANETs. And the strategies proposed in$Q2$and$Q3$ of the thesis are meaningful in exploiting/leveraging the vehicle mobility differentiation to improve the system performance in order to approach the corresponding capacities

A novel intrusion detection system against spoofing attacks in connected electric vehicles

The Electric Vehicles (EVs) market has seen rapid growth recently despite the anxiety about driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging that enables power exchange between the vehicle and the grid while the vehicle is moving. Specifically, part of the literature focuses on the intelligent routing of EVs in need of charging. Inter-Vehicle communications (IVC) play an integral role in intelligent routing of EVs around a static charging station or dynamic charging on the road network. However, IVC is vulnerable to a variety of cyber attacks such as spoofing. In this paper, a probabilistic cross-layer Intrusion Detection System (IDS), based on Machine Learning (ML) techniques, is introduced. The proposed IDS is capable of detecting spoofing attacks with more than accuracy. The IDS uses a new metric, Position Verification using Relative Speed (PVRS), which seems to have a significant effect in classification results. PVRS compares the distance between two communicating nodes that is observed by On-Board Units (OBU) and their estimated distance using the relative speed value that is calculated using interchanged signals in the Physical (PHY) layer

Distributed Decision Making for V2v Charge Sharing in Intelligent Transportation Systems

Electric vehicles (EVs) have emerged in the intelligent transportation system (ITS) to meet the increasing environmental concerns. To facilitate on-demand requirement of EV charging, vehicle-to-vehicle (V2V) charge transfer can be employed. However, most of the existing approaches to V2V charge sharing are centralized or semi-centralized, incurring huge message overhead, long waiting time, and infrastructural cost. In this paper, we propose novel distributed heuristic algorithms for V2V charge sharing based on the multi-criteria decision-making policy. The problem is mapped to an alias classical problem (i.e., optimum matching in weighted bipartite graphs), where the goal is to maximize the matching cardinality while minimizing the matching cost. An integer linear programming (ILP)-based problem formulation cannot achieve optimum matching because the global network topology is not available with the EVs due to their limited communication range. Our proposed heuristics can yield an almost stable matching with lesser computational, and message overhead compared to other existing distributed approaches. An average case matching probability is also calculated. Simulation experiments are conducted to measure the performance of our heuristics in terms of message overhead, matching percentage, and matching preference. The proposed solution outperforms the existing distributed approaches and shows comparable result with respect to standard centralized stable matching algorithm

A REINFORCEMENT LEARNING APPROACH TO VEHICLE PATH OPTIMIZATION IN URBAN ENVIRONMENTS

Road traffic management in metropolitan cities and urban areas, in general, is an important component of Intelligent Transportation Systems (ITS). With the increasing number of world population and vehicles, a dramatic increase in road traffic is expected to put pressure on the transportation infrastructure. Therefore, there is a pressing need to devise new ways to optimize the traffic flow in order to accommodate the growing needs of transportation systems. This work proposes to use an Artificial Intelligent (AI) method based on reinforcement learning techniques for computing near-optimal vehicle itineraries applied to Vehicular Ad-hoc Networks (VANETs). These itineraries are optimized based on the vehicle’s travel distance, travel time, and traffic road congestion. The problem of traffic density is formulated as a Markov Decision Process (MDP). In particular, this work introduces a new reward function that takes into account the traffic congestion when learning about the vehicle’s best action (best turn) to take in different situations. To learn the effect of this approach, the work investigated different learning algorithms such as Q-Learning and SARSA in conjunction with two exploration strategies: (a) e-greedy and (b) Softmax. A comparative performance study of these methods is presented to determine the most effective solution that enables the vehicles to find a fast and reliable path. Simulation experiments illustrate the effectiveness of proposed methods in computing optimal itineraries allowing vehicles to avoid traffic congestion while maintaining reasonable travel times and distances

Vehicular Networks with Infrastructure: Modeling, Simulation and Testbed

This thesis focuses on Vehicular Networks with Infrastructure. In the examined scenarios, vehicular nodes (e.g., cars, buses) can communicate with infrastructure roadside units (RSUs) providing continuous or intermittent coverage of an urban road topology. Different aspects related to the design of new applications for Vehicular Networks are investigated through modeling, simulation and testing on real field. In particular, the thesis: i) provides a feasible multi-hop routing solution for maintaining connectivity among RSUs, forming the wireless mesh infrastructure, and moving vehicles; ii) explains how to combine the UHF and the traditional 5-GHz bands to design and implement a new high-capacity high-efficiency Content Downloading using disjoint control and service channels; iii) studies new RSUs deployment strategies for Content Dissemination and Downloading in urban and suburban scenarios with different vehicles mobility models and traffic densities; iv) defines an optimization problem to minimize the average travel delay perceived by the drivers, spreading different traffic flows over the surface roads in a urban scenario; v) exploits the concept of Nash equilibrium in the game-theory approach to efficiently guide electric vehicles drivers' towards the charging stations. Moreover, the thesis emphasizes the importance of using realistic mobility models, as well as reasonable signal propagation models for vehicular networks. Simplistic assumptions drive to trivial mathematical analysis and shorter simulations, but they frequently produce misleading results. Thus, testing the proposed solutions in the real field and collecting measurements is a good way to double-check the correctness of our studie

Social internet of vehicles for smart cities

Open Access journa

A Review of Research on Privacy Protection of Internet of Vehicles Based on Blockchain

Numerous academic and industrial fields, such as healthcare, banking, and supply chain management, are rapidly adopting and relying on blockchain technology. It has also been suggested for application in the internet of vehicles (IoV) ecosystem as a way to improve service availability and reliability. Blockchain offers decentralized, distributed and tamper-proof solutions that bring innovation to data sharing and management, but do not themselves protect privacy and data confidentiality. Therefore, solutions using blockchain technology must take user privacy concerns into account. This article reviews the proposed solutions that use blockchain technology to provide different vehicle services while overcoming the privacy leakage problem which inherently exists in blockchain and vehicle services. We analyze the key features and attributes of prior schemes and identify their contributions to provide a comprehensive and critical overview. In addition, we highlight prospective future research topics and present research problems