MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

Vehicle-to-Vehicle Inductive Charge Transfer Feasibility and Public Health Implications

There has been an increased push away from the traditional combustion-engine powered vehicle due to environmental, health, and political concerns. As a result, alternative methods of transportation such as electric vehicles (EVs) have gaining popularity in the market. However, the EVs are not penetrating the market as quickly as expected, due in part to a combination of range, charge anxiety, and their financial costs. EVs cannot travel far due to limited driving range and require longer charge times than combustion-engine powered vehicles to recharge. Coupled with a lacking infrastructure for charging, the feasibility of an all-electric transportation market is still not possible. We propose a novel system in which we study and characterize the feasibility of increasing the effective driving range of a battery electric vehicle by utilizing inductive charge transfer to create an ad-hoc charging network where vehicles can “share” charge with one another. The application of wireless charge transfer from vehicle-to-vehicle (V2V) is the first of its kind and does not require any changes to current metropolitan infrastructures. Through the use of computer networking and communications algorithms, we analyze real-world commuter and taxi data to determine the potential effectiveness of such a system. We propose a participation and incentive mechanism to encourage participation in this network that enables the system to be functional.To illustrate proof of principle for V2V charging at traffic lights, we simulate a simplified model in which vehicles only exchange charge at traffic lights without coordination with other vehicles. Using a greedy heuristic, vehicles only exchange charge if they happen to meet another vehicle that has charge to share. The heuristic is greedy since decisions are made at each iteration with longer optimality not being considered. We are able to demonstrate an increase in effective driving range of EVs using these simplistic assumptions. In this thesis, we develop and quantify a complete simulation framework, which allows EVs to operate using charge sharing. We analyze data from the United States Department of Transportation, New York City Taxi and Limousine Commission, and Regional New York City data sources to understand the cumulative driving distance distributions for passenger/commuter vehicles and taxicabs in large metropolitan areas such as New York City. We show that the driving distributions can best be represented as heavy-tail distribution functions with most commuter vehicles not requiring additional charge during a typical day’s usage of their vehicle as compared to taxicabs, which regularly travel more than 100 miles during a 12-hour shift. We develop and parameterize several variables for input into our simulation framework including driving distance, charge exchange heuristics, models for determining pricing of charge units, traffic density, and geographic location. The inclusion of these parameters helps to build a framework that can be utilized for any metropolitan area to determine the feasibility of EVs. We have performed extensive evaluation of our model using real data. Our current simulations indicate that we can increase the effective distance that an electric vehicle travels by a factor of at least 2.5. This increased driving range makes EVs a more feasible mode of transportation for fleet vehicles such as taxicabs that rely heavily on commuting long cumulative distances. We have identified areas for future improvement to add further parameters to make the model even more sensitive. Finally, we focus on the application of our charge sharing framework in a real-world application for utilizing this methodology for the New York City bus system. In partnership with the New York City MTA, we launched a feasibility study of converting the currently majority hybrid bus fleet into a complete electric bus fleet with charging available at bus stops during scheduled bus stops. Unlike the earlier charge sharing framework, this simulation focuses on discrete distances that are traveled by the bus before having an opportunity to charge at the next bus stop. In this scenario, a large source of variability is the amount of time that the bus is able to stop at a bus stop for charging since this is determined by the amount of time needed to successfully embark and disembark the passengers at the given bus stop. This particular variability impacts how much charge the bus is able to gain during any given stop. We conclude with a list of opportunities for future work in expanding the model with additional parameters and conclusions of our work. Further, we identify areas of further research that outline the potential positive and negative outcomes from a charge sharing system that can be extended to various other applications including micro-mobility applications such as electric scooters and bicycles

Protocole de routage basé sur des passerelles mobiles pour un accès Internet dans les réseaux véhiculaires

La rapide progression des technologies sans fil au cours de ces dernières années a vu naître de nouveaux systèmes de communication dont les réseaux véhiculaires. Ces réseaux visent à intégrer les nouvelles technologies de l’information et de la communication dans le domaine automobile en vue d’améliorer la sécurité et le confort sur le réseau routier. Offrir un accès Internet aux véhicules et à leurs occupants peut sans doute aider à anticiper certains dangers sur la route tout en rendant plus agréables les déplacements à bord des véhicules. Le déploiement de ce service nécessite que des messages soient échangés entre les véhicules. Le routage constitue un élément crucial dans un réseau, car définissant la façon dont les différentes entités échangent des messages. Le routage dans les VANETS constitue un grand défi car ces derniers sont caractérisés par une forte mobilité entraînant une topologie très dynamique. Des protocoles ont été proposés pour étendre Internet aux réseaux véhiculaires. Toutefois, la plupart d’entre eux nécessitent un coût élevé de messages de contrôle pour l’établissement et le maintien des communications. Ceci a pour conséquence la saturation de la bande passante entrainant ainsi une baisse de performance du réseau. Nous proposons dans ce mémoire, un protocole de routage qui s’appuie sur des passerelles mobiles pour étendre Internet aux réseaux véhiculaires. Le protocole prend en compte la mobilité des véhicules et la charge du réseau pour l’établissement et le maintien des routes.The fast progression of wireless technologies has motivated the emergence of new communications system called VANETS (Vehicular Adhoc Networks). VANETS enable vehicles on the roadway to communicate with each other and with road infrastructure using wireless capabilities. The applications of VANETS include improving safety and comfort on the road. For example, by providing Internet to vehicles, traveling can be safer and more comfortable. To provide Internet connectivity, messages need to be exchanged between the vehicles. However, it is hard to design an efficient routing protocol for connecting vehicles to Internet with a reasonable cost due to high mobility in VANETS. Although, several existing routing protocols have been proposed in the open literature to extend Internet to VANETS, they generate considerable overhead. This leads to unfairly consumption of bandwidth decreasing network performance. We design a routing protocol to connect vehicles to Internet through mobile gateways with the objective to make efficient use of the network bandwidth. Indeed, the protocol significantly reduces the communication overhead required to establish and maintain the routes relying on the mobility of the gateways and the network’s load

CognitiveCharge: disconnection tolerant adaptive collaborative and predictive vehicular charging

Electric vehicles (EVs) are rapidly becoming more common and ownership is set to rise globally in coming years. The potential impacts of increased EVs on the electrical grid have been widely investigated and in its current state, existing grid infrastructure will struggle to meet the high demands at peak charging hours. The limited range of electric cars compounds this issue. We therefore propose CognitiveCharge, a novel approach to predictive and adaptive disconnection aware opportunistic energy discovery and transfer for the smart vehicular charging. CognitiveCharge detects and reacts to individual nodes and network regions which are at risk of getting depleted by using implicit predictive hybrid contact and resources congestion heuristics. CognitiveCharge exploits localised relative utility based approach to adaptively offload the energy from parts of the network with energy surplus to depleting areas with non-uniform depletion rates. We evaluate CognitiveCharge using a multi-day traces for the city of San Francisco, USA and Nottingham, UK to compare against existing infrastructure across a range of metrics. CognitiveCharge successfully eliminates congestion at both ad hoc and infrastructure charging points, reduces the time that a vehicle must wait to charge from the point at which it identifies as being in need of energy, and drastically reduces the total number of nodes in need of energy over the evaluation period

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust – a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies in order to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform in order to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

Distributed Data Management in Vehicular Networks Using Mobile Agents

En los últimos años, las tecnologías de la información y las comunicaciones se han incorporado al mundo de la automoción gracias a sus avances, y han permitido la creación de dispositivos cada vez más pequeños y potentes. De esta forma, los vehículos pueden ahora incorporar por un precio asequible equipos informáticos y de comunicaciones.En este escenario, los vehículos que circulan por una determinada zona (como una ciudad o una autopista) pueden comunicarse entre ellos usando dispositivos inalámbricos que les permiten intercambiar información con otros vehículos cercanos, formando así una red vehicular ad hoc, o VANET (Vehicular Ad hoc Network). En este tipo de redes, las comunicaciones se establecen con conexiones punto a punto por medio de dispositivos tipo Wi-Fi, que permiten la comunicación con otros del mismo tipo dentro de su alcance, sin que sea necesaria la existencia previa de una infraestructura de comunicaciones como ocurre con las tecnologías de telefonía móvil (como 3G/4G), que además requieren de una suscripción y el pago de una tarifa para poder usarlas.Cada vehículo puede enviar información y recibirla de diversos orígenes, como el propio vehículo (por medio de los sensores que lleva incorporados), otros vehículos que se encuentran cerca, así como de la infraestructura de tráfico presente en las carreteras (como semáforos, señales, paneles electrónicos de información, cámaras de vigilancia, etc.). Todos estas fuentes pueden transmitir datos de diversa índole, como información de interés para los conductores (por ejemplo, atascos de tráfico o accidentes en la vía), o de cualquier otro tipo, mientras sea posible digitalizarla y enviarla a través de una red.Todos esos datos pueden ser almacenados localmente en los ordenadores que llevan los vehículos a medida que son recibidos, y sería muy interesante poder sacarles partido por medio de alguna aplicación que los explotara. Por ejemplo, podrían utilizarse los vehículos como plataformas móviles de sensores que obtengan datos de los lugares por los que viajan. Otro ejemplo de aplicación sería la de ayudar a encontrar plazas de aparcamiento libres en una zona de una ciudad, usando la información que suministrarían los vehículos que dejan una plaza libre.Con este fin, en esta tesis se ha desarrollado una propuesta de la gestión de datos basada en el uso de agentes móviles para poder hacer uso de la información presente en una VANET de forma eficiente y flexible. Esta no es una tarea trivial, ya que los datos se encuentran dispersos entre los vehículos que forman la red, y dichos vehículos están constantemente moviéndose y cambiando de posición. Esto hace que las conexiones de red establecidas entre ellos sean inestables y de corta duración, ya que están constantemente creándose y destruyéndose a medida que los vehículos entran y salen del alcance de sus comunicaciones debido a sus movimientos.En un escenario tan complicado, la aproximación que proponemos permite que los datos sean localizados, y que se puedan hacer consultas sobre ellos y transmitirlos de un sitio cualquiera de la VANET a otro, usando estrategias multi-salto que se adaptan a las siempre cambiantes posiciones de los vehículos. Esto es posible gracias a la utilización de agentes móviles para el procesamiento de datos, ya que cuentan con una serie de propiedades (como su movilidad, autonomía, adaptabilidad, o inteligencia), que hace que sean una elección muy apropiada para este tipo de entorno móvil y con un elevado grado de incertidumbre.La solución propuesta ha sido extensamente evaluada y probada por medio de simulaciones, que demuestran su buen rendimiento y fiabilidad en redes vehiculares con diferentes condiciones y en diversos escenarios.<br /

Modeling and optimizing network infrastructure for autonomous vehicles

Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin

MODELING AND ANALYSIS OF AN AUTONOMOUS MOBILITY ON DEMAND SYSTEM

Ph.DDOCTOR OF PHILOSOPH