The Next Generation Intelligent Transportation System: Connected, Safe and Green

Modern Intelligent Transportation Systems (ITSs) employ communication technologies in order to ameliorate the passenger's commuting experience. Vehicular Networking lies at the core of inaugurating an efficient transportation system and aims at transforming vehicles into smart mobile entities that are able to sense their surroundings, collect information about the environment and communicate with each other as well as with Roadside Units (RSUs) deployed alongside roadways. As such, the novel communication paradigm of vehicular networking gave birth to an ITS that embraces a wide variety of applications including but not limited to: traffic management, passenger and road safety, environment monitoring and road surveillance, hot-spot guidance, Drive Thru Internet access, remote region connectivity, and so forth. Furthermore, with the rapid development of computation and communication technologies, the Internet of Vehicles (IoV) promises huge commercial interest and research value, thereby attracting a significant industrial and academic attention. This thesis studies and analyses fundamentally challenging problems in the context of vehicular environments and proposes new techniques targeting the improvement of the performance of ITSs envisioned to play a remarkable role in the IoV era. Unlike existing wireless mobile networks, vehicular networks possess unique characteristics, including high node mobility and a rapidly-changing topology, which should be carefully accounted for. Four major problems from the pool of existing vehicular networking persisting challenges will be addressed in this thesis, namely: a) establishing a connectivity path in a highly dynamic Vehicular Ad Hoc Network, b) examining the performance of Vehicle-to-Infrastructure communication Medium Access Control schemes, c) addressing the scheduling problem of a vehicular networking scenario encompassing an energy-limited RSU by exploiting machine learning techniques, particularly reinforcement learning, to train an agent to make appropriate decisions and develop a scheduling policy that prolongs the network's operational status and allows for acceptable Quality-of-Service levels and d) overcoming the limitations of reinforcement learning techniques in high-dimensional input scenarios by exploiting recent advances in deep learning in an effort to satisfy the driver's well-being as well as his demand for continuous connectivity in a green, balanced, connected and efficient vehicular network. These problems will be extensively studied throughout this thesis, followed by discussions that highlight open research directions worth further investigations

Accelerated Benders Decomposition and Local Branching for Dynamic Maximum Covering Location Problems

The maximum covering location problem (MCLP) is a key problem in facility location, with many applications and variants. One such variant is the dynamic (or multi-period) MCLP, which considers the installation of facilities across multiple time periods. To the best of our knowledge, no exact solution method has been proposed to tackle large-scale instances of this problem. To that end, in this work, we expand upon the current state-of-the-art branch-and-Benders-cut solution method in the static case, by exploring several acceleration techniques. Additionally, we propose a specialised local branching scheme, that uses a novel distance metric in its definition of subproblems and features a new method for efficient and exact solving of the subproblems. These methods are then compared through extensive computational experiments, highlighting the strengths of the proposed methodologies

Maximum flow-based formulation for the optimal location of electric vehicle charging stations

With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EVs) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed-integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real-world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances

An Electric Vehicle Control Strategy to Mitigate Load Altering Attacks Against Power Grids

Due to growing environmental concerns, the world's governments have been encouraging the shift of the transportation sector towards the adoption of Electric Vehicles (EVs). As a result, EV numbers have been growing exponentially and are expected to continue growing further which will add a large EV charging load to the power grid. To this end, this paper presents an EV-based defense mechanism against Load-Altering (LA) attacks targeting the grid. The developed mechanism utilizes H-infinity controllers and Linear Matrix Inequalities (LMIs) to mitigate LA attacks. After the controller synthesis and presentation of the attack scenarios, we demonstrate the effectiveness and success of our defense mechanism against the three known types of LA attacks. The scenarios include three 800 MW LA attacks against the New England 39-bus grid. The results demonstrate how our EV-based mitigation scheme eliminates the attack impacts and maintains the grid's stability in face of an unknown persisting attack.Comment: Accepted in the 2023 8th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)-ICRAIE 2023. arXiv admin note: substantial text overlap with arXiv:2308.0752

Protecting the Future Grid: An Electric Vehicle Robust Mitigation Scheme Against Load Altering Attacks on Power Grids

Due to the growing threat of climate change, the worlds governments have been encouraging the adoption of Electric Vehicles (EVs). As a result, EV numbers have been growing exponentially which will introduce a large EV charging load into the power grid. On this basis, we present a scheme to utilize EVs as a defense mechanism to mitigate Load-Altering (LA) attacks against the grid. The developed scheme relies on robust control theory and Linear Matrix Inequalities (LMIs). Our EV-based defense mechanism is formulated as a feedback controller synthesized using H-2 and H-infinity control techniques to eliminate the impact of unknown LA attacks. The controller synthesis considers the grid topology and the uncertainties of the EV connection to the grid. To demonstrate the effectiveness of the proposed mitigation scheme, it is tested against three types of LA attacks on the New England 39-bus grid. We test our mitigation scheme against 800 MW static, switching, and dynamic attacks in the presence of multiple sources of uncertainty that can affect the EV load during deployment. The results demonstrate how the grid remains stable under the LA attacks that would otherwise lead to serious instabilities.Comment: Accepted for publication in Applied Energ

Impact of information availability on starvation mitigation and delay minimal delivery in ICRCNs

This paper looks into an Intermittently Connected Roadside Communication Network (ICRCN) scenario comprising two isolated source Stationary Roadside Units (SRUs) relying on mobile smart vehicles to relay data to a destination SRU. In this case, it was shown in [1] that the downstream source SRU may suffer from a significant starvation problem. As such, a Markov decision process framework was established therein to identify a suitable Bulk Release Decision Policy (BRDP). BRDP was then implemented within a Starvation Mitigation and Delay-Minimal (SMDM) delivery scheme. In this paper, we investigate the impact of the level of information availability at the upstream non-starving node on the performance of the SMDM scheme. In particular, extensive simulations are conducted for the purpose of quantifying the ability of SMDM to jointly mitigate starvation and achieve minimal end-to-end bundle delivery delay under conditions of perfect, imperfeN/