Performance Analysis of Cooperative V2V and V2I Communications under Correlated Fading

Cooperative vehicular networks will play a vital role in the coming years to implement various intelligent transportation-related applications. Both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications will be needed to reliably disseminate information in a vehicular network. In this regard, a roadside unit (RSU) equipped with multiple antennas can improve the network capacity. While the traditional approaches assume antennas to experience independent fading, we consider a more practical uplink scenario where antennas at the RSU experience correlated fading. In particular, we evaluate the packet error probability for two renowned antenna correlation models, i.e., constant correlation (CC) and exponential correlation (EC). We also consider intermediate cooperative vehicles for reliable communication between the source vehicle and the RSU. Here, we derive closed-form expressions for packet error probability which help quantify the performance variations due to fading parameter, correlation coefficients and the number of intermediate helper vehicles. To evaluate the optimal transmit power in this network scenario, we formulate a Stackelberg game, wherein, the source vehicle is treated as a buyer and the helper vehicles are the sellers. The optimal solutions for the asking price and the transmit power are devised which maximize the utility functions of helper vehicles and the source vehicle, respectively. We verify our mathematical derivations by extensive simulations in MATLAB.Comment: Internet of Vehicles (IoV), Vehicular communication, Antenna correlation, Stackelberg game, Vehicle-to-infrastructure (V2I), Vehicle-to-vehicle (V2V), Game theory, Cooperative vehicular network

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field

Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV

Cyber-Physical Systems (CPSs) are increasingly important in everyday applications including the latest mobile devices, power grids and intelligent buildings. CPS functionality has intrinsic characteristics including considerable heterogeneity, variable dynamics, and complexity of operation. These systems also typically have insufficient resources to satisfy their full demand for specialized services such as data edge storage, data fusion, and reasoning. These novel CPS characteristics require new management strategies to support the resilient global operation of CPSs. To reach this goal, we propose a Software Defined Networking based solution scaled out by Network Function Virtualization modules implemented as distributed management agents. Considering the obvious need for orchestrating the distributed agents towards the satisfaction of a common set of global CPS functional goals, we analyze distinct incentive strategies to enact a cooperative behavior among the agents. The repeated operation of each agent’s local algorithm allows that agent to learn how to adjust its behavior following both its own experience and observed behavior in neighboring agents. Therefore, global CPS management can evolve iteratively to ensure a state of predictable and resilient operation

Towards Smart Vehicular Environments via Deep Learning and Emerging Technologies

Intelligent Transportation Systems (ITS) embrace smart vehicular environments through a fully connected paradigm known as vehicular networks. Vehicular networks allow automobiles to stay online and connected with their surroundings while travelling. In that sense, vehicular networks enable various activities; for example, autonomous driving, road surveillance, data collection, content delivery, and many others. This leads to more efficient, safer, and comfort driving experiences and opens up new opportunities for many business sectors. As such, the networking industry and academia have shown great interests in advancing vehicular networks and leveraging relevant services. In this dissertation, several vehicular network problems are addressed along with proposing novel ideas and utilizing effective solutions. As opposed to stationary or slow moving communications, vehicular networks experience more challenging environment as a result of vehicle mobility. Consequently, vehicular networks suffer from ever-changing topology, short contact times, and intractable propagation environments. In particular, this dissertation presents six works that participate in supplementing the literature as follows. First, a content delivery framework in the context of vehicular network is studied where digital contents are generated by different content providers (CP) and have distinct values. To this end, a prefetching technique along with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications are used to enable fast content delivery. Furthermore, a pricing model is proposed to deal with contents' values to attain a satisfactory Quality of Experience (QoE). Second, a more advanced system model is discussed to cache contents with the assistance of vehicles and to enable a disconnected and fixed Road-Side Unit (RSU) to participate in providing content delivery services. The changing popularity of contents is investigated besides accounting for the limited RSU cache capabilities. Third, the stationary RSU proposed in the second work is replaced by a more flexible infrastructure, namely an aerial RSU mounted on an unmanned aerial vehicle (UAV). The mobility of the UAV and its constrained energy capacity are analyzed and Deep Reinforcement Learning is incorporated to aid in solving the challenges in leveraging UAVs. Fourth, the previous two studies are integrated by investigating the collaboration between a UAV and terrestrial RSUs in delivering large-size contents. A strategy to fill up the UAV cache is also suggested via mulling contents over vehicles. Fifth, the complexity of vehicular urban environments is addressed. In particular, the problem of disconnected areas in vehicular environments due to the appearance of high-rise buildings and other obstacles is studied. In details, a Reconfigurable Intelligent Surface (RIS) is exploited to provide indirect links between the RSU and vehicles travelling through such areas. Our sixth and final contribution deals with time-constrained Internet of Things (IoT) devices (IoTD) supporting ITS networks. In this regard, a UAV is dispatched to collect their data timely and fully while being assisted by a RIS to improve the wireless channel quality. In the end, this dissertation provides discussions that highlight open research directions worth of further investigations