Browsing by Subject "V2V"

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Modeling, analysis, and design of collaborative services in vehicular and cloud/edge networks
(2022-05-02) Kassir, Saadallah; De Veciana, Gustavo; Andrews, Jeffrey G; Caramanis, Constantine; Leibowicz, Benjamin D; Shakkottai, Sanjay
The new wireless network technologies introduced in the fifth generation of cellular networks (5G) have enabled the development of various classes of mobile applications. This thesis investigates how these emerging mobile use-cases can make the most of the state-of-the-art wireless and computing technologies through effective collaborative network management and operations strategies. We study two general classes of services: (1) collaborative traffic relaying in vehicular ad-hoc networks (VANETs), aiming at providing highly available, fair and reliable connectivity/throughput to the network users; and (2) collaborative real-time services, aimed at providing devices with low-latency and high availability/reliability connectivity. In the first part of this thesis, we study VANETs and propose a novel vehicle connectivity framework wherein vehicles within communication range of each other form vehicle clusters, allowing them to opportunistically route traffic from/to each other. With the formation of these logical entities, vehicles can be viewed as mobile relay nodes, and have the potential to substantially improve the coverage and per-user throughput of the vehicular network. In this setting, we begin by presenting an analytical framework to study the performance gains enabled by this network architecture on a single road, and we show that vehicle clustering leads to considerable benefits including reduced throughput variability and improved coverage. We then look at larger-scale cellular networks and leverage results from the stochastic geometry literature to show that the proposed opportunistic vehicle clustering and relaying scheme has the potential to improve the throughput for both vehicles and non-vehicle-bound users by more than an order of magnitude through opportunistic relaying and cell load-balancing. Finally, we study wireless resource allocation mechanisms leading to improvements in shared-rate fairness among the network users. In the second part of this thesis, we study the operation of networks supporting real-time services, with a focus on devising efficient and timely information sharing mechanisms among the interconnected entities. We first examine how joint management of wireless communication and cloud/edge-computing resources can improve the timeliness of the information shared over the network, while reducing network resource provisioning costs. We investigate tradeoffs associated with status-update rate adaptation and service placement in the Cloud-to-Thing continuum for devices running real-time applications, and develop associated algorithms aiming at controlling the network congestion and improving the service availability. We argue that sending more information might be detrimental to its quality, and that various application-specific properties influence the service placement decision in the Cloud-to-Thing continuum. We then examine the performance of real-time multi-user services via the specific example of Multiplayer Cloud Gaming (MCG), and exhibit how joint rate adaptation is key to controlling congestion and providing a high quality of service in spite of spatio-temporal variations in the network delays particularly impacting massive multi-user services. Finally, we give particular attention to timely information sharing in collaborative-sensing vehicular networks. We introduce a communication-efficient information-sharing mechanism enabling vehicles to benefit from each other’s sensing capability in real-time via a centralized node (e.g., edge compute node, a cellular base station, or a road side unit). Our proposed mechanism opportunistically improves the vehicles’ situational awareness when assistance is available, allowing them, for instance, to drive at a faster speed without compromising on safety.
Potential impacts of connected-autonomous vehicles on congestion and safety : a look at Austin, Texas
(2017-05-02) Archer, Jackson Longstreet; Zhang, Ming, 1963 April 22-; Jiao, Junfeng
Data is a central component of Connected-Autonomous Vehicle (CAV) systems: the advantages and potential challenges of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) CAV data underlie the question of wide scale CAV implementation. This report looks at the potential congestion and safety benefits of a vehicle system highly saturated with CAVs in Austin, Texas. Traffic factors such as capacity, intersection delay, and crash rate are examined with respect to their effect on an urban corridor in Austin. The case study relies almost entirely on collected field data to be used as a comparison against potential CAV advantages. In addition to a presentation of the quantitative benefits of CAVs, an infrastructure placement scheme that maximizes data transmission efficiency is also proposed. The results find that vehicle systems can see large improvements in capacity, intersection delay, and number of crashes, and at a relatively inexpensive cost.