Wednesday, April 12, 2023 - 11:00 to 12:30
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present…
PhD Dissertation Proposal by: Xiaofeng Liu
Date: Wednesday April 12, 2023
Time: 11:00 am – 12:30 pm
Location: Essex Hall, Room 122
Reminders: 1. Two-part attendance mandatory (sign-in sheet, QR Code) 2. Arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED. Note that due to demand, if the room has reached capacity, even if you are "early" admission is not guaranteed. 3. Please be respectful of the presenter by NOT knocking on the door for admittance once the door has been closed whether the presentation has begun or not (If the room is at capacity, overflow is not permitted (ie. sitting on floors) as this is a violation of the Fire Safety code). 4. Be respectful of the decision of the advisor/host of the event if you are not given admittance. The School of Computer Science has numerous events occurring soon.
Road traffic safety is a persistent issue that has been the subject of study for many years. In addition to driver behavior and attitudes, improving vehicle safety through inter-vehicular communication is a crucial aspect. Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users. To achieve reliable delivery of periodic status information, referred to as basic safety messages (BSMs) and event-driven alerts, vehicles need to manage the conflicting requirements of situational awareness and congestion control in a dynamic environment. The limited channel capacity and high message rates needed to ensure an adequate level of awareness make the reliable delivery of BSMs a challenging problem for VANETs. Traditionally, adjusting transmission rate or power can have a negative impact on awareness, while a combination of both is an ideal solution but with an optimization problem that is not convex. In this dissertation, we first propose a decentralized congestion control algorithm that uses variable transmission power levels to reduce the channel busy ratio while maintaining a high level of awareness for nearby vehicles. The simulation results indicate that the proposed approach is able to achieve a suitable balance between awareness and bandwidth usage. The reliable delivery of safety messages in V2V communication requires the maintenance of channel congestion below a critical level. At the same time, an increased transmission rate is necessary for higher awareness. To address this challenge, we present an innovative RL framework that employs Q-learning to determine the most suitable transmission rate policy for BSMs. The results showed that the proposed approach outperformed the other approaches by consistently maintaining the channel load at or near the specified level, without exceeding it, for both low and high traffic densities.
Keywords: VANETs, Congestion Control, V2V, Reinforcement Learning
Internal Reader: Dr. Jianguo Lu
Internal Reader: Dr. Sherif Saad
External Reader: Dr. Huapeng Wu
Advisor: Dr. Arunita Jaekel
PhD Dissertation Proposal Announcement
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