MSc Thesis Proposal Announcement of Gnana Shilpa Nuthalapati:"Data Rate-Based Congestion Control in VANET using Q-Learning algorithm"

Friday, March 3, 2023 - 13:00 to 14:30


The School of Computer Science is pleased to present…

MSc Thesis Proposal by: Gnana Shilpa Nuthalapati

Date: Friday March 3rd, 2023
Time:  1:00 PM – 2:30 PM
Location: DH 361 (Dillon Hall)
1. Two-part attendance mandatory (sign-in sheet, QR Code)
2. Arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED. Due to demand, if the room has reached capacity, even if you are "early" admission is not guaranteed.
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Vehicular Ad-Hoc Network (VANET) is an emerging wireless technology used for vehicle-to-vehicle and vehicle-to-infrastructure communication and is crucial for avoiding road accidents and traffic congestion in an Intelligent Transportation System (ITS). When the vehicle density, i.e., the number of vehicles communicating increases, the communication channel faces congestion resulting in unreliable safety applications. Various decentralized congestion control algorithms have been proposed to effectively decrease the channel congestion by controlling different transmission parameters like message rate, transmission power, beacon Interval and data rate. In this research, we propose a Data rate-based congestion control technique using Q-Learning algorithm to reduce and maintain the channel load near the channel threshold (0.6). Q-Learning algorithm is a model-free reinforcement learning technique that has a set of states (vehicle densities) and actions (data rates) and will find the best action for each state. We use Q-Learning algorithm with the data obtained from a simulated dynamic traffic environment. We define a reward function combining CBR and Data rate to maintain the channel load near the target threshold with the least data rate possible. Simulation results show that, Data rate-based congestion control technique using Q-Learning algorithm performs better over other techniques like Transmit Data rate Control (TDRC) and Data Rate based Decentralized Congestion Control (DR-DCC) by reducing the Channel Busy Ration (CBR) and resulting in less packet loss.
Keywords: VANET, Congestion Control, Reinforcement Learning, Q-Learning

MSc Thesis Committee:
Internal Reader: Dr. Shaoquan Jiang      
External Reader: Dr. Kevin Li     
Advisor: Dr. Arunita Jaekal
Co-Advisor: Dr. Ning Zhang

MSc Thesis Proposal Announcement


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