Date of Award

9-27-2023

Publication Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Congestion Control;Q-Learning;Reinforcement Learning;VANET

Supervisor

Arunita Jaekel

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

To enhance road safety, Vehicular ad hoc networks (VANETs), an emerging wireless technology used for vehicle-to-vehicle and vehicle-to-infrastructure communication, are essential components to reduce road accidents and traffic congestion in Intelligent Transportation Systems (ITS). It also provides additional services to vehicles and their users. However, vehicles must balance awareness and congestion control in a dynamic environment to efficiently transmit basic safety messages (BSMs) and event-driven warnings. The limited channel capacity makes the reliable delivery of BSMs a challenging problem for VANETs. This paper aims to optimize the performance of VANETs by effectively managing channel load and reducing congestion by maintaining the channel busy ratio (CBR) near the threshold value of 0.6. This is resolved using a transmission power-based congestion control algorithm that employs a Markov decision process (MDP) and solves it using a Q-Learning algorithm. The algorithm uses varying transmission power levels to lower the channel busy ratio while maintaining high awareness for surrounding vehicles. According to simulation results for various traffic scenarios, the suggested technique chooses a suitable transmission power depending on the present channel circumstances to achieve a balance between awareness and bandwidth usage. The findings show that the proposed strategy reliably maintained the channel load at or near the stipulated level without surpassing it for both low and high traffic densities.

Download

Share

COinS