Seminar Details

Seminar Title
Optimization of MAC Parameters for Message Dissemination in IEEE 802.11p-based Vehicular Ad-hoc NETworks (VANETs)
Seminar Type
Synopsis Seminar
Computer Science and Engineering
Speaker Type
Speaker Name
LOPAMUDRA HOTA ( RollNo : 519CS1015)
Date  &  Time
26 Sep 2023  4:15 PM
Convention Hall
Dr. Arun Kumar

Vehicular Ad-hoc NETworks (VANETs) enable vehicles to exchange safety-based information via broadcasts to get updates of vehicle&rsquos speed, direction and road conditions. Highly dynamic topology, high mobility, and varying traffic density lead to network performance degradation in VANETs. MAC protocols are designed to provide reliable and rapid delivery of safety messages to safer and more efficient vehicles on the road. As vehicle density increases in the VANET environment, MAC protocols adapt to the changing data traffic patterns. The multi-channel access mechanism in MAC adapts to changing vehicular densities, thereby guaranteeing data transmission
and increased throughput for various VANET applications. Providing efficient service with less delay and high throughput is a significant challenge while designing a MAC protocol for VANET.
Therefore, the objective is to counter these challenges, by design of Adaptive Contention Window (CW) schemes to reduce delay in high vehicular density, providing proper channel allocation, and analyzing the current channel environment to adapt the MAC parameter by reinforcement learning.
The first approach is based on the optimization of CW with AIFS scheme to provide reliable and efficient data dissemination. The analytical study is then compared with the traditional 802.11p MAC protocol of VANET. The result of the analysis aid in selecting a CW value for different vehicular densities and analyzing the collision probability. The performance evaluation demonstrates that the optimal CW value reduces the packet collision rate by 50% and access delay by 56%, and maximizes the network throughput by 45%.
This research work proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC, as the second contribution. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collision in uncertain situations. Different obstructing scenarios are identified during the travel, and solutions to these challenging situations are proposed. The Merge and Join maneuvers are performed to help the platoon&rsquos steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel
time, and avoiding collisions.
The third approach dynamically adjusts the CW parameter to maximise the throughput of a vehicular network. To accomplish this, a Reinforcement Learning (RL) framework is formulated. The simulation result shows the proposed learning-based CW mechanism significantly improves the throughput requirements of the traditional IEEE 802.11p standard.
The fourth approach is based on fair channel allocation to packets arriving at the MAC. The channel allocation problem is stated as a Multiple Knapsack Problem (MKP) which is proved to be NP-hard. An Asynchronous Advantage Actor-Critic (A3C) policy model is considered for the learning mechanism.