Article
COMPARISON OF TRADITIONAL AND REINFORCEMENT LEARNING BASED ROUTING PROTOCOLS IN VANET SCENARIO
DOI: 10.7708/ijtte2023.13(1).10
13 / 1 / 125-137 Pages
Author(s)
Nenad Jevtić - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia -
Abstract
Vehicular ad hoc networks (VANETs) are characterized by high mobility of nodes and frequent changes in the network topology, which significantly complicates the process of routing data packets. It has been shown that traditional routing protocols are unable to promptly follow these changes and cannot be efficiently used in VANETs for vehicle to vehicle (V2V) communications. This is the reason why protocols based on reinforcement learning (RL) have been developed. These protocols enable constant monitoring of changes in the network environment, and adaptation of the routing process to those changes. In this paper, an analysis and comparison of the traditional and RL based routing protocols are performed in VANET scenario. The Ad hoc on-demand distance vector routing protocol (AODV) and AODV with Expected transmission count (ETX) metric are chosen as the representatives of traditional routing protocols, while the Adaptive routing protocol based on reinforcement learning (ARPRL) is chosen as the representative of routing protocols based on RL. The simulation results show that the ARPRL protocol has significantly better network performance in terms of packet loss ratio (PLR) and end-to-end delay (E2ED) in urban VANET scenario.
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