The importance of vehicle ad hoc networks (VANETs) lies in their ability to improve traffic monitoring, enhance road safety, and provide in-car infotainment. However, these networks face significant challenges, such as frequent disconnections between vehicles due to high mobility, limited bandwidth, roadside obstructions, and a scarcity of roadside units. Effective routing becomes a critical aspect in addressing these issues. In this paper, a novel approach utilizing a reinforcement learning (RL) strategy based on the Q-learning algorithm is presented. The objective is to use RL to enable vehicles to establish and maintain a stable connection even in the presence of multiple roadside units, thereby mitigating the problem of frequent disconnections. This innovative solution aims to enhance the overall performance of VANETs, & hence contributing to more seamless and efficient V2V2I communication among vehicles on the road.
V2V2I VANET Data Offloading Path Using Reinforcement Learning
Lect. Notes in Networks, Syst.
International Conference on ICT for Sustainable Development ; 2024 ; Goa, India August 08, 2024 - August 09, 2024
03.05.2025
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch