An Enhanced Q-Learning MAC Protocol for Energy Efficiency and Convergence in Underwater Sensor Networks

Under-Water Sensor Networks (UWSNs) are important for applications like oceanographic data collection, environmental protection, monitoring, and disaster response. These UWSN networks face challenges in energy efficiency and protocol convergence because of dynamic underwater ecosystems limited in number and low channel capacity. This paper covers and finds real-world solutions for these challenges by proposing an adaptive Q-Learning Medium Access Control (MAC) protocol for UWSNs. The methodology used in this paper involves utilizing Q-Learning, a reinforcement learning technique, to make sensor nodes autonomously refine their transmission strategies in real-time, enhancing energy consumption and improving protocol convergence. The protocol was implemented using the NS 3 network simulator, which offers a detailed and real-world environment for analyzing the protocol’s performance. Extensive simulations were conducted, and experiments were used to analyze the performance of the proposed protocol. The results showcase significant improvements over other and traditional MAC protocols, with a 13% to 19% increase in energy efficiency and channel utilization enhancement in static and mobile network scenarios. The adaptive Q-Learning MAC protocol provides a robust solution for the challenges of UWSNs, offering energy efficiency and faster convergence times. This research significantly contributes to the advancement of efficient and adaptive underwater communication protocols, paving the way for future development in the field