Work

Reinforcement Learning-Based ALOHA-Q Protocol with Slot Subdivision for Underwater Acoustic Sensor Networks

Year: 2025

Type: conference-paper

Abstract: This paper presents SS-ALOHA-Q, a Reinforcement Learning-based MAC protocol that builds upon slotted ALOHA by introducing slot subdivision to effectively mitigate the impact of large propagation delays in Underwater Acoustic Sensor Networks (UASNs). The protocol leverages reinforcement learning to dynamically optimize channel access, enabling adaptive scheduling and responsive decision-making in highly variable underwater environments. Performance evaluation was conducted using the DESERT simulator, where a Hidden Markov Model (HMM) was employed to model underwater acoustic channel dynamics. Comparative analysis against benchmark MAC protocols demonstrates that SS-UWALOHA-Q significantly improves channel utilization while maintaining energy efficiency, demonstrating its potential for robust and efficient UASN operations.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15072163

Authors: Albijanic, Aleksa, Tomović, Slavica, Radusinović, Igor

Institution: University of Montenegro

Language: English

Topic: Underwater Vehicles and Communication Systems

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: MONtenegrin center for Underwater SEnsor Networks