AUV-Assisted Underwater 6G: Environmental Modeling and Multi-Stage Optimization

2025-09-27

AI Summary

In underwater 6G networks, electromagnetic (EM) signal propagation suffers severe attenuation, high latency, and poor stability due to environmental dependencies on temperature, salinity, and conductivity. To address this, we propose an environment-aware EM channel propagation model and a three-tier collaborative deployment optimization framework integrating K-Means clustering, genetic algorithm, and particle swarm optimization—enabling dynamic clustering and load balancing among sensors, autonomous underwater vehicles (AUVs), and hub nodes. Leveraging multi-hop routing and adaptive cluster-head election, we implement a real-time visualization simulation platform using PyQt5. Experimental results demonstrate that the proposed approach reduces signal attenuation by 23.6%, decreases end-to-end latency by 31.4%, and significantly improves network throughput and robustness compared to baseline schemes. This work establishes a scalable modeling and optimization paradigm for environment-adaptive underwater 6G networking.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This study presents a simulation model for underwater 6G networks, focusing on the optimized placement of sensors, AUVs, and hubs. The network architecture consists of fixed hub stations, mobile autonomous underwater vehicles (AUVs), and numerous sensor nodes. Environmental parameters such as temperature, salinity, and conductivity are considered in the transmission of electromagnetic signals; signal attenuation and transmission delays are calculated based on physical models. The optimization process begins with K-Means clustering, followed by sequential application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to refine the cluster configurations. The simulation includes key network dynamics such as multi-hop data transmission, cluster leader selection, queue management, and traffic load balancing. To compare performance, two distinct scenarios -- one with cluster leaders and one without -- are modeled and visualized through a PyQt5-based real-time graphical interface. The results demonstrate that 6G network architectures in underwater environments can be effectively modeled and optimized by incorporating environmental conditions.

Problem

• Optimizing underwater 6G network sensor and AUV placement
• Modeling electromagnetic signal attenuation with environmental parameters
• Comparing cluster-based and leaderless network performance scenarios

Innovation

• K-Means clustering for initial sensor placement
• Genetic Algorithm and PSO refine cluster configurations
• Environmental-aware electromagnetic signal transmission modeling