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

G communication plays a crucial role in enabling high-speed, low-latency data transfer for underwater operations. Underwater communications need optimization support to overcome various problems such as packet loss and latency. This study presents a simulation model for underwater 6 G 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 6 G network architectures in underwater environments can be effectively modeled and optimized by incorporating environmental conditions.