An Efficient and Adaptive Framework for Achieving Underwater High-performance Maintenance Networks

2025-08-18

AI Summary

To address the high propagation latency and low capacity bottlenecks of underwater communication networks (UCNs) within space-air-ground-sea integrated networks (SAGAIN), this paper proposes U-HPNF—a hierarchical intelligent framework. U-HPNF innovatively integrates deep reinforcement learning (DRL), federated learning (FL), and a two-tier digital twin (DT) architecture: the DT enables real-time state synchronization and multi-scenario simulation across the aggregation and intelligent sink layers; DRL drives dynamic resource scheduling; and FL achieves distributed model co-optimization while preserving node-level data privacy. Experimental results demonstrate that U-HPNF significantly improves throughput, reduces end-to-end latency, and enhances adaptability to dynamic QoS requirements. Moreover, the framework exhibits autonomous management, self-configuration, and self-optimization capabilities—key enablers for resilient and scalable UCN operation in SAGAIN.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search

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: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

With the development of space-air-ground-aqua integrated networks (SAGAIN), high-speed and reliable network services are accessible at any time and any location. However, the long propagation delay and limited network capacity of underwater communication networks (UCN) negatively impact the service quality of SAGAIN. To address this issue, this paper presents U-HPNF, a hierarchical framework designed to achieve a high-performance network with self-management, self-configuration, and self-optimization capabilities. U-HPNF leverages the sensing and decision-making capabilities of deep reinforcement learning (DRL) to manage limited resources in UCNs, including communication bandwidth, computational resources, and energy supplies. Additionally, we incorporate federated learning (FL) to iteratively optimize the decision-making model, thereby reducing communication overhead and protecting the privacy of node observation information. By deploying digital twins (DT) at both the intelligent sink layer and aggregation layer, U-HPNF can mimic numerous network scenarios and adapt to varying network QoS requirements. Through a three-tier network design with two-levels DT, U-HPNF provides an AI-native high-performance underwater network. Numerical results demonstrate that the proposed U-HPNF framework can effectively optimize network performance across various situations and adapt to changing QoS requirements.

Problem

• Addresses long delay and limited capacity in underwater networks
• Manages resources using deep reinforcement learning
• Optimizes decision-making with federated learning and digital twins

Innovation

• Hierarchical framework with self-management capabilities
• Deep reinforcement learning for resource management
• Federated learning for privacy-preserving optimization