A Digital Twin-based Intelligent Network Architecture for Underwater Acoustic Sensor Networks
Shanshan Song, Bingwen Huangfu, Jiani Guo, Jun Liu, Junhong Cui, and, Xuemin (Sherman) Shen

TL;DR
This paper introduces a Digital Twin-based network architecture for underwater acoustic sensor networks, significantly enhancing adaptability, intelligence, and multifunctionality through layered design, AI acceleration, and collaborative frameworks.
Contribution
It proposes a novel layered digital twin architecture with resource allocation, multi-agent reinforcement learning, and network slicing to improve UASN performance and flexibility.
Findings
Improved timeliness and robustness of resource allocation
Reduced AI training time significantly
Faster network status acquisition for multi-task scheduling
Abstract
Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems
