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A Digital Twin-based Intelligent Network Architecture for Underwater Acoustic Sensor Networks by Song, Shanshan; Huangfu, Bingwen; Guo, Jiani; Liu, Jun; Cui, Junhong; Xuemin; Shen is a scholarly article available to read on EtoBox.

What is A Digital Twin-based Intelligent Network Architecture for Underwater Acoustic Sensor Networks about?

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 paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement lea

Author

Song, Shanshan; Huangfu, Bingwen; Guo, Jiani; Liu, Jun; Cui, Junhong; Xuemin; Shen

Published

2024

Language

EN