A method, device and medium for determining routing of an underwater acoustic sensor network

By combining Q-learning and Dijkstra's algorithms in an underwater acoustic sensor network, routing paths are optimized, solving the data transmission problem under dynamic underwater environment changes. This results in higher packet delivery rate, throughput, and lower latency, improving the network's adaptability and robustness.

CN122437808APending Publication Date: 2026-07-21ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG UNIV
Filing Date
2026-06-23
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

In underwater acoustic sensor networks, existing routing algorithms struggle to achieve reliable, low-latency, and high-efficiency data routing in dynamically changing underwater environments. Classical algorithms tend to select unstable links, leading to a decline in overall network performance, while online learning algorithms consume significant resources and compromise plug-and-play functionality.

Method used

The Q-learning algorithm is used to perform offline iterative training on the initial Q-table to construct an offline link cost matrix. The routing path is optimized by combining the Dijkstra algorithm, integrating historical experience and real-time status, and determining the optimal path by reshaping the online initial link cost matrix.

Benefits of technology

It improves the data transmission adaptability and robustness of underwater acoustic sensor networks, increasing packet delivery rate by 13.7%, throughput by 23.4%, and reducing end-to-end average latency by 11.2%, significantly optimizing communication performance without changing the existing protocol stack.

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Abstract

The application discloses a routing determination method and device of an underwater acoustic sensor network and a medium, relates to the field of underwater acoustic communication and network routing optimization, and comprises the following steps: based on the underwater acoustic sensor network, taking the negative value of an offline link cost matrix as a reward function, and adopting a Q-learning algorithm to perform offline iterative training on an initial Q table, an optimized Q table is obtained; based on an online initial link cost matrix and the optimized Q table, a remodeling formula is adopted to calculate a remodeled online initial link cost matrix; based on an online initial adjacency matrix and the remodeled online initial link cost matrix, a Dijkstra algorithm is adopted to determine an optimal path of the underwater acoustic sensor network, and the determination of the routing of the underwater acoustic sensor network is completed. The application optimizes the balance among the delivery rate, the time delay and the throughput, and improves the adaptability and robustness of data transmission of the underwater acoustic sensor network.
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