Deep Learning-Based Enhanced Cluster Head Selection for Underwater Wireless Sensor Networks

Các tác giả

Email tác giả liên hệ:

thept@hcmute.edu.vn

DOI:

https://doi.org/10.54644/jte.2025.1837

Từ khóa:

Underwater Wireless Sensor Networks, Deep Learning, Cluster Head Selection, Energy Efficiency, Graph Attention Networks

Tóm tắt

Underwater wireless sensor networks (UWSNs) are subject to unique operational challenges, including constrained energy availability, dynamic topological structures, and unreliable acoustic communication. These factors significantly impact the efficiency and stability of data collection processes, particularly the selection of cluster heads (CHs), which plays a vital role in prolonging network functionality. This paper presents GAT-CHS, a cluster head selection algorithm that integrates graph-based attention mechanisms with deep reinforcement learning to adaptively optimize clustering decisions in underwater environments. The proposed approach encodes critical node attributes into a spatial–topological representation, applies multi-head attention to quantify inter-node relevance, and utilizes a Deep Q-Network (DQN) to determine CH roles based on long-term network performance. The algorithm is evaluated across a range of simulated UWSN scenarios reflecting varying node distributions and environmental conditions. Results show that GAT-CHS reduces energy usage by 24%, improves network longevity by 36%, and achieves a packet delivery ratio of 98.6%. These findings underscore the model’s robustness and scalability, establishing GAT-CHS as a promising direction for next-generation clustering in complex underwater sensor deployments.

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Tiểu sử của Tác giả

The Phan Thi, Ho Chi Minh City University of Technology and Education, Vietnam

The Phan Thi was born in Vietnam in 1982. She received Master Data Transmission and Network in Post & Telecommunications Institute of Technology(Ptit), Vietnam, 2012, She got PhD degree PhD in Information System from Post & Telecommunications Institute of Technology, Vietnam in 2022. She is working as a lecture in Faculty of Information Technology, University of Technology and Education, HCM Vietnam. Her research interests include WSN, artificial intelligence, machine learning, data mining.

Email: thept@hcmute.edu.vn. ORCID:  https://orcid.org/0009-0004-0251-5152

Thi Trang Le, Dong Nai Technology University, Vietnam

Thi Trang Le received a Master's degree in Information Technology from Lac Hong University, Vietnam, and is currently working as a lecturer at the Faculty of Information Technology, Dong Nai Technology University, Bien Hoa City, Vietnam. Her research interests include Computer Science, Computer Vision, Image Recognition and Classification, Face Detection and Recognition, Abnormal Motion Detection, and Graphic Design. You can contact her via:

Email: lethitrang@dntu.edu.vn. ORCID:  https://orcid.org/0009-0008-8407-7545

Thanh Son Nguyen, Ho Chi Minh City University of Technology and Education, Vietnam

Thanh Son Nguyen is the head of Information System Division at Faculty of Information Technology, University of Technology and Education, HCM Vietnam. He got PhD degree from University of Technology, HCM, Vietnam. His research interests include artificial intelligence, machine learning, data mining, and time series. He can be contacted at:

Email: sonnt@hcmute.edu.vn. ORCID:  https://orcid.org/0000-0001-9414-3456

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Tải xuống

Đã Xuất bản

2025-08-28

Cách trích dẫn

[1]
The Phan Thi, Thi Trang Le, và Thanh Son Nguyen, “Deep Learning-Based Enhanced Cluster Head Selection for Underwater Wireless Sensor Networks”, JTE, vol 20, số p.h 03, tr 68–77, tháng 8 2025.

Số

T. 20 S. 03 (2025)

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