ORIGINAL RESEARCH article

Front. Mar. Sci.

Sec. Marine Conservation and Sustainability

Adaptive Energy-Efficient and Secure Clustering-Based Routing Architecture for Underwater Wireless Sensor Networks in Marine Environmental and Ecosystem Monitoring

  • 1. Chitkara University, Chandigarh, India

  • 2. Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

  • 3. University of Jeddah, Jeddah, Saudi Arabia

  • 4. Saveetha Institute of Medical and Technical Sciences, chennai, India

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Abstract

Reliable long-term monitoring of coral reefs and other marine ecosystems is limited by the harsh underwater environment, restricted battery capacity of sensor nodes, and the high energy cost of acoustic communication. This study proposes an integrated architecture for Underwater Wireless Sensor Networks (UWSNs) that enhances energy efficiency, security, and data reliability for marine environmental monitoring. The framework combines a hybrid Adaptive Swarm Fitness Optimization– Golden Eagle Optimizer (ASFO–GEO)–based K-Medoids (KM) clustering algorithm with a Tiny Security (TinySec)–enabled Energy-aware Coral-Environmental Reliable Path (E-CERP) routing protocol and Autonomous Underwater Vehicle (AUV)–assisted data collection. The ASFO–GEO–KM mechanism selects optimal Cluster Heads (CHs) based on residual energy, underwater link quality, and node density, improving load balancing and cluster stability. TinySec-enabled E-CERP ensures authenticated, energy-aware multi-hop routing while accounting for underwater path loss and propagation delay. AUVs periodically retrieve aggregated data from CHs, reducing long-range acoustic transmissions and conserving node energy. Simulation results in a realistic 3D marine environment show that the proposed framework outperforms existing methods, including DEDG, AP, ALP, HECRA, GSA, and CTRGWO-CRP, achieving higher network lifetime, improved packet This is a provisional file, not the final typeset article delivery ratio, and significantly lower routing overhead. By enabling long-term, secure, and energy-efficient sensing, the proposed architecture directly supports coral reef health assessment, early detection of environmental stressors such as thermal anomalies and turbidity spikes, and reliable real-time data acquisition for marine ecosystem protection and conservation-oriented decision-making. Overall, the proposed architecture provides an efficient and scalable solution for sustained coral reef monitoring and broader marine observation applications.

Summary

Keywords

AI, ASFO–GEO Optimization, Autonomous Underwater Vehicles, Coral reef monitoring, Energy-efficient routing, Hybrid clustering, Real-time environmental sensing, TinySec Security

Received

15 December 2025

Accepted

17 February 2026

Copyright

© 2026 Sharma, Bharany, Gaber, Alamoudi and Rehman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jyoti Sharma; Salil Bharany

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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