A Drift-Aware Clustering and Recovery Strategy for Surface-Deployed Wireless Sensor Networks in Ocean Environments

Area of Science:

• Marine technology
• Network engineering
• Oceanography

Background:

• Wireless sensor networks (WSNs) face connectivity challenges on the sea surface due to node drift from currents and wind.
• Dynamic maritime environments require robust, long-range, low-latency sensing systems for stable data transmission.

Purpose of the Study:

• To propose a novel wireless sensor network architecture, Drift-Aware Routing and Clustering with Recovery (DARCR), for sea surface drifting nodes.
• To enhance communication stability, reduce latency, and ensure persistent data transmission in dynamic marine conditions.

Main Methods:

• Developed an enhanced dynamic drift model for accurate prediction of node movement.
• Implemented a cluster-based framework with refined clustering and route setup for improved stability and adaptability.
• Integrated a self-recovery routing strategy to re-establish communication links post-disconnection.

Main Results:

• The proposed DARCR system achieved an average hourly disconnection rate of 6.2% with a variance of 0.31%.
• Data transmission for newly sensed information was completed within 3 to 5 seconds, with a maximum delay of approximately 10 seconds.
• Demonstrated the feasibility of maintaining stable, low-latency communication for WSNs in highly dynamic sea states.

Conclusions:

• The DARCR architecture effectively addresses the challenges of node drift and communication disruption in maritime WSNs.
• The system provides a reliable and efficient solution for persistent sensing and data transmission on the sea surface.
• The findings support the deployment of advanced WSNs for enhanced maritime monitoring and data collection.