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A lightweight hybrid framework for real-time data refinement in resource-constrained underwater and underground
Samia Allaoua Chelloug1, Sajal Nazir2, Rahim Khan3
1Department of Information Technology, College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific Reports
|June 22, 2026
Summary
A new hybrid framework refines data in underwater sensor networks (UWSNs) by using a Kalman filter for missing values and Manhattan distance for duplicates. This improves data quality and network lifetime in challenging environments.
Area of Science:
- Underwater sensor networks
- Data acquisition and processing
- Network performance optimization
Background:
- Harsh underwater environments cause packet loss, latency, and energy scarcity in UWSNs/UGWSNs.
- Data quality issues include missing values and duplicate readings, hindering reliable data acquisition.
- Existing filtering techniques are often complex or address issues separately, unsuitable for resource-constrained networks.
Purpose of the Study:
- To introduce a computationally simple, real-time hybrid data refinement framework for UWSNs/UGWSNs.
- To jointly address missing values and duplicate readings without requiring training data or cloud offloading.
- To enhance data quality and network efficiency in challenging underwater conditions.
Main Methods:
- Utilized a Kalman filter (KF) for efficient imputation of missing data points.
- Employed a Sliding Window based on Manhattan Distance (SWMD) for detecting and filtering redundant entries.
- Implemented and validated the framework in the OMNeT++ simulation environment under realistic UWSN conditions.
Main Results:
- Achieved a Mean Absolute Deviation (MAE) of 1.20 and Root Mean Square Error (RMSE) of 1.75 for missing value estimation.
- Filtered out 20.5% of redundant packets and improved Packet Delivery Ratio (PDR) by up to 88%.
- Extended network lifetime by over two times (up to 122s) while maintaining an average end-to-end delay of 11.1 ms.
Conclusions:
- The proposed hybrid framework effectively refines data quality in UWSNs/UGWSNs by jointly handling missing and duplicate values.
- The computationally simple approach is well-suited for resource-constrained underwater environments, enhancing network lifetime and data reliability.
- Enables robust real-time analytics for long-term monitoring in inaccessible underwater and subterranean systems.
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