Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework.

Area of Science:

• Computer Science
• Electrical Engineering
• Data Science

Background:

• Wireless Sensor Networks (WSNs) face challenges in data gathering due to intermittently active nodes.
• Inactive nodes in WSNs are often treated as non-existent, leading to data loss.
• Existing Matrix Completion (MC) techniques struggle with significant data gaps, especially missing rows.

Purpose of the Study:

• To develop a novel data gathering technique for WSNs using Matrix Completion (MC).
• To address the challenge of recovering data from inactive nodes in WSNs.
• To improve the efficiency and accuracy of data reconstruction in WSNs with sparse measurements.

Main Methods:

• Proposed a novel Matrix Completion (MC) methodology tailored for WSN data gathering.
• Developed a clustering technique considering inter-node correlations to handle missing data.
• Introduced a minimization problem-based interpolation technique for recovering inactive node readings.

Main Results:

• The proposed MC technique effectively recovers data from inactive WSN nodes.
• The combined sampling and reconstruction pattern demonstrated significant improvements.
• Simulations confirmed the validity of individual components and the overall approach's efficiency.

Conclusions:

• The novel MC-based approach successfully reconstructs WSN data matrices with missing rows.
• The technique outperforms existing methods in recovering data from intermittently active nodes.
• This work provides an efficient solution for data gathering in WSNs with sparse and structured data loss.