CS²-Collector: A New Approach for Data Collection in Wireless Sensor Networks Based on Two-Dimensional Compressive...

Area of Science:

• Computer Science
• Electrical Engineering
• Signal Processing

Background:

• Wireless Sensor Networks (WSNs) are crucial for monitoring physical phenomena.
• Energy efficiency is a major challenge in WSN data collection.
• Existing compressive sensing methods in WSNs often consider only temporal or spatial sparsity.

Purpose of the Study:

• To propose a novel data collection strategy for WSNs that improves energy efficiency.
• To enhance the reconstruction accuracy of physical phenomena monitored by WSNs.
• To leverage both temporal and spatial sparsity for data collection in WSNs.

Main Methods:

• The study proposes CS²-collector, a strategy based on Two Dimensional Compressive Sensing (2DCS).
• This method exploits the 2D-sparsity (temporal and spatial) of sensor data.
• Numerical simulations and evaluations were performed on diverse sensor data types.

Main Results:

• CS²-collector achieves significant gains in the tradeoff between compression ratio and reconstruction accuracy.
• The proposed strategy offers more accurate reconstruction of temporal-spatially sparse phenomena compared to existing methods.
• Evaluations demonstrate the effectiveness of CS²-collector across different sensor data.

Conclusions:

• CS²-collector provides an energy-efficient solution for data collection in WSNs.
• Exploiting 2D-sparsity through 2DCS significantly improves reconstruction performance.
• The proposed method offers a practical approach for accurate monitoring of physical phenomena in WSNs.