Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) face energy constraints, driving research into energy-efficient data acquisition.
• Compressed sensing (CS) offers energy savings via sparse sampling but struggles with timely data.
• Prediction-based methods provide timely data but incur high overhead for model synchronization and sampling.

Purpose of the Study:

• To develop an integrated data acquisition scheme combining compressed sensing and prediction-based approaches for WSNs.
• To address the challenge of adaptively switching between data gathering modes for optimal performance.
• To improve energy efficiency and timely critical event detection in WSNs.

Main Methods:

• Proposed an integration framework for compressed sensing and prediction-based data gathering modes.
• Introduced an adaptive deviation tolerance mechanism to enhance data acquisition flexibility.
• Developed an adaptive switching mechanism to effectively react to changing phenomena without high sampling frequencies.

Main Results:

• The proposed scheme demonstrates good flexibility and scalability in experiments.
• The integrated approach effectively suppresses data transmission while ensuring timely collection of critical data.
• Simultaneous achievement of good energy efficiency and high-quality sensing of critical events was validated.

Conclusions:

• The novel adaptive switching mechanism overcomes limitations of traditional methods in reacting to dynamic changes.
• The proposed data acquisition scheme offers a promising solution for balancing energy efficiency and sensing quality in WSNs.
• The research contributes a flexible and scalable approach for advanced WSN data management.