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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Q-Learning-Based Pending Zone Adjustment for Proximity Classification.

Jung-Hyok Kwon¹, Sol-Bee Lee², Eui-Jik Kim²

¹Smart Computing Laboratory, Hallym University, 1 Hallymdaehak-gil, Chuncheon 24252, Gangwon-do, Republic of Korea.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This study introduces Q-learning-based pending zone adjustment (QPZA) to enhance proximity classification accuracy using received signal strength indicator (RSSI). QPZA adaptively adjusts the pending zone, improving performance in dynamic environments.

Keywords:

Q-learning pending zone proximity classification proximity-based services received signal strength indicator

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Area of Science:

Computer Science
Machine Learning
Wireless Communication

Background:

Received Signal Strength Indicator (RSSI) is commonly used for proximity classification.
Existing RSSI-based methods struggle with accuracy due to environmental changes and noise.
The 'pending zone' in classification maintains previous results but needs dynamic adjustment.

Purpose of the Study:

To improve the accuracy of RSSI-based proximity classification.
To introduce an adaptive method for adjusting the pending zone in RSSI classification.
To leverage Q-learning for dynamic environmental adaptation in proximity sensing.

Main Methods:

Developed Q-learning-based Pending Zone Adjustment (QPZA).
QPZA adaptively adjusts the near and far boundaries of the pending zone.
Noise level is calculated using estimation error; Q-learning agent and reward calculator manage boundary adjustments.

Main Results:

QPZA demonstrated improved accuracy in experimental implementations.
Achieved an average accuracy improvement of 11.69% compared to existing approaches.
The adaptive adjustment of the pending zone effectively handles environmental noise variations.

Conclusions:

QPZA offers a significant improvement in RSSI-based proximity classification accuracy.
The Q-learning approach provides robust adaptation to changing environmental conditions.
QPZA represents a promising advancement for precise proximity sensing applications.