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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
Boopathi Chettiagounder Sengodan1, Prince Mary Stanislaus2, Sivakumar Sabapathy Arumugam3
1Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603 203, Tamil Nadu, India.
Sensors (Basel, Switzerland)
|September 14, 2024
Summary
This study introduces a novel deep learning method using variational autoencoders (VAEs) to enhance energy efficiency in wireless sensor networks (WSNs) by compressing data. The VAE approach significantly improves network lifetime and data compression rates compared to traditional methods.
Area of Science:
- Computer Science
- Electrical Engineering
- Network Engineering
Background:
- Wireless sensor networks (WSNs) are crucial for monitoring but suffer from high energy consumption during data transmission.
- Existing energy-saving methods include routing optimization, topology control, and sleep scheduling.
Purpose of the Study:
- To introduce a novel deep learning-based method for improving WSN energy efficiency.
- To leverage variational autoencoders (VAEs) for effective data compression in WSNs.
Main Methods:
- A customized variational autoencoder (VAE) model was developed to compress WSN data by analyzing its statistical structure.
- The VAE model was integrated with openly available WSN data and simulated using MATLAB.
- Performance was evaluated against traditional methods like compressed sensing and autoencoders.
Main Results:
- The proposed VAE method achieved an average compression rate of 1.5572, outperforming traditional techniques.
- The VAE-incorporated architecture resulted in a maximum network lifetime of 1491 seconds.
- A high reconstruction rate of 0.9902 was achieved, indicating effective data preservation.
Conclusions:
- Variational autoencoders (VAEs) offer a promising approach for compression-based data transmission in WSNs.
- The VAE method significantly enhances energy efficiency and network lifetime.
- The VAE's superior data reconstruction rate validates its effectiveness over other compression techniques.
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