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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT
Shiv Nath Chaudhri1, Navin Singh Rajput1, Saeed Hamood Alsamhi2,3
1Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.
Sensors (Basel, Switzerland)
|April 23, 2022
Summary
Researchers optimized gas sensor arrays for 6G-IoT using Convolutional Neural Networks (CNNs). This reduced power consumption by 50% while maintaining high accuracy for hazardous gas detection, enabling edge computing.
Area of Science:
- Sensor Technology
- Artificial Intelligence
- Internet of Things
Background:
- Ultra-low-power consumption is critical for 6G-IoT ecosystems.
- Sensor nodes in 6G-IoT can execute lightweight AI models.
- Gas sensor arrays are essential for environmental monitoring.
Purpose of the Study:
- To reduce power consumption in gas sensor systems for 6G-IoT.
- To maintain high performance in gas sensing with fewer sensor elements.
- To enable edge computation in resource-constrained environments.
Main Methods:
- Identified and removed redundant gas sensor elements in an array.
- Applied specialized data pre-processing techniques (zero-padded virtual sensors, spatial augmentation).
- Utilized Convolutional Neural Networks (CNNs) for classification and quantification.
Main Results:
- Reduced power consumption from 10 Watts to 5 Watts.
- Achieved 100% classification performance for four hazardous gases.
- Maintained quantification performance with a Mean Squared Error (MSE) of 1.12 × 10-2.
Conclusions:
- Power-efficient optimization of gas sensor arrays is feasible for 6G-IoT.
- CNNs and data pre-processing can compensate for reduced sensor elements.
- The approach supports edge computation in resource-constrained 6G-IoT paradigms.
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