Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm
Abstract
:1. Introduction
1.1. Motivation and Contributions
- A novel approach for gas sensor node power optimization without significant compromise in performance while classifying and/or quantifying the gases.
- The power-efficient sensor node is a key enabler for “Computation on the edge” for resource-constrained 6G-IoT applications.
- The especially used data pre-processing (zero-padded virtual sensors and spatial augmentation) and CNN-based sensor node optimization can reduce the cost of hardware and effective power consumption without significant deviation in the sensor node’s performance.
- The proposed simpler CNN when used with the zero-padded virtual sensors and spatial augmentation is computationally less complex and is well-suited for “computations on edge” in resource-constrained 6G-IoT environments.
1.2. Paper Structure
2. Materials and Methods
2.1. The Dataset
2.2. Gas Sensor System in Resource-Constrained 6G-IoT Scenarios
2.3. A 2D Convolutional Neural Network (2D-CNN)
2.4. Contextual Outline of Virtual Gas Sensors and Zero-Padding
2.5. Contextual Outline of Spatial Augmentation
2.6. Case-Based Experiments (Baseline and Possible Scenarios)
Algorithm 1 Spatial Augmentation Procedure for Data Up-Scaling | |
1 | function Spatial Augmentation (S) |
Input: Number of sensor elements S in the sensing unit OR Length of raw data vectors | |
Output: The spatially augmented data vectors compatible with the input to the 2D-CNN | |
2 | BEGIN |
3 | IF S is a perfect square number: |
4 | Represent the data vector in the squared array of size: [√(S) × √(S)] |
5 | Following stage 2 as discussed, data vector spatially augmented to the size: [(3 × √(S)) × (3 × √(S))] |
6 | ELSE |
7 | Find the number of required virtual sensors to make the total number of sensor elements equal to the nearest perfect square number, say α |
8 | Following stage 1 virtual sensors were included to make the total number of sensor elements in the sensing unit, [(S + α)] |
9 | Represent the obtained data vectors in the squared array of size: [√(S + α) × √(S + α)] |
10 | Following stage 2 as discussed, data vector spatially augmented to the size [(3×√(S + α)) × (3×√(S + α))] |
11 | STOP |
3. Results
3.1. Experiment 1: Baseline (Four Physical Sensor Elements)
3.2. Experiment 2: Scenario 1 (Three Physical and One Zero-Padded Sensor Element)
3.3. Experiment 3: Scenario 2 (Two Physical and Two Zero-Padded Sensor Elements)
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
6G | Sixth Generation (of wireless communication) |
IoT | Internet of Things |
CNN | Convolutional Neural Network |
MSE | Mean Squared Error |
CO | Carbon Monoxide |
QoS | Quality of Service |
MOX | Metal Oxide |
AI | Artificial Intelligence |
DCNN | Deep Convolutional Neural Network |
2D | Two Dimensional |
ace | Acetone |
car | Carbon Tetrachloride |
emk | Ethyl Methyl Ketone |
xyl | Xylene |
CdS | Cadmium Sulfide |
MoO | Molybdenum Oxide |
SnO2 | Tin Oxide |
ZnO | Zinc Oxide |
ANN | Artificial Neural Network |
SGD | Stochastic Gradient Descent |
VGS | Virtual Gas Sensor |
QNT_MSE | Quantification-Mean Squared Error |
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Experiments | Physical Sensors | Zero-Padded Virtual Sensors | Classification Accuracy (%) | Quantification MSE | Effective Power (Consumed) |
---|---|---|---|---|---|
Baseline | CdS, MoO, SnO2, ZnO | 0 | 100 | (7.61 ± 1.66) × 10−3 | 10 W |
Scenario 1: Case 1 | CdS, MoO, SnO2 | 1 | 100 | (1.15 ± 0.35) × 10−2 | 7.5 W |
Scenario 1: Case 2 | CdS, MoO, ZnO | 1 | 100 | (4.01 ± 0.53) × 10−2 | 7.5 W |
Scenario 1: Case 3 | CdS, SnO2, ZnO | 1 | 100 | (7.02 ± 0.86) × 10−3 | 7.5 W |
Scenario 1: Case 4 | MoO, SnO2, ZnO | 1 | 100 | (1.43 ± 0.21) × 10−2 | 7.5 W |
Scenario 2: Case 1 | CdS, MoO | 2 | 100 | (3.01 ± 0.16) × 10−0 | 5 W |
Scenario 2: Case 2 | CdS, SnO2 | 2 | 100 | (1.42 ± 0.33) × 10−1 | 5 W |
Scenario 2: Case 3 | CdS, ZnO | 2 | 100 | (2.43 ± 0.40) × 10−2 | 5 W |
Scenario 2: Case 4 | MoO, SnO2 | 2 | 100 | (1.68 ± 0.19) × 10−1 | 5 W |
Scenario 2: Case 5 | MoO, ZnO | 2 | 100 | (3.17 ± 0.91) × 10−1 | 5 W |
Scenario 2: Case 6 | SnO2, ZnO | 2 | 100 | (1.64 ± 0.38) × 10−2 | 5 W |
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Chaudhri, S.N.; Rajput, N.S.; Alsamhi, S.H.; Shvetsov, A.V.; Almalki, F.A. Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm. Sensors 2022, 22, 3039. https://doi.org/10.3390/s22083039
Chaudhri SN, Rajput NS, Alsamhi SH, Shvetsov AV, Almalki FA. Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm. Sensors. 2022; 22(8):3039. https://doi.org/10.3390/s22083039
Chicago/Turabian StyleChaudhri, Shiv Nath, Navin Singh Rajput, Saeed Hamood Alsamhi, Alexey V. Shvetsov, and Faris A. Almalki. 2022. "Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm" Sensors 22, no. 8: 3039. https://doi.org/10.3390/s22083039
APA StyleChaudhri, S. N., Rajput, N. S., Alsamhi, S. H., Shvetsov, A. V., & Almalki, F. A. (2022). Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm. Sensors, 22(8), 3039. https://doi.org/10.3390/s22083039