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Review
. 2019 Aug 30;19(17):3760.
doi: 10.3390/s19173760.

Review on Smart Gas Sensing Technology

Shaobin Feng  1 , Fadi Farha  1 , Qingjuan Li  1 , Yueliang Wan  2   3 , Yang Xu  1 , Tao Zhang  4 , Huansheng Ning  5   6
Affiliations
Review

Review on Smart Gas Sensing Technology

Shaobin Feng et al. Sensors (Basel). .

Abstract

With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

Keywords: gas sensor; machine learning; selectivity; sensitive; sensor arrays; smart gas sensing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
2018-2023 Gas Sensor Market in Value($B) [12].
Figure 2
Figure 2
The step of Smart Gas Sensing.
Figure 3
Figure 3
Classification of Gas Sensitive Materials [18,19,20,21,22].
Figure 4
Figure 4
The Performance of Zn-OPV for Detecting NH3 at Room Temperature [33].
Figure 5
Figure 5
Typical Response of a Chemical Gas Sensor [89].
Figure 6
Figure 6
PCA Result of Each Sensor of the Array [95].
Figure 7
Figure 7
Unsynchronized Response and Recovery Curves [129].
Figure 8
Figure 8
Structure of Gas Detection Systems [140].
Figure 9
Figure 9
Smart Gas Sensing SOC [141].
Figure 10
Figure 10
A Structure of Centralized WSN [9].
Figure 11
Figure 11
A Distributed WSN Based on Fog Calculation [149].
See this image and copyright information in PMC

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