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Open AccessReview

Review on Smart Gas Sensing Technology

1
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Beijing Engineering Research Center for Cyberspace Data Analysis and Applications, Beijing 100083, China
3
Research Institute, Run Technologies Co., Ltd. Beijing, Beijing 100192, China
4
Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security), Shanghai 201204, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(17), 3760; https://doi.org/10.3390/s19173760
Received: 17 July 2019 / Revised: 24 August 2019 / Accepted: 28 August 2019 / Published: 30 August 2019
(This article belongs to the Special Issue Multisensor Arrays for Environmental Monitoring)
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. View Full-Text
Keywords: smart gas sensing; gas sensor; sensor arrays; machine learning; sensitive; selectivity smart gas sensing; gas sensor; sensor arrays; machine learning; sensitive; selectivity
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MDPI and ACS Style

Feng, S.; Farha, F.; Li, Q.; Wan, Y.; Xu, Y.; Zhang, T.; Ning, H. Review on Smart Gas Sensing Technology. Sensors 2019, 19, 3760.

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