Review on Smart Gas Sensing Technology

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...

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Main Authors: Shaobin Feng, Fadi Farha, Qingjuan Li, Yueliang Wan, Yang Xu, Tao Zhang, Huansheng Ning
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/19/17/3760
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author Shaobin Feng
Fadi Farha
Qingjuan Li
Yueliang Wan
Yang Xu
Tao Zhang
Huansheng Ning
author_facet Shaobin Feng
Fadi Farha
Qingjuan Li
Yueliang Wan
Yang Xu
Tao Zhang
Huansheng Ning
author_sort Shaobin Feng
collection DOAJ
description 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.
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spelling doaj.art-be3e4c72d6d34155adcc657a15f43faa2022-12-22T01:58:37ZengMDPI AGSensors1424-82202019-08-011917376010.3390/s19173760s19173760Review on Smart Gas Sensing TechnologyShaobin Feng0Fadi Farha1Qingjuan Li2Yueliang Wan3Yang Xu4Tao Zhang5Huansheng Ning6School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center for Cyberspace Data Analysis and Applications, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security), Shanghai 201204, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaWith 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.https://www.mdpi.com/1424-8220/19/17/3760smart gas sensinggas sensorsensor arraysmachine learningsensitiveselectivity
spellingShingle Shaobin Feng
Fadi Farha
Qingjuan Li
Yueliang Wan
Yang Xu
Tao Zhang
Huansheng Ning
Review on Smart Gas Sensing Technology
Sensors
smart gas sensing
gas sensor
sensor arrays
machine learning
sensitive
selectivity
title Review on Smart Gas Sensing Technology
title_full Review on Smart Gas Sensing Technology
title_fullStr Review on Smart Gas Sensing Technology
title_full_unstemmed Review on Smart Gas Sensing Technology
title_short Review on Smart Gas Sensing Technology
title_sort review on smart gas sensing technology
topic smart gas sensing
gas sensor
sensor arrays
machine learning
sensitive
selectivity
url https://www.mdpi.com/1424-8220/19/17/3760
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