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.
Highlights
In recent years, with the development of IoT technology, gas sensing has been firmly integrated with smart life and smart industry: (1) Food testing; (2) Animal and plant breeding;(3) Air detecting; (4) Disease diagnosing;(5) Industrial field [8] and pipeline leak detection [9]; (6) Hazard monitoring etc
Smart gas sensing technology is a combination of a gas sensor array and pattern recognition method to detect, analyze, and quantify mixed gases, which can achieve high measurement accuracy and get smarter conclusions, such as judging the maturity according to the odor emitted from the fruit
The results show that polyaniline indicates the highest response to ammonia due to the effect of p-phenylene resonance on deprotonation process, and the PCL/carbon black (CB) sensor response is more pronounced compared with ethyl cellulose (EC)/CB as its higher porous structure
Summary
With the development of IoT technology, gas sensing has been firmly integrated with smart life and smart industry: (1) Food testing (e.g., predicting the freshness of pork, beef and mutton [1] and detecting the fresh vegetables freezing time [2]); (2) Animal and plant breeding (e.g., determine the time of cow estrus [3] and determining the time of fruit ripening [4]);. Smart sensing has played a role in life and production, it should be emphasized that there are still many challenges in the development of smart gas sensing corresponding to the various stages of technology mentioned above. Considering the sensing scenarios mentioned above, gas sensing encounters many challenges; (1) Cross sensitivity and weak selectivity: Gas sensors always detect gases depending on their chemical properties, e.g., redox properties. Smart gas sensing technology is a combination of a gas sensor array and pattern recognition method to detect, analyze, and quantify mixed gases, which can achieve high measurement accuracy and get smarter conclusions, such as judging the maturity according to the odor emitted from the fruit.
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