Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Abstract

With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in gas detection. This study describes the overall types of smart gas sensors used in air quality control, signal pre-processing and extraction features. Also it presents pattern recognition methods used in E-nose applications including linear methods such as Linear Discriminant Analysis LDA, K-Nearest Neighbors KNN and Non-linear such as algorithms Support vector Machine SVM and Artificial Neural Network ANN and their impact on improving accuracy rate for gas detection. Finally, this paper summarizes by providing directions for about how to leverage the benefits of combining these classifiers which is known as Data fusion approach and ensemble classifiers.

Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique

“…However, almost all of the investigated gas sensors still suffer from lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms could be a solution to empower conventional gas sensing technologies and increase accuracy in gas detection [31].…”