Improved Diverse Gaussian HMM and SVM ML Technique for Sensor Fault Detection and Classification in Air Quality Monitoring System.

Varadharajan, Anandhalekshmi A.; Vaddi, Srinivasa R.; Ramasubramanian, Kanagachidambaresan G.

Internet of Things (IoT) based real-time applications are highly prone to sensor faults because of their deployment in a risky environment. One of the important applications of IoT that is in great demand in this modern era is the air quality monitoring system because of the increase in air pollution over the years around the globe. Hence handling the reliability issue of air quality sensors is of great concern. In this article, a data based diverse fault detection and classification technique is implemented to overcome the sensor fault issue involved in air quality monitoring systems. The proposed work is a two-phase process; first, a Gaussian Hidden Markov Model (GHMM) is used to perform sensor fault detection on real-time air quality sensor data to detect the presence of fault in sensors followed by performing sensor fault classification using a Support Vector Machine (SVM) on the faulty sensor data obtained from fault detection to identify the most difficult to find sensor fault types like 'Out of bounds' and 'Spike fault'. The proposed technique efficiently carries out sensor fault detection and classification with an overall accuracy of 99.48%. Compared to Machine Learning (ML) algorithms like Logistic Regression (LR), Naive Bayes (NB), and Multi-Layer Perceptron (MLP) the diverse proposed technique works well with a precision of 99.50%, recall of 99.08%, and an F1-score of 99.53%.

AIR quality; INTERNET of things; HIDDEN Markov models

Ingénierie des Systèmes d'Information, 2023, Vol 28, Issue 1, p105

1633-1311

Academic Journal

10.18280/isi.280111