Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

• Published in: IEEE sensors journal Vol. 20; no. 8; pp. 4430 - 4441
• Main Authors: Ha, Quang Phuc, Metia, Santanu, Phung, Manh Duong
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air monitoring; Air pollution; Air quality; Buildings; Case studies; Data integration; Environmental monitoring; extended fractional Kalman filter; Humidity; Indexes; Indoor air pollution; Indoor air quality; Internet of Things; Kalman filters; Monitoring; Multisensor fusion; Noise measurement; Outdoor air quality; Performance enhancement; Pollutants; Pollution monitoring; Quality management; Real time; Sensing fusion; Sensors; Smart buildings; Temperature sensors
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2964396

Multisensor fusion of air pollutant data in smart buildings remains an important input to address the well-being and comfort perceived by their inhabitants. An integrated sensing system is part of a smart building where real-time indoor air quality data are monitored round the clock using sensors and operating in the Internet-of-Things (IoT) environment. In this work, we propose an air quality management system merging indoor air quality index (IAQI) and humidex into an enhanced indoor air quality index (EIAQI) by using sensor data on a real-time basis. Here, indoor air pollutant levels are measured by a network of waspmote sensors while IAQI and humidex data are fused together using an extended fractional-order Kalman filter (EFKF). According to the obtained EIAQI, overall air quality alerts are provided in a timely fashion for accurate prediction with enhanced performance against measurement noise and nonlinearity. The estimation scheme is implemented by using the fractional-order modeling and control (FOMCON) toolbox. A case study is analysed to prove the effectiveness and validity of the proposed approach.