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Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Ha, Quang and Metia, Santanu and Phung, Manh Duong (2020) Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring. IEEE Sensors Journal . ISSN 1530-437X

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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.

Item Type: Article
Subjects: Electronics and Communications
ISI-indexed journals
Divisions: Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr Manh Duong Phung
Date Deposited: 08 Jan 2020 01:06
Last Modified: 08 Jan 2020 01:07

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