eprintid: 3918
rev_number: 8
eprint_status: archive
userid: 285
dir: disk0/00/00/39/18
datestamp: 2020-01-08 01:06:40
lastmod: 2020-01-08 01:07:03
status_changed: 2020-01-08 01:06:40
type: article
metadata_visibility: show
creators_name: Ha, Quang
creators_name: Metia, Santanu
creators_name: Phung, Manh Duong
creators_id: quang.ha@uts.edu.au
creators_id: Santanu.Metia@uts.edu.au
creators_id: duongpm@vnu.edu.vn
title: Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring
ispublished: pub
subjects: ECE
subjects: isi
divisions: fac_fet
abstract: 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.
date: 2020
date_type: published
publisher: IEEE
id_number: 10.1109/JSEN.2020.2964396
full_text_status: public
publication: IEEE Sensors Journal
refereed: TRUE
issn: 1530-437X
citation:   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     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3918/1/IEEE_sensors_R2-QH%20%281%29.pdf