Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

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 realtime 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.

Sensor Aided Beamforming in Vehicular Environment

“…The next chart indicates the calculated beamforming angle for the APs and mobile client. When the mobile client travels from point A to point B, the antenna radiating beam of the mobile client and the APs will steer dynamically to each other and the beam angle is calculated using formula (5) and (6), the calculated F m and F AP when the mobile client is moving from point A to point B and roamed through the APs are plotted in Fig. 6.…”

“…For driving assistance or autonomous vehicle [3], the safety of future autonomous vehicles was discussed that includes the problem when the sensors data was shared between the vehicles and [4], the adaptive cruise control (ACC) system by utilizing cloud and sensor fusion by adaptive Kalman filter. Another application, the sensor fusion for indoor air quality monitoring [5] was implemented by using the fractional-order modelling and control (FOMCON) toolbox providing overall air quality alerts in a timely manner for accurate prediction with enhanced performance against measurement noise and non-linearity. Sensor fusion also utilized in the smart city for public space monitoring with IoT sensors [6], a data processing module was developed to capture public space utilization with renewable wireless sensor network (RWSN) platform using pyroelectric infrared (PIR) and analog sound sensor to monitor the public space utilization.…”

Indoor air quality prediction systems for smart environments: A systematic review

“…Two studies [131,136] followed ARIMA and two other studies used the GRU method for IAQ prediction. Besides this, one study each focused on ANFIS [134], Kalman filter [49], GA-based SVM [114], time slicer method [106], Bayesian inference [132] and decision tree regression [119]. However, none of these studies included fuzzy logic, which otherwise offers potentials scope for forecasting problems [56,57].…”

“…These systems can help occupants to take adequate decisions about ventilation and follow preventive measures to avoid serious health complications. By considering the advantage of [2,33,77,119] for prediction hour analysis to alert the occupants, re- [33,49,66,77,113,119,133] searchers also need to make efforts to develop a costeffective, reliable and accurate future alert-based IAQ prediction system.…”

“…RQ6 concerns the opportunities for integrating IAQ prediction systems with smart building systems. In total, seven (33.3%) out of 21 studies [2,24,33,49,106,119,134] were based on smart building solutions where the IAQ prediction system was integrated with other smart solutions in the premises for improved lifestyle and well-being. However, the remaining 14 studies (66.6%) were independent solutions where researchers worked solely on IAQ monitoring and prediction.…”