With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

Monitoring indoor air quality is becoming crucial as people spend most of their time indoors. In recent years, respiratory problems such as chronic obstructive pulmonary disease and asthma appear as among the common diseases that are the reasons for hospitalization among people. Monitoring the indoor pollutant, using the low-cost sensor-based IoT system, can play an important role in the management of these chronic conditions. In this paper, we set out the design of a trial involving an unobtrusive IoT-based monitoring system to observe indoor environment passively in the house of people suffering from Asthma. The system monitors Particulate Matter (PM2.5 & PM10) and Carbon dioxide (CO2) in the indoor environment together with householder's indoor activities such as type of cooking, smoking, ventilation hour, cleaning, and so on.

In this demonstration, we propose an AQ-SCIENCE (Air Quality - Smart Cities with IoT-ENabled Citizen Engagement) framework to enhance citizen engagement with an IoT-enabled LCS (low-cost sensors) monitoring kit and platform. Our LCS kits and user study platform empower citizens with indoor air quality data with referential values and allow them to learn about ways to improve the air quality in their homes. It also provides scientists to collect qualitative data to link the air quality with indoor daily activities. The kits and platform is utilized in a user study linking Air quality and activities in the Bradford region in the UK as part of two European Union projects.