Indoor Air Quality Monitoring Using InfrastructureBased Motion Detectors Poor indoor air quality is a significant burden to society that can cause health issues and decrease productivity. According to research, indoor air quality is intrinsically linked with human activity and mobility. Indeed, mobility is directly linked with transfer of small particles (e.g. PM 2.5 ) and extent of activity affects production of CO 2 . Currently, however, estimation of indoor quality is difficult, requiring deployment of highly specialized sensing devices which need to be carefully placed and maintained. In this paper, we contribute by examining the suitability of infrastructurebased motion detectors for indoor air quality estimation. Such sensors are increasingly being deployed into smart environments, e.g., to control lighting and ventilation for energy management purposes. Being able to take advantage of these sensors would thus provide a costeffective solution for indoor quality monitoring without need for deploying additional sensors. We perform a feasibility study considering measurements collected from a smart office environment having a dense deployment of motion detectors and correlating measurements obtained from motion detectors against air quality values. We consider two main pollutants, PM 2.5  and CO 2 , and demonstrate that there indeed is a connection between extent of movement and PM 2.5  concentration. However, for CO 2 , no relationship can be established, mostly due to difficulties in separating between people passing by and those residing longterm in the environment.

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyberphysical Systems and Mobile Computing Technologies This paper presents a realtime air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and wellbeing. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for costeffective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatialtemporal information. This system incorporates a cyberphysical system for data collection and mobile computing software for data consulting. Moreover, this method provides a costeffective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides realtime identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.