Air4People: a Smart Air Quality Monitoring and Context-Aware Notification System (Summary)

  1. Alfonso Garcia De Prado Fontela 1
  2. Guadalupe Ortiz 2
  3. Juan Boubeta-Puig 3
  4. David Corral-Plaza 3
  1. 1 Universidad de Cádiz - Spain
  2. 2 UCASE Software Engineering Group - Spain
  3. 3 University of Cádiz - Spain
Actas:
XV Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2019)

Editorial: SISTEDES

Año de publicación: 2019

Páginas: 1-1

Tipo: Aportación congreso

Resumen

Air quality is one of the key topics in the focus of Internet of Things (IoT) appli-cations and smart cities, since it plays an essential role for citizens nowadays and is currently a worldwide concern. Indeed, air pollution can seriously affect citi-zens’ health; particularly, air pollution may worsen and favour certain illnesses or even cause death to specific risk groups. The fact is that due to this worldwide concern, several IoT systems for air quality monitoring have been created over the last years. Nevertheless, the problem is that monitoring alone is not enough; it is necessary to ensure compliance with the following requirements: (1) air quali-ty information and alerts have to be updated in real time; (2) the information has to be actively provided to citizens in a user-friendly way; (3) the information provided to users, in particular to risks groups, needs to be adapted to their spe-cific features and (4) the system should also take into account the type of activity the user is going to be involved in and adapt notifications accordingly.Currently, most systems providing air quality information lack several of such key characteristics; as a result, information does not reach citizens in a sim-ple way and notifications neither consider citizens’ specific characteristics nor take their physical activity into account. In order to tackle these challenges effec-tively, and to pay special attention to context-awareness issues, we present Air4People: an air quality monitoring and context-aware notification system, which permits obtaining the user’s air quality relevant context, processing both the data coming from IoT air information sources and from the user context, and notifying users in real time when a health risk for their particular context is de-tected.