Distributed context acquisition and reasoning in the Internet of Things for indoor air quality monitoring

Abstract

The rapidly emerging Internet of Things supports many diverse applications including environmental monitoring. Air quality, both indoors and outdoors, proved to be a significant comfort and health factor for people. This thesis proposes a smart context-aware system for indoor air quality monitoring and prediction called DisCPAQ. The system uses data streams from air quality measurement sensors to provide real-time personalized air quality service to users through a mobile application. The proposed system is agnostic to sensor infrastructure. The thesis proposes a context model based on Context Spaces Theory, presents the architecture of the system and identifies challenges in developing large scale IoT applications. DisCPAQ implementation, evaluation and lessons learned are all discussed in the thesis.