Foundations of Data Quality Assurance for IoT-based Smart Applications

Most current scientific and industrial efforts in IoT are geared towards building integrated platforms to finally realize its potential in commercial scale applications. The IoT and Big Data contemporary context brings a number of challenges, such as providing quality assurance (defined by availability and veracity) for sensor data. Traditional signal processing approaches are no longer sufficient, requiring combined approaches in both architectural and analytical layers. This paper proposes a discussion on the adequate foundations of a new general approach aimed at increasing robustness and antifragility of IoT-based smart applications. In addition, it shows results of preliminary experiments with real data in the context of precision irrigation using multivariate methods to identify relevant situations, such as sensor failures and the mismatch of contextual sensor information due to different spatial granularities capture. Our results provide initial indications of the adequacy of the proposed framework.