Using Trust as a Measure to Derive Data Quality in Data Shared IoT Deployments

Abstract

Recent developments in Internet of Things have heightened the need for data sharing across application domains to foster innovation. As most of these IoT deployments are based on heterogeneous sensor types, there is increased scope for sharing erroneous, inaccurate or inconsistent data. This in turn may lead to inaccurate models built from this data. It is important to evaluate this data as it is collected to establish its quality. This paper presents an analysis of data quality as it is represented in Internet of Things (IoT) systems and some of the limitations of this representation. The paper then introduces the use of trust as a heuristic to drive data quality measurements. Trust is a well-established metric that has been used to determine the validity of a piece or source of data in crowd sourced or other unreliable data collection techniques. The analysis extends to detail an appropriate framework for representing data quality within the big data model. To demonstrate the application of a trust backed framework, we used data collected from a IoT deployment of sensors to measure air quality in which a low cost sensor was co-located with a gold reference sensor. Using data streams modeled based on a dataset from an IoT deployment, our initial results show that the framework's trust score are consistent with the accuracy measure of the machine learning models.