Data Quality and Trust : A Perception from Shared Data in IoT

Internet of Things devices and data sources are seeing increased use in various application areas. The proliferation of cheaper sensor hardware has allowed for wider scale data collection deployments. With increased numbers of deployed sensors and the use of heterogeneous sensor types there is increased scope for collecting 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 determine its validity. 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 discusses 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 effectively within the big data model and why a trust backed framework is important especially in heterogeneously sourced IoT data streams.