TAF: A trust assessment framework for inferencing with uncertain streaming information

Pervasive information consumers in open, loosely-coupled systems, such as in Internet of Things and crowd-sensing environment, will rely more and more often on streaming information from sensory sources with whom they have only ephemeral, transient relationships. In such settings, information uncertainties arise as the trustworthiness of the sources and their information become questionable. It is thus necessary to quantify the quality of inferences made with such information to aid more informed and effective decision making and action taking. One of the aspects of trust assessment systems is to provide for such quality metrics, however, these systems have been traditionally applied in static situations. In this paper, we introduce TAF, a trust assessment framework for streaming information that leverages the rich toolkit of subjective logic operators to estimate the quality of said inferences under information uncertainty. We present the system architecture, describe its components and provide some preliminary quality results for the framework.