Composing WoT services with uncertain and correlated data

Abstract

The Web of Things (WoT) aims to connect everyday objects to the Web. With the data provided by these connected objects, we can build several interesting applications by composing WoT services which collect and process WoT data and give orders to objects. Often, WoT data are uncertain and correlated due to various reasons. In this paper, we propose a probabilistic approach based on Bayesian networks to model and evaluate the composition of WoT services with uncertain and correlated data. Our approach is implemented and evaluated and the obtained results are promising.