  The combination of the Internet of Things and the Edge Computing gives many
opportunities to support innovative applications close to end users. Numerous
devices present in both infrastructures can collect data upon which various
processing activities can be performed. However, the quality of the outcomes
may be jeopardized by the presence of outliers. In this paper, we argue on a
novel model for outliers detection by elaborating on a `soft' approach. Our
mechanism is built upon the concepts of candidate and confirmed outliers. Any
data object that deviates from the population is confirmed as an outlier only
after the study of its sequence of magnitude values as new data are
incorporated into our decision making model. We adopt the combination of a
sliding with a landmark window model when a candidate outlier is detected to
expand the sequence of data objects taken into consideration. The proposed
model is fast and efficient as exposed by our experimental evaluation while a
comparative assessment reveals its pros and cons.
