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The combination of the Internet of Things and the Edge Computing gives manyopportunities to support innovative applications close to end users. Numerousdevices present in both infrastructures can collect data upon which variousprocessing activities can be performed. However, the quality of the outcomesmay be jeopardized by the presence of outliers. In this paper, we argue on anovel model for outliers detection by elaborating on a `soft' approach. Ourmechanism is built upon the concepts of candidate and confirmed outliers. Anydata object that deviates from the population is confirmed as an outlier onlyafter the study of its sequence of magnitude values as new data areincorporated into our decision making model. We adopt the combination of asliding with a landmark window model when a candidate outlier is detected toexpand the sequence of data objects taken into consideration. The proposedmodel is fast and efficient as exposed by our experimental evaluation while acomparative assessment reveals its pros and cons.

A Soft Method for Outliers Detection at the Edge of the Network