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

TL;DR

This paper introduces a novel 'soft' outlier detection method for edge network data, leveraging a sequence-based approach with sliding and landmark windows to improve detection accuracy and efficiency.

Contribution

It proposes a new outlier detection model that confirms outliers based on data sequences, enhancing detection at the network edge.

Findings

The method is fast and efficient in experimental evaluations.

It offers a comparative assessment highlighting its advantages and limitations.

Abstract

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.