Landmark based Outliers Detection in Pervasive Applications

Kolomvatsos, K. and Anagnostopoulos, C. (2021) Landmark based Outliers Detection in Pervasive Applications. In: 12th International Conference on Information and Communication Systems (ICICS 2021), Valencia, Spain (Virtual), 24-26 May 2021, pp. 201-206. ISBN 9781665433518 (doi: 10.1109/ICICS52457.2021.9464571)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Anagnostopoulos, Dr Christos and Kolomvatsos, Dr Kostas
• Authors: Kolomvatsos, K., and Anagnostopoulos, C.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2573-3346
• ISBN: 9781665433518
• Copyright Holders: Copyright © 2021 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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