A clustering algorithm for detecting differential deviations in the multivariate time-series IoT data based on sensor relationship

Internet-of-things (IoT) applications involve a large number of sensors reporting data as a set of time series. Often these data are related to each other based on the relationship of the sensors in the actual application. Any small deviations could indicate a change in the operation of the IoT syst...

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Veröffentlicht in:	Knowledge and information systems Jg. 67; H. 3; S. 2641 - 2690
Hauptverfasser:	Idrees, Rabbia, Maiti, Ananda, Garg, Saurabh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.03.2025 Springer Nature B.V
Schlagworte:	Algorithms Anomalies Clustering Complexity Computer Science Data analysis Data Mining and Knowledge Discovery Data transmission Database Management Datasets Deviation Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) Internet of Things IT in Business Multivariate analysis Outliers (statistics) Regular Paper Sensors Synthetic data Time series Outlier detection Multivariate time series Sensor relationship Clustering Deviations Anomaly detection
ISSN:	0219-1377, 0219-3116
Online-Zugang:	Volltext
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