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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Published in:	Knowledge and information systems Vol. 67; no. 3; pp. 2641 - 2690
Main Authors:	Idrees, Rabbia, Maiti, Ananda, Garg, Saurabh
Format:	Journal Article
Language:	English
Published:	London Springer London 01.03.2025 Springer Nature B.V
Subjects:	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
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Abstract	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 system and potential problems with the IoT application’s goals. It is often difficult to detect such deviations with respect to the relationship between the sensors. This paper presents the clustering algorithm that can efficiently detect all the deviations small or large in the complex and evolving IoT data streams with the help of sensor relationships. We have demonstrated with the help of experiments that our algorithm can efficiently handle high-dimensional data and accurately detect all the deviations. In this paper, we have presented two more algorithms, anomaly detection and outlier detection, that can efficiently categorize the deviations detected by our proposed clustering algorithm into anomalous or normal deviations. We have evaluated the performance and accuracy of our proposed algorithms on synthetic and real-world datasets. Furthermore, to check the effectiveness of our algorithms in terms of efficiency, we have prepared synthetic datasets in which we have increased the complexity of the deviations to show that our algorithm can handle complex IoT data streams efficiently.
AbstractList	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 system and potential problems with the IoT application’s goals. It is often difficult to detect such deviations with respect to the relationship between the sensors. This paper presents the clustering algorithm that can efficiently detect all the deviations small or large in the complex and evolving IoT data streams with the help of sensor relationships. We have demonstrated with the help of experiments that our algorithm can efficiently handle high-dimensional data and accurately detect all the deviations. In this paper, we have presented two more algorithms, anomaly detection and outlier detection, that can efficiently categorize the deviations detected by our proposed clustering algorithm into anomalous or normal deviations. We have evaluated the performance and accuracy of our proposed algorithms on synthetic and real-world datasets. Furthermore, to check the effectiveness of our algorithms in terms of efficiency, we have prepared synthetic datasets in which we have increased the complexity of the deviations to show that our algorithm can handle complex IoT data streams efficiently.
Author	Maiti, Ananda Garg, Saurabh Idrees, Rabbia
Author_xml	– sequence: 1 givenname: Rabbia surname: Idrees fullname: Idrees, Rabbia email: rabbia.idrees@utas.edu.au organization: School of Information and Communication Technology, University of Tasmania – sequence: 2 givenname: Ananda surname: Maiti fullname: Maiti, Ananda organization: School of Information Technology, Deakin University – sequence: 3 givenname: Saurabh surname: Garg fullname: Garg, Saurabh organization: School of Information and Communication Technology, University of Tasmania
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SubjectTerms	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
Title	A clustering algorithm for detecting differential deviations in the multivariate time-series IoT data based on sensor relationship
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