Publication:
IoT for water management: towards Intelligent anomaly detection

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3.IoT_for_Water_Management_Towards_Intelligent_Anomaly_Detection.pdf(563.91 KB)
Date
2019-07-22
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Authors
Cuenca-Jara, Jesús ; Antonio F. Skarmeta ; González Vidal, Aurora ; Skarmeta Gómez, Antonio
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Facultad de Informática
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Publisher
IEEE
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Ingeniería de la Información y las Comunicaciones
DOI
https://doi.org/10.1109/WF-IoT.2019.8767190
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info:eu-repo/semantics/lecture
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Abstract
Given that the global water system is deteriorating and the supply and demand are very dynamic, smart ways to improve the water management system are needed so that it becomes more efficient and to extend the services provided to the citizens leading to smart cities. One of many water related problems that can be addressed by the Internet of Things is anomaly detection in water consumption. The analysis of data collected by smart meters will help to personalize the feedback to customers, prevent water waste and detect alarming situations. Water consumption data can be considered as a time series. Time series anomaly detection is an old topic but in this work we attempt to examine which techniques suits better for water consumption. We examine two very well-known methods for time series anomaly detection: an ARIMA-based framework anomaly detection technique which selects as outliers those points no fitting an ARIMA process and also a technique named HOTSAX which represents windows of data in a discrete way and then discriminates them using a heuristic. They are both very different in nature but the true positive analysis is excellent. The challenge remains in removing the false positive from the picture.
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Smart cities , Anomaly detection , Water management , Intelligent data analysis techniques
Citation
González-Vidal, A., Cuenca-Jara, J., & Skarmeta, A. F. (2019, April). IoT for water management: Towards intelligent anomaly detection. In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) (pp. 858-863). IEEE.
URI
http://hdl.handle.net/10201/209261
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