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IoT for water management: towards Intelligent anomaly detection

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dc.contributor.authorCuenca-Jara, Jesús
dc.contributor.authorAntonio F. Skarmeta
dc.contributor.authorGonzález Vidal, Aurora
dc.contributor.authorSkarmeta Gómez, Antonio
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.contributor.otherFacultad de Informática
dc.date.accessioned2026-02-20T11:34:42Z
dc.date.available2026-02-20T11:34:42Z
dc.date.copyright© 2019 IEEE
dc.date.issued2019-07-22
dc.description.abstractGiven 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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dc.format.extent6
dc.identifier.citationGonzález-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.
dc.identifier.doihttps://doi.org/10.1109/WF-IoT.2019.8767190
dc.identifier.eisbn978-1-5386-4980-0
dc.identifier.isbn978-1-5386-4981-7
dc.identifier.urihttp://hdl.handle.net/10201/209261
dc.languagespa
dc.publisherIEEE
dc.relationThis work has been sponsored by MINECO through the PERSEIDES project (ref. TIN2017-86885-R) and grant BES- 2015-071956 and by the European Comission through the H2020-ENTROPY-649849 and the H2020 IoTCrawler (con- tract 779852) EU Projects.
dc.relation.ispartof2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8767190
dc.rightsAttribution-NonCommercial-NoDerivates 4.0 International
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSmart cities
dc.subjectAnomaly detection
dc.subjectWater management
dc.subjectIntelligent data analysis techniques
dc.subject.odsObjetivo 6: Agua y saneamiento
dc.titleIoT for water management: towards Intelligent anomaly detection
dc.typeinfo:eu-repo/semantics/lecture
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublicationcf8009bf-6088-449d-9f79-a516af312945
relation.isAuthorOfPublication7dacd587-416b-43e7-bec1-30dbb093d0a4
relation.isAuthorOfPublication.latestForDiscoverycf8009bf-6088-449d-9f79-a516af312945
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