Smart City Gnosys

Smart city article details

Title	Iot For Water Management: Towards Intelligent Anomaly Detection
ID_Doc	33828
Authors	Gonzalez-Vidal A.; Cuenca-Jara J.; Skarmeta A.F.
Year	2019
Published	IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings
DOI	http://dx.doi.org/10.1109/WF-IoT.2019.8767190
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 HOT-SAX 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. © 2019 IEEE.
Author Keywords	anomaly detection; intelligent data analysis techniques; smart cities; water management

Similar Articles

Id	Similarity	Authors	Title	Published
9623	0.892	Galeb B.; Saad H.; Bashar H.; Al-Majdi K.; Al-Hilali A.	Anomaly Detection In Smart Home Electrical Appliances Using Machine Learning With Statistical Algorithms And Optimized Time Series Algorithms	Journal of Mechanics of Continua and Mathematical Sciences, 19, 5 (2024)
18799	0.88	Li Z.; Liu Y.; Xie D.; Li S.	Design Of An Intelligent Water Quality Warning System	Proceedings - 2022 12th International Conference on Information Technology in Medicine and Education, ITME 2022 (2022)
9645	0.869	Zhou W.; Chen C.; Yan Q.; Li B.; Liu K.; Zheng Y.; Yang H.; Xiao H.; Su S.	Anomaly Usage Behavior Detection Based On Multi-Source Water And Electricity Consumption Information	IEEE Access, 13 (2025)
48413	0.857	Ma A.; Karande A.; Dahlquist N.; Ferrero F.; Nguyen N.R.	Sensor Fusion Enhances Anomaly Detection In A Flood Forecasting System	Journal of Sensor and Actuator Networks, 14, 2 (2025)
33619	0.855	Chatterjee A.; Ahmed B.S.	Iot Anomaly Detection Methods And Applications: A Survey	Internet of Things (Netherlands), 19 (2022)
57417	0.855	Ghosh A.	Time Series Transformer For Long Term Rainfall Forecasting Towards Water Distribution Management In Smart Cities.	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (2023)
3760	0.855	Bourelly C.; Bria A.; Ferrigno L.; Gerevini L.; Marrocco C.; Molinara M.; Cerro G.; Cicalini M.; Ria A.	A Preliminary Solution For Anomaly Detection In Water Quality Monitoring	Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020 (2020)
8528	0.855	Syrmos E.; Sidiropoulos V.; Bechtsis D.; Stergiopoulos F.; Aivazidou E.; Vrakas D.; Vezinias P.; Vlahavas I.	An Intelligent Modular Water Monitoring Iot System For Real-Time Quantitative And Qualitative Measurements	Sustainability (Switzerland), 15, 3 (2023)
35909	0.852	Malki A.; Atlam E.-S.; Gad I.	Machine Learning Approach Of Detecting Anomalies And Forecasting Time-Series Of Iot Devices	Alexandria Engineering Journal, 61, 11 (2022)
36020	0.852	Ebisi F.; Nikolakakos I.P.; Karunamurthi J.V.; Ahmed Binahmed Alnuaimi A.N.; Al Buraimi E.; Alblooshi S.	Machine Learning Schemes For Leak Detection In Iot-Enabled Water Transmission System	2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023 (2023)