Cognitive internet of things for smart water pipeline monitoring system

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dc.contributor.advisorPermission to reuse the abstract has been secured from the Publisher.en_US
dc.contributor.authorAbdelhafidh, Maroua
dc.contributor.authorMohamed, Fourati
dc.contributor.authorFourati, Lamia Chaari
dc.contributor.authorMnaouer, Adel Ben
dc.contributor.authorMokhtar, Zid
dc.date.accessioned2020-02-09T09:32:21Z
dc.date.available2020-02-09T09:32:21Z
dc.date.copyright2018
dc.date.issued2018-07-02
dc.description.abstractWater Pipeline Monitoring System (WPMS) is extremely important considering the several pipeline damages and the various hydraulic failures that cause a critical water loss. In this context, Cognitive Water Distribution System integrates Internet of Things (IoT) technology, based on smart sensors, actuators and connected objects, with a reliable Big Data processing for smart and robust Structural Health Monitoring (SHM) of pipelines. In this paper, we propose a cognitive IoT-based architecture where we used Apache Spark framework to maintain a real time processing of the large amount of collected data. This efficient processing of measured data and its correspondent calculated values simplify the transient simulations and leak detection and make it faster and easier. © 2018 IEEE.en_US
dc.identifier.citationAbdelhafidh, M., Fourati, M., Fourati, L. C., Ben Mnaouer, A., & Mokhtar, Z. (2018). Cognitive Internet of Things for Smart Water Pipeline Monitoring System. In Proceedings of the 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2018 (pp. 212–219). https://doi.org/10.1109/DISTRA.2018.8600999en_US
dc.identifier.isbn9781538650486
dc.identifier.urihttp://dx.doi.org/10.1109/DISTRA.2018.8600999
dc.identifier.urihttps://hdl.handle.net/20.500.12519/113
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors Affiliations: Abdelhafidh, M., Laboratory of Technology of Smart Systems (LT2S), Tunisia, Digital Research Center of Sfax, University of Sfax, B.P. 275, Sakiet Ezzit, Sfax, 3021, Tunisia, National Engineering School of Sfax, University of Sfax, Tunisia; Fourati, M., Laboratory of Technology of Smart Systems (LT2S), Tunisia, Digital Research Center of Sfax, University of Sfax, B.P. 275, Sakiet Ezzit, Sfax, 3021, Tunisia; Fourati, L.C., Laboratory of Technology of Smart Systems (LT2S), Tunisia, Digital Research Center of Sfax, University of Sfax, B.P. 275, Sakiet Ezzit, Sfax, 3021, Tunisia; Ben Mnaouer, A., Canadian University of Dubai (CUD), United Arab Emirates; Mokhtar, Z., Research Center Gasfa Chemical Group (SIAPE), Gafsa, Tunisia
dc.relation.ispartofseriesProceedings of the 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2018;
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectBig dataen_US
dc.subjectCognitive IoTen_US
dc.subjectSimulationen_US
dc.subjectWater Monitoring Systemen_US
dc.subjectData handlingen_US
dc.subjectLeak detectionen_US
dc.subjectMonitoringen_US
dc.subjectPipeline processing systemsen_US
dc.subjectPipelinesen_US
dc.subjectStructural health monitoring (SHM)en_US
dc.subjectWater distribution systemsen_US
dc.subjectWater pipelinesen_US
dc.subjectInternet of Things (IoT)en_US
dc.subjectPipeline monitoringen_US
dc.subjectRealtime processingen_US
dc.subjectSimulationen_US
dc.subjectTransient simulationen_US
dc.subjectWater monitoring systemsen_US
dc.titleCognitive internet of things for smart water pipeline monitoring systemen_US
dc.typeConference Paperen_US

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