Lifetime maximization for pipeline monitoring based on data aggregation and bio-inspired clustering algorithm

Simple item page
dc.contributor.authorAbdelhafidh, Maroua
dc.contributor.authorFourati, Mohamed
dc.contributor.authorFourati, Lamia Chaari
dc.contributor.authorMnaouer, Adel Ben
dc.contributor.authorZid, Mokhtar
dc.date.accessioned2020-02-09T11:04:23Z
dc.date.available2020-02-09T11:04:23Z
dc.date.copyright2018en_US
dc.date.issued2018
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) (2018), available online at: https://doi.org/10.1109/IWCMC.2018.8450302.en_US
dc.description.abstractHydraulic failures in Water Pipeline System (WPS) can cause catastrophic environmental hazards. Wireless Sensor Networks (WSN) are greatly deployed to maintain a Structural Health Monitoring of pipeline and supervise the WPS. Since, its implementation increases significantly, its energy consumption represents a critical challenge that should be imperatively investigated in order to ensure an efficient and seamless interconnection between sensor nodes. In this context, the data aggregation techniques are well-designed and various smart algorithms are developed to reduce the quantity of transmitted data and to minimize the energy consumption. In this paper, we combine between data aggregation and bio-inspired clustering algorithm in order to improve the WSN Lifetime. © 2018 IEEE.en_US
dc.identifier.citationAbdelhafidh, M., Fourati, M., Fourati, L. C., Ben Mnaouer, A., & Zid, M. (2018). Lifetime maximization for pipeline monitoring based on data aggregation and bio-inspired clustering algorithm. In 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018 (pp. 666–671). https://doi.org/10.1109/IWCMC.2018.8450302en_US
dc.identifier.isbn9781538620700
dc.identifier.urihttp://dx.doi.org/10.1109/IWCMC.2018.8450302
dc.identifier.urihttp://hdl.handle.net/20.500.12519/119
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, 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, B.P. 275, Sakiet Ezzit, Sfax, 3021, Tunisia; Fourati, L.C., Laboratory of Technology of Smart Systems (LT2S), Tunisia, Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax, 3021, Tunisia; Ben Mnaouer, A., Canadian University of Dubai (CUD), United Arab Emirates; Zid, M., Research Direction of Tunisian Chemical Group, Gafsa, Tunisia
dc.relation.ispartofseries2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018;
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectBatteriesen_US
dc.subjectClustering algorithmsen_US
dc.subjectData aggregationen_US
dc.subjectEnergy consumptionen_US
dc.subjectMonitoringen_US
dc.subjectPipelinesen_US
dc.subjectWireless sensor networks (WSN)en_US
dc.subjectEnergy utilizationen_US
dc.subjectMobile computingen_US
dc.subjectSensor nodesen_US
dc.subjectSolar cellsen_US
dc.subjectStructural health monitoring (SHM)en_US
dc.subjectWater pipelinesen_US
dc.subjectWireless telecommunication systemsen_US
dc.subjectCritical challengesen_US
dc.subjectData aggregationen_US
dc.subjectEnvironmental hazardsen_US
dc.subjectHydraulic failureen_US
dc.subjectLifetime maximizationen_US
dc.subjectPipe-line systemsen_US
dc.subjectPipeline monitoringen_US
dc.subjectSmart algorithmsen_US
dc.subjectClustering algorithmsen_US
dc.titleLifetime maximization for pipeline monitoring based on data aggregation and bio-inspired clustering algorithmen_US
dc.typeConference Paperen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction 119.pdf
Size:
104.27 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Department of Computer Engineering and Computational Sciences