Linear WSN lifetime maximization for pipeline monitoring using hybrid K-means ACO 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.accessioned2020-01-29T09:13:56Z
dc.date.available2020-01-29T09:13:56Z
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 Wireless Days (WD) (2018), available online at: https://doi.org/10.1109/WD.2018.8361715.en_US
dc.description.abstractWater Pipeline Monitoring Systems have emerged as a reliable solution to maintain the integrity of the water distribution infrastructure. Various emerging technologies such as the Internet of Things, Physical Cyber Systems, and machine-to-machine networks are efficiently deployed to build a Structural Health Monitoring of pipeline and invoke the deployment of the Industrial Wireless Sensor Networks (IWSN) technology. Efficient energy consumption is imperatively required to maintain the continuity of the network and to allow an adequate interconnection between sensor nodes deployed in the harsh environment. In this context, to maximize the Lifetime of the WSN under Water Distribution system domain is a primordial objective to ensure its permanently working and to enable a promising solution for hydraulic damage detection according to diverse performance metrics. In this paper, we propose an hybrid clustering algorithm based on K-means and Ant Colony Optimization (ACO); called K-ACO to improve the WSN Lifetime. © 2018 IEEE.en_US
dc.identifier.citationAbdelhafidh, M., Fourati, M., Fourati, L. C., Mnaouer, A. B., & Zid, M. (2018). Linear WSN lifetime maximization for pipeline monitoring using hybrid K-means ACO clustering algorithm. In IFIP Wireless Days (Vol. 2018–April, pp. 178–180). https://doi.org/10.1109/WD.2018.8361715en_US
dc.identifier.isbn9781538656327
dc.identifier.issn21569711
dc.identifier.urihttp://dx.doi.org/10.1109/WD.2018.8361715
dc.identifier.urihttps://hdl.handle.net/20.500.12519/61
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.relationAuthors Affiliations: Abdelhafidh, M., Digital Research Center of Sfax (CRNS), Tunisia, National Engineering School of Sfax, University of Sfax, Tunisia; Fourati, M., Digital Research Center of Sfax (CRNS), Tunisia; Fourati, L.C., Digital Research Center of Sfax (CRNS), Tunisia; Mnaouer, A.B., Canadian University of Dubai (CUD), United Arab Emirates; Zid, M., Research Direction of Tunisian Chemical Group, Gafsa, Tunisia
dc.relation.ispartofseriesIFIP Wireless Days;2018-April
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.subjectAnt colony optimizationen_US
dc.subjectIndustrial wireless sensor networken_US
dc.subjectK-meansen_US
dc.subjectNetwork Lifetime (NL)en_US
dc.subjectWater Pipeline monitoringen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDamage detectionen_US
dc.subjectEnergy utilizationen_US
dc.subjectHydraulic machineryen_US
dc.subjectMachine-to-machine communicationen_US
dc.subjectPipelinesen_US
dc.subjectSensor nodesen_US
dc.subjectStructural health monitoring (SHM)en_US
dc.subjectWater distribution systemsen_US
dc.titleLinear WSN lifetime maximization for pipeline monitoring using hybrid K-means ACO clustering algorithmen_US
dc.typeConference Paperen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction 61.pdf
Size:
103.81 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Department of Computer Engineering and Computational Sciences