Linear WSN lifetime maximization for pipeline monitoring using hybrid K-means ACO clustering algorithm

Abdelhafidh, MarouaFourati, MohamedFourati, Lamia ChaariMnaouer, Adel BenZid, Mokhtar2020-01-292020-01-2920182018Abdelhafidh, 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.2018.8361715978153865632721569711http://dx.doi.org/10.1109/WD.2018.8361715https://hdl.handle.net/20.500.12519/61This 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.Water 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.enPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.Ant colony optimizationIndustrial wireless sensor networkK-meansNetwork Lifetime (NL)Water Pipeline monitoringArtificial intelligenceDamage detectionEnergy utilizationHydraulic machineryMachine-to-machine communicationPipelinesSensor nodesStructural health monitoring (SHM)Water distribution systemsLinear WSN lifetime maximization for pipeline monitoring using hybrid K-means ACO clustering algorithmConference PaperCopyright : 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.