Abdelhafidh, MarouaFourati, MohamedFourati, Lamia ChaariMnaouer, Adel BenZid, Mokhtar2020-02-092020-02-0920182018Abdelhafidh, 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.2018.84503029781538620700http://dx.doi.org/10.1109/IWCMC.2018.8450302http://hdl.handle.net/20.500.12519/119This 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.Hydraulic 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.enPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.BatteriesClustering algorithmsData aggregationEnergy consumptionMonitoringPipelinesWireless sensor networks (WSN)Energy utilizationMobile computingSensor nodesSolar cellsStructural health monitoring (SHM)Water pipelinesWireless telecommunication systemsCritical challengesData aggregationEnvironmental hazardsHydraulic failureLifetime maximizationPipe-line systemsPipeline monitoringSmart algorithmsClustering algorithmsConference Paper