Lifetime maximization for pipeline monitoring based on data aggregation and bio-inspired clustering algorithm
dc.contributor.author | Abdelhafidh, Maroua | |
dc.contributor.author | Fourati, Mohamed | |
dc.contributor.author | Fourati, Lamia Chaari | |
dc.contributor.author | Mnaouer, Adel Ben | |
dc.contributor.author | Zid, Mokhtar | |
dc.date.accessioned | 2020-02-09T11:04:23Z | |
dc.date.available | 2020-02-09T11:04:23Z | |
dc.date.copyright | 2018 | en_US |
dc.date.issued | 2018 | |
dc.description | This 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.abstract | 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. | en_US |
dc.identifier.citation | Abdelhafidh, 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.2018.8450302 | en_US |
dc.identifier.isbn | 9781538620700 | |
dc.identifier.uri | http://dx.doi.org/10.1109/IWCMC.2018.8450302 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12519/119 | |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation | Authors 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.ispartofseries | 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018; | |
dc.rights | Permission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc. | |
dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | |
dc.subject | Batteries | en_US |
dc.subject | Clustering algorithms | en_US |
dc.subject | Data aggregation | en_US |
dc.subject | Energy consumption | en_US |
dc.subject | Monitoring | en_US |
dc.subject | Pipelines | en_US |
dc.subject | Wireless sensor networks (WSN) | en_US |
dc.subject | Energy utilization | en_US |
dc.subject | Mobile computing | en_US |
dc.subject | Sensor nodes | en_US |
dc.subject | Solar cells | en_US |
dc.subject | Structural health monitoring (SHM) | en_US |
dc.subject | Water pipelines | en_US |
dc.subject | Wireless telecommunication systems | en_US |
dc.subject | Critical challenges | en_US |
dc.subject | Data aggregation | en_US |
dc.subject | Environmental hazards | en_US |
dc.subject | Hydraulic failure | en_US |
dc.subject | Lifetime maximization | en_US |
dc.subject | Pipe-line systems | en_US |
dc.subject | Pipeline monitoring | en_US |
dc.subject | Smart algorithms | en_US |
dc.subject | Clustering algorithms | en_US |
dc.title | Lifetime maximization for pipeline monitoring based on data aggregation and bio-inspired clustering algorithm | en_US |
dc.type | Conference Paper | en_US |