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

Abdelhafidh, Maroua
Fourati, Mohamed
Fourati, Lamia Chaari
Mnaouer, Adel Ben
Zid, Mokhtar
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IEEE Computer Society
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.
This 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:
Ant colony optimization, Industrial wireless sensor network, K-means, Network Lifetime (NL), Water Pipeline monitoring, Artificial intelligence, Damage detection, Energy utilization, Hydraulic machinery, Machine-to-machine communication, Pipelines, Sensor nodes, Structural health monitoring (SHM), Water distribution systems
Abdelhafidh, 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).