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Multi-level clustering protocol for load-balanced and scalable clustering in large-scale wireless sensor networks.
- Source :
-
Journal of Supercomputing . Jul2019, Vol. 75 Issue 7, p3712-3739. 28p. - Publication Year :
- 2019
-
Abstract
- The advent of wireless sensor networks (WSNs) has revolutionized the field of smart applications. In order to improve the performance of WSNs, refinement of clustering and routing protocols can make a vast difference. Existing classical and evolutionary optimization technique-based protocols have high computational complexity since clustering and routing problems are solved separately. Moreover, these protocols suffer from hot-spot problem due to uneven formation of clusters. In this paper, we propose a multi-level clustering protocol (MLCP) for energy-efficient data gathering in large-scale WSNs. Additionally, a hierarchical clustering architecture is designed in MLCP to jointly solve the problems of clustering and routing. Further, for the purpose of cluster head selection, a hybrid dragonfly algorithm-based particle swarm optimization technique is proposed which combines the exploration and exploitation capabilities of dragonfly algorithm and particle swarm optimization, respectively. MLCP considers intra-cluster distance, node degree and inter-cluster distance for the formation of scalable, load-balanced and energy-efficient clusters. To demonstrate the full potential of MLCP, network simulations have been carried out in diverse network conditions. MLCP has shown up to 90% increase in the network lifetime and an improvement of 19.36% in conservation of energy in comparison with the competent protocols. The comparison of obtained results with state-of-the-art clustering protocols clearly establishes the superiority of MLCP in achieving load-balanced, scalable and energy-efficient clustering. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09208542
- Volume :
- 75
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Journal of Supercomputing
- Publication Type :
- Academic Journal
- Accession number :
- 137207780
- Full Text :
- https://doi.org/10.1007/s11227-018-2727-5