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Erschienen in: Soft Computing 24/2020

22.05.2020 | Methodologies and Application

APSO-MVS: an adaptive particle swarm optimization incorporating multiple velocity strategies for optimal leader selection in hybrid MANETs

verfasst von: J. Sathya Priya, M. A. Femina, R. A. Samuel

Erschienen in: Soft Computing | Ausgabe 24/2020

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Abstract

In this paper, we propose a hierarchical topological-based auto-configuration scheme for MANETs providing global internet connectivity among leader and member nodes to reduce the control overhead. The proposed scheme has performed the duplication address detection (DAD) operation through selecting a pre-configured node called coordinator node by a new joining cluster node. Hence, the overhead is reduced by the elimination of DAD messages broadcasting in the whole network. Also, the clustering problem in MANETs is solved by introducing a new adaptive particle swarm optimization with multiple velocity strategy (APSO-MVS) algorithm for a new leader selection with the frequent departure and failure of a leader node. However, to enhance the robustness and global searching ability of classical PSO, the three new velocity updating strategies are used in a newly developed APSO-MVS algorithm. This proposed APSO-MVS algorithm has considered multiple node metrics (node distance from the cluster group centre, node speed and node density) for the selection of an optimal leader node. Simulation results have proved the efficacy of proposed protocol in overhead reduction compared to other existing auto-configuration protocols and in terms of 15 benchmark test functions.

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Metadaten
Titel
APSO-MVS: an adaptive particle swarm optimization incorporating multiple velocity strategies for optimal leader selection in hybrid MANETs
verfasst von
J. Sathya Priya
M. A. Femina
R. A. Samuel
Publikationsdatum
22.05.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 24/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-05034-z

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