Abstract
In recent years, localization turns out to be a crucial aspect in the realm of wireless sensor networks (WSNs) sparking a lot of research interest among researchers. It is the procedure of discovering the locality of target nodes concerning the installed anchor nodes whose placements are well known as they have a GPS component integrated into them. But as GPS is incompatible with indoor and/or aquatic situations, all sensor nodes are often not set up with it. If all the nodes are fitted with GPS, a network becomes too expensive and uses extra energy, which is a key disadvantage of WSNs. In the literature, various localization strategies have been presented; however, the majority of research ideas focus on 2D applications. In 3D implementations, however, the region under consideration in the sensing environment may be complicated. The determination of node placement in a 3D environment necessitates an optimal algorithm. In this research, we proposed a repulsion-based improved grey wolf optimizer (R-GWO) for the sensor nodes localization that outperforms the traditional GWO in terms of exploration and exploitation abilities. The suggested R-GWO has been evaluated on the WSN Localization problem and has shown to have the lowest localization error when contrasted to the other strategies used in 3D environments.
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Appendix
Appendix
Algorithm 3 Pseudo-code of GWO
Algorithm 4 Pseudo-code of R-GWO
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Abuaddous, H.Y., Kaur, G., Jyoti, K. et al. Repulsion-based grey wolf optimizer with improved exploration and exploitation capabilities to localize sensor nodes in 3D wireless sensor network. Soft Comput 27, 3869–3885 (2023). https://doi.org/10.1007/s00500-022-07590-y
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DOI: https://doi.org/10.1007/s00500-022-07590-y