Repulsion-based grey wolf optimizer with improved exploration and exploitation capabilities to localize sensor nodes in 3D wireless sensor network

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.

Automated summary

This article investigates the localization problem in wireless sensor networks and proposes an improved grey wolf optimization algorithm (R-GWO) for 3D environments. The R-GWO algorithm, which incorporates repulsion mechanism, outperforms the traditional GWO algorithm in terms of exploration and exploitation abilities. Experimental results show that R-GWO achieves the lowest localization error in 3D environments.