High-accuracy iterative localization algorithm for underground mine WSNs with dynamic path loss optimization and RSSI clustering.

To address the insufficient localization accuracy of wireless sensor networks (WSNs) in complex underground coal mine tunnel environments caused by signal fluctuations and dynamic node movement, this paper proposes an iterative weighted centroid localization algorithm based on Received Signal Strength Indicator (RSSI) clustering. The algorithm optimizes RSSI data using K-means clustering to dynamically acquire path loss parameters and achieves high-precision localization by integrating an improved iterative weighted centroid algorithm. The experimental data show that, compared with several currently high-performance localization algorithms, the algorithm proposed in this paper exhibits certain performance advantages in different scenarios such as adjustment of node communication radius, change of beacon node ratio, and variation of tunnel width, which improves the localization robustness in complex environments.This study provides a theoretical reference for three-dimensional localization in confined, elongated spaces such as underground mine tunnels.