Abstract: The application of Wireless Sensor Networks (WSNs) in environmental monitoring, military surveillance, and industrial automation have led to broad adoption of these networks. For the proper functioning of the network, node localization is critical, which poses a challenge in harsh NLOS and noisy environments. Cooperative localization improves the reliability of position estimation using shared data between nodes due to its reliance on inter-node communication. Incorporating localization uncertainty, probabilistic models, and Gaussian Mixture Models (GMM), offers a powerful solution. This review aims to merge the main contributions in the range-based, range-free, and hybrid localization approaches, mainly focusing on probabilistic models which provide robust precision and scaling efficiency in WSNs.

Keywords: Wireless Sensor Networks (WSNs), Node Localization, Cooperative Localization, Non-Line-of-Sight (NLOS), Probabilistic Models, Gaussian Mixture Models (GMM), Range-Based Localization, Range-Free Localization, Hybrid Localization

| DOI: 10.17148/IARJSET.2025.12609