k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report

This short paper explores the usability of Long Range Wide Area Network (LoRaWAN) technology for localization within the context of modern Industry 5.0 wireless networks. Traditional localization methods have often fallen short in providing meaningful accuracy in this domain. Our research addresses this gap by investigating the potential of LoRaWAN for localization, synthesizing key findings and advancements. Two primary contributions are presented: the analysis of two underground LoRaWAN datasets, valuable resources for researchers and practitioners, and the proposal of two innovative 𝑘-nearest neighbors (𝑘-NN) algorithms designed to enhance position estimation accuracy through optimized nearest neighbor selection. By integrating preprocessing strategies with these algorithms, an improvement in accuracy of up to 17% is achieved.