Improving indoor WiFi localization by using Machine Learning techniques Articles uri icon

publication date

  • October 2024

start page

  • 1

end page

  • 21

issue

  • 19

volume

  • 24

Electronic International Standard Serial Number (EISSN)

  • 1424-8220

abstract

  • Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.

subjects

  • Telecommunications

keywords

  • wifi positioning; machine learning; random forest; knn; nn; catboost; xgboost; gridsearchcv.