Machine Learning Techniques for Outlier Detection in Indoor IOT Localization System

Abstract

Accurate indoor localization is a critical component in the Internet of Things (IoT) ecosystem, enabling applications in areas such as smart buildings, healthcare, and logistics. However, the presence of outliers in localization data can lead to significant errors and reduce system reliability. This paper presents a novel approach to outlier detection in IoT-based indoor localization using machine learning techniques. We develop a robust framework that combines multiple machine learning algorithms to detect and mitigate outliers, enhancing the accuracy of localization data [2]. Our experimental results, conducted in a variety of indoor environments, demonstrate the superiority of our method in improving localization precision and robustness compared to traditional approaches. This research provides a comprehensive solution for addressing the challenges posed by outliers in IoT indoor localization, offering valuable insights for future developments in the field [5].