Leveraging Edge Resources for Indoor Localization for Improved Accuracy

This paper presents a novel approach to zone prediction through Wi-Fi fingerprinting combined with machine learning, leveraging data collected by a UAV and a robotic car across distinct zones. Among the evaluated models, the Confidence-Aware Framework (ConFi) outperformed state-of-the-art methods, achieving a test accuracy of 91%, compared to Long Short-Term Memory (LSTM) at 73% and Long Range Wide Area Network (LoRaWAN) at 85%. This superior accuracy underscores the ConFi model’s capability to effectively manage the complexities of real-world environments. By leveraging the ConFi framework, the proposed system enhances precision, scalability, and adaptability over traditional methods. This study represents a significant advancement in indoor localization, offering a deployable and efficient solution for GPS-denied environments with promising applications in healthcare, logistics, and disaster management.