Exploring the Feasibility of Automated Data Standardization using Large Language Models for Seamless Positioning
This addresses data integration challenges for IoT navigation systems, but it is incremental as it applies existing methods like EKF and fine-tuned LLMs to a specific domain.
The study explored using Large Language Models (LLMs) to automate data standardization for seamless positioning in IoT environments, demonstrating adaptability and scalability in real-time evaluations to enhance operational efficiency and accuracy.
We propose a feasibility study for real-time automated data standardization leveraging Large Language Models (LLMs) to enhance seamless positioning systems in IoT environments. By integrating and standardizing heterogeneous sensor data from smartphones, IoT devices, and dedicated systems such as Ultra-Wideband (UWB), our study ensures data compatibility and improves positioning accuracy using the Extended Kalman Filter (EKF). The core components include the Intelligent Data Standardization Module (IDSM), which employs a fine-tuned LLM to convert varied sensor data into a standardized format, and the Transformation Rule Generation Module (TRGM), which automates the creation of transformation rules and scripts for ongoing data standardization. Evaluated in real-time environments, our study demonstrates adaptability and scalability, enhancing operational efficiency and accuracy in seamless navigation. This study underscores the potential of advanced LLMs in overcoming sensor data integration complexities, paving the way for more scalable and precise IoT navigation solutions.