Exploring the Feasibility of Automated Data Standardization using Large Language Models for Seamless Positioning

TL;DR

This paper investigates the use of large language models to automate real-time data standardization in IoT positioning systems, aiming to improve data compatibility and navigation accuracy.

Contribution

It introduces a novel framework combining LLMs with transformation rule generation for seamless sensor data integration in IoT environments.

Findings

Enhanced positioning accuracy with standardized sensor data

Demonstrated scalability and adaptability in real-time settings

Automated rule generation reduces manual data processing efforts

Abstract

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.