Enhancing IoT Interactions with Large Language Models: A Progressive Approach

Daniela Timisica, Daniela Timisica, Radu Boncea, Mariana Mocanu, Bogdan Dura, Sebastian Balmus

2025

Abstract

This paper explores the development and implementation of an Intelligent Virtual Assistant (IVA) leveraging Large Language Models (LLMs) to enhance interactions with Internet of Things (IoT) systems. Our work demonstrates the initial success in enabling the IVA to perform telemetry readings and basic interpretations, showcasing the potential of LLMs in transforming Natural Language Processing (NLP) applications within smart environments. We discuss the future enhancements planned for the IVA, including the ability to sequentially call multiple tools, perform readings from various sources, and execute robust data analysis. Specifically, we aim to fine-tune the LLM to translate human intentions into Prometheus queries and integrate additional analytical tools like MindDB to extend the system’s capabilities. These advancements are expected to improve the IVA’s ability to provide comprehensive responses and deeper insights, ultimately contributing to more intelligent and intuitive virtual assistants. Our ongoing research highlights the potential of integrating advanced NLP, IoT, and data analytics technologies, paving the way for significant improvements in smart home and vehicle environments.

Paper Citation

in Harvard Style

Timisica D., Boncea R., Mocanu M., Dura B. and Balmus S. (2025). Enhancing IoT Interactions with Large Language Models: A Progressive Approach. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 1025-1033. DOI: 10.5220/0013346700003929

in Bibtex Style

@conference{iceis25,
author={Daniela Timisica and Radu Boncea and Mariana Mocanu and Bogdan Dura and Sebastian Balmus},
title={Enhancing IoT Interactions with Large Language Models: A Progressive Approach},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={1025-1033},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013346700003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Enhancing IoT Interactions with Large Language Models: A Progressive Approach
SN - 978-989-758-749-8
AU - Timisica D.
AU - Boncea R.
AU - Mocanu M.
AU - Dura B.
AU - Balmus S.
PY - 2025
SP - 1025
EP - 1033
DO - 10.5220/0013346700003929
PB - SciTePress