Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration

Negin Jahanbakhsh, Mario Vega-Barbas (), Iván Pau, Lucas Elvira-Martín, Hirad Moosavi and Carolina García-Vázquez
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Negin Jahanbakhsh: ETSIS de Telecomunicación, Universidad Politécnica de Madrid, Calle Nikola Tesla S/N, 28038 Madrid, Spain
Mario Vega-Barbas: ETSIS de Telecomunicación, Universidad Politécnica de Madrid, Calle Nikola Tesla S/N, 28038 Madrid, Spain
Iván Pau: ETSIS de Telecomunicación, Universidad Politécnica de Madrid, Calle Nikola Tesla S/N, 28038 Madrid, Spain
Lucas Elvira-Martín: ETSIS de Telecomunicación, Universidad Politécnica de Madrid, Calle Nikola Tesla S/N, 28038 Madrid, Spain
Hirad Moosavi: ETSIS de Telecomunicación, Universidad Politécnica de Madrid, Calle Nikola Tesla S/N, 28038 Madrid, Spain
Carolina García-Vázquez: Facultad de Diseño y Tecnología, University of Design, Innovation and Technology, 28016 Madrid, Spain

Future Internet, 2025, vol. 17, issue 5, 1-30

Abstract: The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments.

Keywords: smart home orchestration; generative AI; large language models (LLMs); retrieval-augmented generation (RAG); AI agent; OSGi framework; dynamic service bundles; vector databases; IoT integration; AI-driven automation; real-time adaptation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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