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Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration

Author

Listed:
  • 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)

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.

Suggested Citation

  • Negin Jahanbakhsh & Mario Vega-Barbas & Iván Pau & Lucas Elvira-Martín & Hirad Moosavi & Carolina García-Vázquez, 2025. "Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration," Future Internet, MDPI, vol. 17(5), pages 1-30, April.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:5:p:198-:d:1646102
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    References listed on IDEAS

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