# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 1 Introduction  
The expansion of the Internet of Things (IoT) has accelerated the widespread adoption of smart home devices, profoundly transforming automation and digital interaction in residential, commercial, and industrial settings. As interconnected devices become increasingly prevalent, effectively managing and orchestrating them has become essential for optimising performance, reducing user intervention, and ensuring seamless interoperability. Nonetheless, traditional approaches to smart home automation--often based on predefined rules or limited device integration--have limitations in adaptability and scalability, restricting their capability to predict or respond to dynamic conditions [1]. These limitations have spurredresearch into AI-driven orchestration mechanisms that enhance automation flexibility while integrating a wide range of smart devices.  
One emerging approach to address these challenges is generative AI, particularly through large language models (LLMs). Owing to their ability to understand and generate text in a human-like manner, LLMs enable more natural interactions, real-time adaptations, and context-rich decision-making. Crucially, their capability to generate code on the fly opens new possibilities for dynamic orchestration: rather than relying on static, predefined workflows, the system can automatically produce or modify service logic in response to user requests and changing conditions. In the context of intelligent environments, this capacity endows systems with the means to interpret user demands, anticipate future needs, and continuously adapt to evolving preferences. Recently, Retrieval-Augmented Generation (RAG) has emerged as a promising technique that links large language models with specialised knowledge repositories, thereby enriching contextual reasoning in automation systems. By combining information retrieval from vector databases or specialised repositories with text generation, RAG allows the system to incorporate relevant, up-to-date data into its decision-making process, delivering more precise and context-aware responses in dynamic IoT settings.  
Alongside these AI-driven advancements, the OSGi framework is a robust and widely accepted technology for ensuring modularity and dynamic service management. Thanks to its ability to deploy, update, and manage system components in real time, OSGi provides an ideal architecture for scalable smart home orchestration [2]. Despite the strengths of modern AI techniques and proven frameworks like OSGi, challenges such as heterogeneous device integration, dynamic service updates, real-time adaptation, and cross-device communication persist.  
In response to these challenges, this research introduces a novel smart home orchestrator that harnesses the potential of generative AI, specifically LLMs, to dynamically generate service bundles based on user requirements, thereby enhancing both personalisation and efficiency in connected home environments. The proposed system allows users to articulate their needs in natural language, which is then converted into executable orchestration logic. By integrating AI-based retrieval and dynamic service management, the orchestrator increases flexibility and intelligence in smart home environments. The system's performance was validated through multiple use-case scenarios, demonstrating its ability to adapt to changing user needs, optimise device functionalities, and improve overall automation efficiency. As such, this work contributes to the advancement of AI-driven smart home orchestration, laying the groundwork for the emergence of more adaptive and intelligent automation frameworks.