An Interoperable IoT Service Platform for Supporting the Development of LLM Agents

Recent advances in Large Language Models (LLMs) have expanded their applicability beyond traditional language processing, creating growing demand for developing LLM-based agents capable of interacting with physical devices in IoT environments. These agents aim to deliver intelligent, context-aware services by leveraging LLMs' reasoning capabilities and real-world data from diverse IoT sensors and actuators. However, building such agents in practice faces two major challenges. First, the inherent heterogeneity of IoT systems̶including differences in device interfaces, data formats, and protocols̶makes it difficult for LLM agents to produce accurate and contextually relevant responses. Second, developers are burdened with implementing low-level device logic and integration for each individual service, which hinders rapid development and reuse. To address these issues, we propose an interoperable IoT service platform that facilitates the development of LLM agents. Rather than focusing on agent construction itself, our approach provides a foundational infrastructure that enables seamless integration of LLMs with heterogeneous IoT environments. The platform standardizes access to sensors and actuators through unified interfaces, abstracts device-level differences, and provides a mechanism to easily convert structured sensor and actuator data into natural language representations interpretable by LLMs. Both atomic and composite services can be registered as callable tools, allowing agents to retrieve contextual information and issue control commands via a consistent interface. We implemented the platform in a smart home setting and conducted three evaluations: (1) a scenario-based demonstration of agent behavior, (2) a quantitative analysis of service selection accuracy across multiple LLMs, and (3) a simulation of development effort based on code size. Results show that the platform improves response accuracy, reduces service selection errors̶especially for ambiguous requests̶and significantly lowers development cost through component reuse. These findings confirm the platform's effectiveness in bridging language-based reasoning with real-world IoT interaction.