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This study introduces a new architecture for accessing Internet of Things (IoT) data in data spaces using natural language. Large Language Models (LLMs) integrated with the Model Context Protocol (MCP) enable easier human interaction with complex IoT data.

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Area of Science:

  • Computer Science
  • Data Engineering
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) generates vast amounts of heterogeneous data, posing challenges in accessibility and interoperability.
  • Data spaces offer governed environments for secure data sharing, often using standards like ETSI NGSI-LD API.
  • Current interfaces are machine-centric, limiting direct human operator access to IoT data.

Purpose of the Study:

  • To propose an architecture enabling natural-language access to IoT data within data spaces.
  • To bridge the gap between complex machine-to-machine interfaces and human operators.
  • To enhance the usability of IoT data stored in data spaces.

Main Methods:

  • Integration of Large Language Models (LLMs) with the Model Context Protocol (MCP).
  • Development of an architecture for natural-language querying of IoT data spaces.
  • Utilizing tools like fastMCP and OpenAI API for experimental validation.

Main Results:

  • Demonstrated accuracy in accessing IoT data via natural language prompts.
  • Successful integration of LLMs with MCP for data space interaction.
  • The proposed architecture handles prompts requiring advanced reasoning.

Conclusions:

  • The developed architecture effectively enables natural-language access to IoT data in data spaces.
  • LLM integration with MCP provides a user-friendly interface for complex IoT data.
  • This approach enhances the exploitation of IoT data for human operators.