Indoor Environment Prediction with Multiple Sensors and Generative AI via MCP Integration
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105031077445
Journal Title
Proceedings 9th International Conference on Information Technology Incit 2025
Start Page
471
End Page
478
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings 9th International Conference on Information Technology Incit 2025 (2025) , 471-478
Suggested Citation
Surakupt K., Akamatsu S., Hashimoto H., Fujimoto Y., Kashihara S., Visoottiviseth V. Indoor Environment Prediction with Multiple Sensors and Generative AI via MCP Integration. Proceedings 9th International Conference on Information Technology Incit 2025 (2025) , 471-478. 478. doi:10.1109/InCIT66780.2025.11276064 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115514
Title
Indoor Environment Prediction with Multiple Sensors and Generative AI via MCP Integration
Author's Affiliation
Corresponding Author(s)
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Abstract
This paper demonstrates an integration of Large Language Model (LLM), a powerful text-centric Generative AI (GenAI) with an Internet of Things (IoT) sensor network for advanced environmental monitoring and analysis. This paper presents a novel system architecture that integrates a multi-sensor IoT network with a Generative AI model using the Model Context Protocol (MCP) for real-time indoor environmental prediction. MCP is used to orchestrate data flow from multiple sensors such as temperature, humidity, and carbon dioxide to GenAI for analysis and prediction of indoor air quality. The AI-driven insights are then delivered to users through a web application. The evaluation results confirmed the system's high performance, achieving an 85% average prediction accuracy across all three metrics, calculated based on whether predictions fell within predefined tolerance levels. This work establishes the practical value of MCP in a real-world application and showcases the potential of GenAI to transform multi-point sensor data into predictive insights.