Real-Time Environment Monitoring and Response Through IoT and Retrieval-Augmented Generation

This paper introduces an architecture that combines IoT-driven data collection with Retrieval-Augmented Generation (RAG) for real-time environmental monitoring, analysis, and prediction. Using edge computing for local processing and centralized LLMs for complex analysis, the system delivers scalable and timely insights into environmental issues like climate change and disaster prevention. Data from IoT sensors is preprocessed at edge devices and sent to a central server, where embeddings are generated, stored in a vector database, and analyzed by the LLM for fast query handling. This system provides real-time feedback through dashboards, focusing on overcoming challenges such as network delays, scalability, and security to deliver reliable environmental monitoring for non-expert users.