A Efficient Architecture for Real Time Data Processing and Visualization for IoT Devices

The rapid proliferation of the Internet of Things (IoT) has generated vast amounts of data from diverse sensors and devices, necessitating the development of efficient architectures for real-time data processing. This paper proposes a scalable and reliable architecture designed to handle the continuous flow of IoT data, ensuring timely ingestion, processing, storage, analysis, and visualization. The architecture incorporates an API gateway for data ingestion, a document-based database for raw data storage, and a relational database for structured data organization and business logic. The system has been implemented for storing and processing data from an AI-powered remote mosquito trap used in the capture and identification of Aedes aegypti. Performance tests were conducted under simulated conditions (single machine) using 35,000 data packets from multiple concurrent clients. The results demonstrated low-latency performance, with median delays of 0.39 ms for IoT device acknowledgment, 0.69 ms for processed data availability, and similarly low delays for analyzed data.