Development and Implementation of Urbana IoT Platform: Real-Time Analytics, Data Management, Processing, and Visualization for Scalable IoT Applications

The increasing adoption of the Internet of Things (IoT) has led to a surge in data generated by interconnected devices. This thesis focuses on the Urbana IoT Platform, a modular system designed to receive, process, store, and analyze IoT data, enabling actionable insights and efficient decision-making. The platform communicates with IoT via MQTT, HTTP, and LoRaWAN, with a decoding layer that decodes payloads, enriches data, and organizes it by device categories like automation and environment. Data is stored using PostgreSQL, Apache Druid for analytics, and MongoDB for device status management. Key features include data transformation, allowing filtering, aggregation, and trend analysis, and customizable dashboards for interactive data visualization. Real-time monitoring ensures users have access to the latest information for proactive decision-making. The system integrates scalable technologies such as analytics service for Apache Druid queries and a ReactJS frontend for a user-friendly interface, making it adaptable to various organizational needs. A practical case study demonstrates its end-to-end capabilities, from device onboarding to real-time analytics and dashboard creation. This thesis presents a scalable solution to IoT data challenges, contributing to efficient data management and insights-driven decision-making. It serves as a resource for researchers, developers, and organizations working in IoT analytics.