A software architecture for database management in a real-time IoT monitoring service

Environmental quality monitoring in indoor spaces directly impacts occupant health and influences energy consumption efficiency. Smart IoT sensors enable this process by measuring various parameters in indoor environments, including temperature, humidity, air quality and illuminance. In this study, we utilized both commercially available sensors and an internally developed device, the Promet&o multisensor, to conduct our measurements. To support this system, the development of a software platform was required. This platform is designed to efficiently store the collected data, calculate indoor environmental comfort indexes, and provide capabilities for data visualization and analysis. Due to the high volume of incoming data from IoT sensors, the wide variety of sensors, and the location of the networks, exploring techniques to preprocess, and to perform comfort indexes calculation should be an important point of attention. We designed and implemented a scheduler to preprocess measurements in real time. This scheduler groups incoming data into pre-aggregated view tables with intervals of five minutes and one hour and calculates various environmental indices (thermal, acoustic, visual, and indoor air quality) and derives an overall indoor environmental quality index. A web API was designed to enable advanced data retrieval and analytics. It provides access to real-time sensor data and generates statistics, including averages, standard deviations, minimum and maximum values, and 10th and 90th percentiles. These statistics are calculated for specific time aggregations and over different periods of time and are based on the pre-aggregated data tables set at five-minute and one-hour intervals. The API is structured for easy integration with Grafana, enabling efficient visualization. We also used Docker to easily adapt and manage different indoor sensor networks for various experimental projects. Additionally, the system incorporates MQTT clients to handle the unique message patterns and data streams of different IoT networks. This platform significantly elevates the efficiency and scope of environmental quality monitoring in indoor spaces, enabling comprehensive and insightful data analysis.