Multiservice-Oriented Framework for Enhanced Data Collection and Visualization in Smart City Applications

The exponential growth in data acquisition devices necessitates the development of sophisticated systems for processing, transforming, and visualizing the acquired information. The issue of data collection IoT devices is becoming increasingly urgent due to utilizing their rapid increase. When developing a Smart City system, it is imperative to consider the multitude of devices for data collection. The primary motivation behind this research is the growing need for server-portable and adaptable data acquisition systems that can meet the demands of modern Smart City environments. The purpose of the proposed solution is to provide a flexible, portable, and cost-effective framework for integrating heterogeneous IoT data sources, enabling efficient environmental monitoring and decision-making. It is crucial to anticipate future scaling or transferring the server side of the framework to a new hardware platform. This study presents an innovative approach for collecting data from heterogeneous sources to enhance efficiency and portability, applicable to various industrial sectors pursuing Industry 4.0 standards. The proposed framework was validated using a microservices-oriented architecture that leveraged containerization technology to create server-portable frameworks. This study demonstrates a versatile and server-portable solution for acquiring weather data, transforming, storing, visualizing, notifying and providing valuable insights for informed decision-making. The proposed framework was evaluated using multiple weather stations, including a custom-developed station and several commercial models, to assess its versatility and portability in data collection using different communication protocols. The evaluation also included the correction of measured values using statistical methods and machine learning techniques, which made it possible to increase data accuracy by compensating for systematic errors based on RMSE and MAE validation.