HIGH-LEVEL ARCHITECTURE OF A STREAMING TEMPORAL MULTIMODAL DATA VISUALIZATION SYSTEM FOR IOT DEVICE NETWORKS

Abstract

 This article focuses on the development of a high-level architecture for visualizing streaming temporal multimodal data generated by Internet of Things devices. The increasing number of IoT devices and the need to manage large data streams for real-time decision-making in sectors such as industry, smart cities, and environmental monitoring underline the relevance of this research. IoT systems generate vast amounts of complex data from various sensors, cameras, and other devices, which need to be processed and visualized for effective analysis and decision making. 
 The proposed architecture is composed of three main components: the IoT device interaction block, the data aggregation block, and the data visualization block. These components work together to enable data collection, processing, aggregation, and real-time visualization. The IoT device interaction block collects data from diverse IoT devices and ensures the data is properly formatted for further processing. The data aggregation block organizes the data streams based on temporal characteristics, preparing them for visualization. Finally, the data visualization block presents the aggregated data in user-friendly visual formats, facilitating quick and accurate decision-making. 
 A key feature of the architecture is its flexibility. It can be adapted to various environments, supporting both centralized and decentralized configurations. In centralized systems, all components can reside on a single server or machine, simplifying data transmission. In decentralized deployments, the system can process data across multiple servers, reducing latency and improving performance. The system's modular design ensures scalability, allowing it to grow with the increasing volume of data generated by IoT devices. 
 In conclusion, the proposed architecture provides a scalable and flexible solution for visualizing streaming IoT data. Its ability to handle large volumes of data and provide real-time insights makes it suitable for a wide range of applications, including smart cities, industrial automation, and environmental monitoring.