Modeling and Visualization of Multi-Sensor Data Streams in Real-Time Systems

Abstract

The increasing availability and diversity of sensors across industrial and research domains have led to a growing need for effective real-time data visualization techniques. This paper presents a comprehensive framework for the modeling, integration, and visualization of multi-sensor data streams in real-time environments. Drawing on modern architectural paradigms such as Lambda Architecture and leveraging visualization design principles adapted to spatio-temporal sensor data, we propose a unified system capable of ingesting, processing, and interactively displaying heterogeneous sensor outputs. The proposed approach integrates a data fusion module, a dimensionality-aware visualization engine, and a flexible AR/3D front-end interface. The system is evaluated through a simulated deployment scenario with multiple sensors, demonstrating its ability to provide live feedback, detect anomalies, and support data-driven decision-making in dynamic environments. Our results show improvements in interpretability, fault detection latency, and system scalability. The findings contribute toward generalizable frameworks for situational awareness in smart environments, robotics, and industrial automation.