METHOD FOR VISUALIZING POINT-BASED TEMPORAL MULTIMODAL STREAMING DATA FROM IOT DEVICES WHILE MAINTAINING THE STABILITY OF PREVIOUSLY VISUALIZED DATA

Abstract

This paper presents a method for visualizing temporal multimodal streaming data from Internet of Things (IoT) devices, ensuring the stability of previously visualized data. The growing volume of real-time IoT data presents challenges in visualization and analysis, requiring efficient techniques for handling large datasets in dynamic environments. The goal of this research is to reduce system load without losing the context of the data being visualized.

The proposed method uses downsampling techniques to reduce the volume of displayed data while maintaining visualization stability by limiting the time window for data resampling. This method optimizes system resources, ensuring that users can interact with real-time data without cognitive overload, even when processing large volumes of data continuously. Moreover, the research shows that the proposed method can handle the challenges of real-time data streams effectively, providing a better user experience by preventing cognitive overload. Users are presented with only the most relevant data, ensuring that the visualization remains clear, stable, and easy to interpret.

The paper demonstrates the effectiveness of the method through an implementation using the SciChart library for .NET, showcasing its ability to reduce system resource consumption and improve real-time data representation. The results reveal that the proposed approach significantly reduces CPU and GPU load, making it suitable for real-time IoT data visualization, especially in resource-constrained environments.

In conclusion, this method offers a scalable and efficient solution for visualizing temporal multimodal IoT data, balancing the need to reduce system load while maintaining stable, clear, and meaningful visualizations. This approach ensures that real-time data remains accessible and interpretable, even when large amounts of data are continuously processed.