Methods for aggregating multi-source heterogeneous data in the IoT based on digital twin technology

Abstract

The Internet of Things (IoT) technology can currently enable devices and systems in various fields to achieve interconnectivity, intelligence, and automation, which is significant for improving daily life. It connects objects through the Internet, achieving information exchange and sharing, bringing many conveniences to humanity, and improving the efficiency and quality of various industries. However, precisely because everything is interconnected, most IoT systems have high data throughput, which leads to issues such as reduced operational efficiency of IoT systems. Therefore, this article used digital twin (DT) technology to aggregate multi-source heterogeneous data of the IoT, overcoming the problems of diversity and differences in massive data, and thus accelerating the system's data processing. Moreover, in the end of this article, an experiment was conducted on the IoT system of a certain university. Taking the system running 10 times as an example, the packet loss rate of the experimental group using DT technology was only 3.48%, while the packet loss rate of the control group running alone was 4.36%. This indicates that DT technology has improved the performance of the IoT system. This study highlights the role of digital twin technology in solving the low operational efficiency, diverse data, and data differences in data aggregation of the Internet of Things. It plays a significant role in improving the operational efficiency of the Internet of Things and improving the performance of the Internet of Things system.