A multi-source heterogeneous data fusion method for intelligent systems in the Internet of Things
Rongrong Sun, Yijia Ren
Abstract
The advent of the Internet of Things (IoT) has revolutionized the field of intelligent system development by providing an extensive amount of data from IoT devices, essential for the management of these systems and the creation of innovative services. This data covers various aspects, including creation at the physical layer, transmission through the network layer, and processing within the application layer. This study presents a groundbreaking approach to amalgamating multi-source and varied data within intelligent systems leveraging IoT technology. Our approach seeks to optimize the integration of diverse datasets by examining the correlations between different data types using a novel mixed information gain strategy, leading to effective data fusion. It capitalizes on the computational and storage capacities of systems for seamless integration and augments the analysis of information, thereby improving the useability of data in intelligent systems. Simulation tests confirm the superiority of our method, demonstrating a remarkable improvement in performance in the fusion of dynamic, multi-source heterogeneous data compared to conventional techniques.