Multi-attribute Feature Fusion Algorithm of IoT Communication Big Data Based on Ant Colony Neural Network

Abstract

Abstract To address the issues of time-consuming and inaccurate feature fusion in traditional Internet of Things (IoT) communication big data multi-attribute fusion algorithms, a new algorithm based on the ant colony neural network is proposed. Modeling and feature decomposition processing is carried out on IoT communication big data, communication data information and data attribute characteristics are optimized into different data groups according to a data clustering algorithm, and a rough set calculation method is used to calculate information attribute characteristics. The genetic algorithm is used to improve the ant colony neural network, improve the search ability, extract the multi-attribute characteristics of IoT communication big data, filter the current big data’s characteristic attribute demand through the comparison method, and achieve multi-attribute feature fusion of IoT communication big data through the similarity measurement method. The experimental results show that the fusion error of the IoT communication big data multi-attribute feature fusion algorithm based on the ant colony neural network is small and relatively stable. During the 600 MB data fusion process, the data fusion processing time of the method in this paper is 150 ms, and the average energy consumption ratio is below 2 %, which reflects better IoT communication big data multi-attribute feature fusion performance.