Data Dynamic Prediction Algorithm in the Process of Entity Information Search for the Internet of Things

Abstract

To address the issue of insufficient real-time capability in existing Internet search engines within the Internet of Things environment, this research investigates the architecture of Internet of Things search systems. It proposes a data dynamic prediction algorithm tailored for the process of entity information search in the Internet of Things. The study is based on the design of a data compression algorithm for the Internet of Things entity information search process using the Rotating Gate Compression Algorithm. The algorithm employs the Least Squares Support Vector Machine to dynamically predict changes in entity node states in the Internet of Things, aiming to reduce sensor node resource consumption and achieve real-time search. Finally, the research introduces an Internet of Things entity information search system based on the data dynamic algorithm. Performance test results indicate that the segmented compression algorithm designed in the study can enhance compression accuracy and compression rate. As compression accuracy increases, errors also correspondingly increase. The prediction algorithm designed in the study shows a decrease in node energy consumption as reporting cycles increase, reaching 0.2 at 5 cycles. At the 5-cycle point, the prediction errors on two research datasets are 0.5 and 7.8, respectively. The optimized data dynamic prediction algorithm in the study effectively reduces node data transmission, lowers node energy consumption, and accurately predicts node state changes to meet user search demands.