Title: A deep fusion method for asynchronous data in the internet of things based on data feature mining

Authors: Yongyi Huang

Addresses: Nanyang Medical College, Nanyang, Henan, China

Abstract: In order to reduce the packet loss rate and fusion time during the data fusion process, a deep fusion method for asynchronous data in the Internet of Things based on data feature mining is proposed. Firstly, the characteristics of the structure of the Internet of Things and asynchronous data were analysed. Secondly, the quality of asynchronous data through sparse filtering processing was improved. Then, association rules were used to perform feature mining on asynchronous data and calculate similarity features to complete deep fusion. The test results show that compared with existing methods, this method can significantly reduce packet loss rate, up to 0.1 and reduce data fusion time.

Keywords: data feature mining; internet of things; asynchronous data; deep fusion.

DOI: 10.1504/IJCAT.2024.143300

International Journal of Computer Applications in Technology, 2024 Vol.74 No.4, pp.275 - 281

Received: 28 Dec 2023
Accepted: 30 Apr 2024
Published online: 12 Dec 2024 *

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