Research on Feature Fusion Method of High Attribute Dimension Data in Internet of Things Based on Dividing Thoughts
Article No.: 7, Pages 1 - 5
Abstract
The magnitude of data in the sensor is increasing, so how to deal with data fusion technology has become the current research direction. Aiming at the problems of high attribute dimensions and the number of big data objects, it is necessary to study an efficient information fusion algorithm, which can better estimate and understand the surrounding environment and the development trend of things, so as to provide better decision support. In this paper, the feature fusion method of IoT(Internet of things) high attribute dimension data based on partition idea is studied. In order to improve the computational efficiency of feature fusion of high-dimensional IoT data, we present an efficient algorithm. This algorithm is based on the idea of partitioning, which cuts the high attribute dimension data into a number of relatively low attribute dimension data. First, these relatively low attribute dimension data are processed, and then the necessary feature attributes of the original high attribute dimension data are calculated by using these processing results, so as to ensure that the results obtained are the same as those obtained by directly calculating the high attribute dimension data. The research shows that the efficiency of the proposed algorithm is 9.362% higher than that of the traditional algorithm, which shows that the proposed algorithm is superior to the common data fusion algorithm.
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- Research on Feature Fusion Method of High Attribute Dimension Data in Internet of Things Based on Dividing Thoughts
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Published In
December 2022
199 pages
ISBN:9781450399548
DOI:10.1145/3588340
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Association for Computing Machinery
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Published: 03 November 2023
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ICBICC 2022
ICBICC 2022: 2022 International Conference on Big data, IoT, and Cloud Computing
December 2 - 4, 2022
Chengdu, China
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