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The analysis of the internet of things database query and optimization using deep learning network model.
1Library, Shandong University of Arts, Jinan, China.
Plos One
|June 28, 2024
Summary
This study optimized deep learning (DL) models for Internet of Things (IoT) database queries, significantly reducing training and optimization times. The enhanced DL model demonstrates superior efficiency, energy consumption, and processing capacity for large-scale IoT data.
Area of Science:
- Computer Science
- Artificial Intelligence
- Data Science
Background:
- Internet of Things (IoT) systems generate vast amounts of data, necessitating efficient database query and optimization techniques.
- Traditional database query methods struggle to keep pace with the scale and complexity of IoT data.
- Deep learning (DL) models offer potential for advanced data processing but require optimization for IoT environments.
Purpose of the Study:
- To investigate the application and effectiveness of a deep learning network model for optimizing Internet of Things (IoT) database queries.
- To analyze the architecture of IoT database queries and explore suitable DL network models.
- To optimize a selected DL model using specific strategies and validate its performance.
Main Methods:
- Analysis of IoT database query architecture.
- Exploration and selection of a suitable deep learning network model.
- Implementation of optimization strategies for the chosen DL model.
- Experimental validation comparing the optimized model against traditional models.
Main Results:
- The optimized DL model significantly reduced model training and parameter optimization times, especially with large datasets (e.g., 2000 data points).
- The optimized model showed improved energy efficiency in terms of Central Processing Unit (CPU), Graphics Processing Unit (GPU), and memory usage.
- Enhanced throughput and reduced latency were observed, with the optimized model handling high transaction volumes and large data requests more effectively.
- Peak processing capacity at 4000 data volumes exceeded that of other models, indicating superior performance in handling large data loads.
Conclusions:
- The optimized deep learning model demonstrates superior performance in processing and optimizing Internet of Things (IoT) database queries.
- The findings provide a valuable reference for IoT data processing and deep learning model optimization, particularly for large-scale data scenarios.
- This research promotes the application of deep learning in the IoT field, offering insights for future research and practical implementation.
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