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A hybrid data-driven framework for real-time edge computing optimization in IoT environments
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DOI：10.1186/s13677-026-00938-x.png)
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Abstract
En 中文
The Internet of Things (IoT) is experiencing rapid growth, with an increasing number of connected devices generating large volumes of data, which poses challenges for real-time processing and management. Edge computing provides a viable solution by enabling local data processing and reducing reliance on centralized data centers, thereby decreasing latency. However, data redundancy remains a key challenge that can negatively impact system performance. This study proposes an edge computing optimization framework that integrates K-Means clustering with decision tree-based data analytics. The proposed approach aims to reduce redundant data and improve data handling efficiency at the network edge. Simulation results indicate improvements in system performance compared to existing methods, including an increase in overall efficiency by 12%, an improvement in data analysis accuracy by 9%, a reduction in energy consumption by 11%, and a decrease in response time by 7%.
Keywords:
Edge computing
Internet of things
Machine learning
Decision tree
Data clustering
Journal
J
IF:
4.3
Papers:
735
Citations:
2.2K
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