The real-time data processing framework for blockchain and edge computing

The rapid growth of IoT has increased the demand for large-scale data processing. However, traditional centralized methods struggle with real-time requirements and data security. This paper introduces VCD-TSNet, a novel real-time IoT data processing framework that combines blockchain and edge comput...

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Vydáno v:Alexandria engineering journal Ročník 120; s. 50 - 61
Hlavní autoři: Gao, Zhaolong, Yan, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.05.2025
Elsevier
Témata:
Blockchain
Deep learning
Edge computing
IoT
Real-time data processing
Deep learning
Real-time data processing
Blockchain
IoT
Edge computing
ISSN:1110-0168
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Shrnutí:The rapid growth of IoT has increased the demand for large-scale data processing. However, traditional centralized methods struggle with real-time requirements and data security. This paper introduces VCD-TSNet, a novel real-time IoT data processing framework that combines blockchain and edge computing. By integrating deep learning models like VGG, ConvLSTM, and DNN, VCD-TSNet effectively performs spatial feature extraction, temporal modeling, and decision-making, while using blockchain to ensure data integrity and privacy. Experimental results demonstrate that VCD-TSNet outperforms baseline models in classification accuracy, prediction precision, and real-time performance. For instance, on the BoT-IoT dataset, the classification accuracy reaches 97.5%, throughput increases to 920 TPS, and response time stays below 85 ms. This study validates the model’s effectiveness and highlights its potential in large-scale IoT environments, offering efficient, secure solutions for real-time data processing. It also provides insights for future improvements in frameworks that combine edge computing with blockchain.
ISSN:1110-0168
DOI:10.1016/j.aej.2025.01.092